graphics processing unit

72 results

pages: 315 words: 89,861

The Simulation Hypothesis
by Rizwan Virk
Published 31 Mar 2019

The ExtraTerrestrial, 38 Everett, Hugh, 149 EverQuest, 44 expanded world, hints of, 37 expanding symmetry, 263 Exposition du système du monde (Laplace), 125 F Facebook, 59–60, 97–98 false memories, 79–80 . see also implanted memories Far Journeys (Monroe), 242 Faraday, Michael, 125–26 Fermi, Enrico, 235–37 Fermi paradox, 235–37 Feynman, Richard, 258 field of view, 137 films, special effects, 64–66 Final Fantasy: The Spirits Within, 65 Final Fantasy video games, 42, 65 Flamm, Ludwig, 178 Flight Simulator, 137 forceful projection, 197–99 Fractal Foundation, 264–65 fractal patterns, 264f fractal processes, 18–19 fractally generated landscape, 48f fractals, 263–66 Freud, Sigmund, 189 Fringe and parallel worlds, 152–53 FTL (faster than light) technology, 233 full immersion, 53 Fundamental Process, 156–57 future selves and parallel lives, 150–52 future vs. the past, measurement, 146–47 futures, multiple possible, 147–48, 148f G game control methods, 53–54 . see also interface technologies game loop, 27, 31, 73, 213 game state, 29 game state, player, graphical representation, 40–41 game theory, 153–55 game world, limited level-based, 36 Gates, James, 256–57 Geller, Uri, 243 General Theory of Relativity, 171 Geons, Black Holes and Quantum Foam (Wheeler), 168 Giddings, Steven B., 174 Go, 104 God/Allah/Jehovah, and creation of physical world, 219–221 God/Allah/Jehovah, and the afterlife Al-Akhirah and the Day of Qiyamah, 220–21 Christianity and Judaism, 223–25 Gods and Heaven, 277–78 Goertzel, Ben, 91 Good, Irving John, 100 Google Assistant, 88 Google Duplex, 90 Google Glass, 62 Google Home, 90 Goswami, Amit, 130, 133 GPUs (graphics processing units), 16, 137 GPUs/CPUs, 157, 173 grandfather paradox, 149 graphical arcade and console games, early, 32–38 graphical non-player characters (NPCs), 41–42 graphical representation of player game state, 40–41 graphically rendered world, big, 40 graphics processing units (GPUs), 16, 137 gravitational constant, 168 gravity waves, 168 Great Game, 150–52 Great Simulation, 19–20, 26, 53–54, 173–74, 214, 268 Great Simulation, conscious beings or unconscious simulations dreamlike nature of reality, 284–85 Godlike AI, angels and afterlife, 286 souls, reincarnation, karma and quests, 285–86 Great Simulation, implications of bridging the great divide, 289–290 computation underlies other sciences, 286–89 Plato’s allegory of the cave, 270–71 Great Simulation, main elements of downloadable consciousness, 281 high-resolution pixelated world, 278–79 MMORPGs (massively multiplayer online roleplaying games), 279 physics engine based on classical and quantum physics, 283–84 player characters (PCs), non-player characters and AI, 280–81 quantized, pixelated reality, 281–82 rendering engine based on quantum indeterminacy, 282–83 seemingly infinite algorithmically generated world, 280 Great Simulation, sources of aliens, 275–76 Gods and Heaven, 277–78 humans/ancestors, 273–74 nonhuman earth-based lifeforms, 275 other simulations, 270–73 super-intelligent machines, 276–77 time travelers from the future, 274–75 Green Simulation, 266 Greisen–Zatsepin–Kuzmin (GZK) limit, 255–56 Grimm, 4 GZK (Greisen-Zatsepin-Kuzmin) limit, 255–56 H Habitat, 44, 209 HAL 9000, 2001: A Space Odyssey, 96, 115 Hall effect, 251 Hanson Robotics, 91 haptic gloves, 61 haptic suits, 56 haram, 225–26 Harlow, Daniel, 260 Hawking, Stephen, 10, 79, 150, 157, 275 Healing Mantras (Ashely-Farrand), 206 Heisenberg, Werner, 125, 130–32, 167, 245, 290 Hello Games, 46–47 Herbert, Brian, 97 Herbert, Frank, 97 Hertz, Heinrich, 167 heuristic systems, 89 high-resolution pixelated world, Great Simulation, 278–79 Hinduism, 14 hints of expanded world, 37 The Hitchhikers Guide to the Galaxy (Adams), 29, 275 holodeck, 6–7, 68 Howe, Elias, 190 HTC Vive HR headset, 55, 60 humans/ancestors, 273–74 Hynek, J.

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Video games wouldn’t be possible without computer graphics, and it is the development of this relatively new field of science that has brought the simulation hypothesis out of science fiction and into serious consideration. Within computer science, video games and entertainment have played a unique role in driving the development of both hardware and software. Examples include the development of GPUs (graphics processing units) for optimized rendering, CGI (computer-generated effects), and CAD (computer-aided design), as well as artificial intelligence and bioinformatics. The most recent incarnation of fully immersive entertainment technology is virtual reality (VR). Despite wondering about the simulation hypothesis for many years, it wasn’t until VR and AI reached their current level of sophistication that I could see a clear path to how we might develop all-encompassing simulations like the one depicted in The Matrix, which led me to write this book.

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The film's pixels are rendered and stored in a digital format, and then simply re-displayed on the movie theater’s screen. Unlike 3D films, video games are much more dynamic, relying on real-time rendering of 3D models of landscapes, objects, and characters. The pixels have to be created in real time using a rendering engine while the game is being played on a CPU or a GPU (graphics processing unit). As we saw in the last chapter in the transition from 2D to 3D games, video game scenes today aren’t stored as pixels; instead they are stored as a combination of 3D models and textures. The rendering engine is responsible for converting these models into the pixels you see on your screen, based upon where your character is in the world, in what can be thought of as a “virtual camera.”

pages: 336 words: 93,672

The Future of the Brain: Essays by the World's Leading Neuroscientists
by Gary Marcus and Jeremy Freeman
Published 1 Nov 2014

See also neural dust complexity brake, 36 computation: brain as organ of, 65; computational neuroscience, 23, 143, 182; percepts and concepts, 171–72; principles, 40–42, 44, 76, 85f, 88, 119, 144–46, 159; role of models in neuroscience, 98; speech perception and cortical oscillations, 144–46; speech production, 146, 187, 190 computational brain, 205–14; challenges, 212–14; central processing units (CPUs) and Graphics Processing Units (GPUs), 208–10; neural networks, 205–7; Parallel Distributed Processing (PDP) networks, 41, 205, 206; random organization assumption, 207–8 computer algorithms, 101, 105, 178, 229 computer science, 86, 142, 143, 224 computer systems: analogy to brain, 177–79, 209–10; comparison to brain, 84–88, 85f; Graphics Processing Units (GPUs) and CPUs, 208–10; Marr’s approach to circuits and behavior, 178f; mind-as-computer metaphors, 205–6. See also computational brain concept: percepts and, 171–72 conceptual clarity: consciousness, 170–71, 175 connections: mapping synaptic, of brain, 94; neuronal, 92 connectivity, 210: algorithms, 119; core theme of, 90–91; human right, 90; in situ sequencing to determine, 59–60; mapping out cell types and, 28–31 connectivity map, 29–31, 182 connectome, 11, 40, 45, 182; circuits of brain, 182–84; defining progress, 200–201; as DNA sequencing problem, 40–49; worms, 265 Connectome (Seung), 12 ConnectomeDB, 13 connectomics, 11, 86; electron microscopy (EM), 45–46 consciousness, 159, 255; behavioral technology, 175; binocular rivalry, 174; brain imaging, 174–75; Burge’s model of perception, 171f; cognitive vs. noncognitive theories of, 165–68; conceptual clarity, 170–71, 175; eventrelated potentials (ERPs), 174–75; global broadcasting, 165, 168, 174; global neuronal workspace, 165f, 166; hard problem of, 162, 269; measurement problem, 161–64, 170–72; nonconceptual representations, 170–72; percepts and concepts, 171–72; precursors to conscious state, 163; transgenic mice and optogenetic switch, 168–70 contrastive method: conscious and unconscious perception, 163; consciousness, 163 copy number variants (CNVs), 236 cortex: grid cell generation, 74; grid cells and grid maps, 71–73; mammalian space circuit, 69–71; of men and mice, 26–27; modular structure of, 43; spatial cell types in entorhinal network, 74–76; teachings from grid cells, 76; understanding the, 67–69 cortical microcircuit: morphoelectric types, 119 cortical oscillations: speech perception and, 144–46 counting, 53, 54, 55, 56 Cre driver lines, 29 Crick, Francis, 172, 269 Crohn’s disease, 234 Dang, Chinh, 3, 25 DARPA (Defense Advanced Research Projects Agency), 195, 199 Darwin, Charles, 191 deCODE, 198 deep brain stimulation (DBS), 195, 227 deep learning, 211 default network, 208 Defense Advanced Research Projects Agency (DARPA), 125.

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But that in itself doesn’t militate against the idea that the brain might be some kind of computer; computers are often portrayed as if they were invariable serial sequential devices, but the reality is that for the last twenty-five years, since personal computers became popular, there has always been some degree of parallelism: an input-output controller working alongside the central processing unit, for instance. By the 1990s Graphics Processing Units (GPUs) started to become popular, acting as coprocessors with a central processor, taking up most of the work of displaying images so that the CPU would be free for a program’s main logic. And importantly, GPUs were themselves computers, but ones with a dedicated job—essentially matrix arithmetic—and one that they did almost entirely in parallel.

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See also brain models; simulation; whole-brain neuroimaging; whole-brain simulation Brain Architecture Management System (BAMS), 12 brain atlases: history of, 3–6; next generation, 115–17 brainbot, 266, 267 Brainbow, 14 brain computer interfaces (BCIs), 228–30 BrainGate, 229 BRAIN initiative (Brain Research Through Advancing Innovative Neurotechnologies), 177, 182, 194–95 brain-machine interface (BMI) technology, 243; control and dexterity of prosthetic devices, 243–244; subsystems of intracortical BMI system, 244 brain models: bottom-up modeling, 85f, 112–13, 267; building virtual brains, 97–99; comprehensive brain mapping and modeling, 51–52; integrating data and theory, 35–38; top-down modeling, 85f, 112, 162, 171f; unifying, 120–21; whole brain simulation, 111 Brain Simulation Platform, 118–19 Brenner, Sydney, 12 Broca, Paul, 139, 257 Broca’s area, 139, 140, 147 Brodmann, Korbinian, 4, 6f Brun, Vegard, 70 Bullock, Ted, 187 Burge, Tyler, 170, 171f Caenorhabditis elegans, 12, 43, 45, 182, 261, 265 calcium imaging experiment, 100–101, 103, 106; larval zebrafish, 106, 107 cancer, 234, 256, 266 canonical neural computations: discovery and characterization, 181–82; divisive normalization, 180; filtering, 180; sensory systems, 179–80 Caplan, Arthur, 159, 194–204 Carandini, Matteo, 159, 177–84 Carmena, Jose M., 243 Celera Genomics, 196 cell types: diversity of, 112; in situ sequencing to determine, 59; mapping out, and connectivity, 28–31 Center for Neural Circuits and Behavior, 177 central processing unit (CPU): comparison to brain, 84–88, 85f; Graphics Processing Units (GPUs) and CPUs, 208–10 Centre for Neural Circuits and Behaviour, 177 Centre Hospitalier Universitaire Vaudois (CHUV), 122 cetaceans: neocortex, 186 Chalmers, David, 162 Champagne, Frances, 189 Changeux, Jean-Pierre, 165 channelrhodopsin, 24, 32 chemical dyes, 256 chicken and egg problem, 51 children: language and learning, 150 Chomsky, Noam, 212 Church, George, 45, 50–63 Churchill, Winston, 263 Church–Turing thesis, 257 circuits, 11, 245; architectures, 43; neurons, 92 circularity, 170 CLARITY, 15 Clinton, Bill, 201 coactivation studies: brain activity, 95–96 cochlear implants, 226–27; electromagnetic (EM) waves, 248 cognition, 162; consciousness vs., 161–62 cognitive access, 162 cognitive decline, 227 cognitive neuroscience: comparison to computer science, 86–87; specialized circuits in brain, 162–63 Colbert Report, 92 Cold War, 255 colon cancer, 234 complementary metal oxide semiconductor technology (CMOS), 246, 250–51.

pages: 412 words: 116,685

The Metaverse: And How It Will Revolutionize Everything
by Matthew Ball
Published 18 Jul 2022

Real-Time Rendered Rendering is the process of generating a 2D or 3D object or environment using a computer program. The goal of this program is to “solve” an equation made up of many different inputs, data, and rules that determine what should be rendered (that is, visualized) and when, and by using various computing resources, such as a graphics processing unit (or GPU) and central processing unit (CPU). As is the case with any math problem, an increase in the resources available to solve it (in this case, time, the number of CPUs/GPUs, and processing power) means that more complex equations can be tackled, and more detail provided in the solution.

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Chapter 6 COMPUTING SENDING ENOUGH DATA AND IN A TIMELY FASHION is just one part of the process of operating a synchronized virtual world. The data must also be understood, code must be run, inputs assessed, logic performed, environments rendered, and so on. This is the job of central processing units (CPUs) and graphics processing units (GPUs), broadly described as “compute.” Compute is the resource that performs all digital “work.” For decades, we’ve seen increases in the number of computing resources available and manufactured per year, and we’ve witnessed how powerful they can be. Despite this, computing resources have always been and will likely remain scarce—because when more computing capability is available, we tend to try and perform more complicated calculations.

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See also TikTok Call of Duty, 29–30 Call of Duty Mobile, 32, 190, 201, 303 Call of Duty Warzone, 32, 91, 146–47, 179 Candy Crush, 82, 116, 263 Capcom’s Street Fighter, 139 Carmack, John, 21, 57, 94, 239 Carnegie Institution of Washington, ix casual gaming, 80, 255 CCP Games, 45–46, 55–56, 90 central processing units (CPUs), 36–37, 89, 98–101, 105 in consoles, 174 required by the Metaverse, 174, 223 in smartphones, 131, 148–49, 159, 243, 245 Chaturbate, 261 China, xiii–xiv gaming in, xiii–xiv, 115 megacorporations of, xii–xiii, 19–20 Minecraft tribute during COVID-19, 10 regionalization of the internet, 62, 302–4 satellite capabilities of, 156n Tencent WeChat’s payment rails, 205–6 see also Alibaba; Tencent; TikTok China Mobile, 212 CHIPS (Clearing House Interbank Payment System), 168–70 Citigroup, 166 “CityDAO,” 228 Civilization V, 181 Cline, Ernest, 21, 144 cloud game streaming, 77–78, 82, 98–99, 112, 195–96, 197, 198–99, 203, 204, 281n, 300. See also Amazon Luna, Google Stadia CNBC, 20–21 Comcast, 165 Commodore 64, 8 computing, 89–102 decentralized, 100–102, 223–24 ultra-wideband (UWB) chips, 160 see also central processing units (CPUs); graphics processing units (GPUs); hardware Conant, James B., x concurrent users (CCUs), 54–57, 90–92, 122, 146, 234, 245, 261, 268, 283 “Condor Cluster,” 65 Confinity, 61 ConstitutionDAO, 227–28 Cook, Tim, 186, 201, 284 copyright, 117n, 165–66, 235 Corning, 148 Counter-Strike, 178, 247 COVID-19, 10–11, 28, 34, 48–49, 112, 173, 247, 252 Crackle, 282 Cramer, Jim, 20–21 credit card systems, 168, 170–72, 177–78, 186–89, 206, 209 credit scores, 38, 127, 129 cross-platform game engines, 109, 131–32, 175–77 cross-platform gaming (cross-play), 132–35, 137–38, 176, 177, 179, 245–46 Crunchyroll, 280 cryptocurrency, 62, 206–12 computing infrastructure and, 223–25 institutional investment in, 307 KYC (Know Your Customer) regulations, 301 mining, 200, 210 “minting” in, 217 platforms that ban/regulate, 200–201 speculative craze over, 234 traditional credit cards involved in, 231–32 see also Bitcoin/bitcoin; blockchains; Ethereum; Web3 crypto-exchange concept, 102 crypto-gaming, 222–25 Cryptonomicon, 101n Cryptopunks, 218, 229, 265, 293–94 Cryptovoxels, 115 CryTek/CryEngine, 278 CTRL-labs, 153, 155, 162, 261, 274 “cyberspace,” 5–8, 129, 130 Daily Mail, 308–9 Dapper Labs, 218, 301 “dapps.”

pages: 161 words: 39,526

Applied Artificial Intelligence: A Handbook for Business Leaders
by Mariya Yao , Adelyn Zhou and Marlene Jia
Published 1 Jun 2018

Rather than building custom deep learning solutions, many enterprises opt for Machine Learning as a Service (MLaaS) solutions from Google, Amazon, IBM, Microsoft, or leading AI startups. Deep learning also suffers from technical drawbacks. Successful models typically require a large volume of reliably-labeled data, which enterprises often lack. They also require significant and specialized computing power in the form of graphical processing units (GPUs) or GPU alternatives such as Google’s tensor processing units (TPUs). After deployment, they also require constant training and updating to maintain performance. Critics of deep learning point out that human toddlers only need to see a few examples of an object to form a mental concept, whereas deep learning algorithms need to see thousands of examples to achieve reasonable accuracy.

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Even with MOOCs, students in developing countries face an uphill battle compared to their counterparts in developed countries. Some struggle with the lack of structured datasets available in their language or culture, others with the lack of reliable internet infrastructure and access. Still others face a lack of career opportunities. Finally, the lack of affordable access to computational resources, such as graphic processing units (GPU) and reliable power sources, presents a major obstacle for students who want to build their own models. Even with the right hardware, complex neural network models can take days, if not weeks, to train. Even if computational resources were widely available, engineering education alone is insufficient to ensure that AI technologies are built safely and successfully.

pages: 404 words: 43,442

The Art of R Programming
by Norman Matloff

See also math functions; string-manipulation functions anonymous, 187–188 applying to data frames, 112–120 aids for learning Chinese dialects example, 115–120 applying logistic regression models example, 113–115 using lapply() and sapply() functions, 112–113 applying to lists, 95–99 abalone data example, 99 lapply() and sapply() functions, 95 text concordance example, 95–98 applying to matrix rows and columns, 70–73 apply() function, 70–72 finding outliers example, 72–73 default arguments, 9–10 listing in packages, 358 as objects, 149–151 replacement, 182–186 for statistical distributions, 193–194 transcendental, 40 variable scope, 9 vector, 35–39, 311 G GCC, 325 GDB (GNU debugger), 288, 327 general-purpose editors, 186 generating covariance matrices, 69–70 filtering indices, 45–47 powers matrices, 312–313 generic functions, xxi classes, 15 implementing on S4 classes, 225–226 getAnywhere() function, 211 get() function, 159 looping over nonvector sets, 142 getnextevnt() function, 165 getwd() function, 245 global variables, 9, 171–174 GNU debugger (GDB), 288, 327 GNU S language, xix GPU programming, 171, 345 GPUs (graphics processing units), 345 gputools package, 345–346 granularity, 348 graphical user interfaces (GUIs), xx graphics processing units (GPUs), 345 graphs, 261–283 customizing, 272–280 adding legends with legend() function, 270 adding lines with abline() function, 263–264 adding points with points() function, 269–270 adding polygons with polygon() function, 275–276 adding text with text() function, 270–271 changing character sizes with cex option, 272–273 changing ranges of axes with xlim and ylim options, 273–275 graphing explicit functions, 276–277 magnifying portions of curve example, 277–280 smoothing points with lowess() and loess() functions, 276 pinpointing locations with locator() function, 271–272 plot() function, 262 INDEX 363 graphs (continued) plots restoring, 272 three-dimensional, 282–283 polynomial regression example, 266–269 saving to files, 280–281 starting new graph while keeping old, 264 two density estimates on same graph example, 264–266 grayscale images, 63 gregexpr() function, 254 grep() function, 109, 252 GUIs (graphical user interfaces), xx H hard drive, loading packages from, 356 help feature, 20–24 additional topics, 23–24 batch mode, 24 example() function, 21–22 help() function, 20–21 help.search() function, 22–23 online, 24 help() function, 20–21 help.search() function, 22–23 higher-dimensional arrays, 82–83 hist() function, 3, 13–14 hosts, 345 Huang, Min-Yu, 324 I identical() function, 55 IDEs (integrated development environments), xx, 186 ifelse() function, 48–49 assessing statistical relation of two variables example, 49–51 control statements, 143–144 recoding abalone data set example, 51–54 if statements, nested, 141–142 image manipulation, 63–66 images component, mapsound() function, 116 immutable objects, 314 indexing list, 87–88 364 INDEX matrices, 62–63 vector, 31–32 indices, filtering, 45–47 inheritance defined, 207 S3 classes, 214 initglbls() function, 165 input/output (I/O).

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• Debugging plays a key role when programming in any language, yet it is not emphasized in most R books. In this book, I devote an entire chapter to debugging techniques, using the “extended example” approach to present fully worked-out demonstrations of how actual programs are debugged. • Today, multicore computers are common even in the home, and graphics processing unit (GPU) programming is waging a quiet revolution in scientiﬁc computing. An increasing number of R applications involve very large amounts of computation, and parallel processing has become a major issue for R programmers. Thus, there is a chapter on this topic, which again presents not just the mechanics but also extended examples

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If one thread is initializing a sum, you wouldn’t want other threads that make use of this variable to continue execution until the sum has been properly set. You can learn more about OpenMP in my open source textbook on parallel processing at http://heather.cs.ucdavis.edu/parprocbook. 16.3.6 GPU Programming Another type of shared-memory parallel hardware consists of graphics processing units (GPUs). If you have a sophisticated graphics card in your machine, say for playing games, you may not realize that it is also a very powerful computational device—so powerful that the slogan “A supercomputer on your desk!” is often used to refer to PCs equipped with high-end GPUs. As with OpenMP, the idea here is that instead of writing parallel R, you write R code interfaced to parallel C.

pages: 424 words: 114,905

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
by Eric Topol
Published 1 Jan 2019

generative adversarial networks (GANs), 70 (table) image generation and, 101 genetic risk scores, 144 genome sequencing, 4 natural-language processing and, 5 Wellderly program and, 183–184 genomic analysis tool kit (GATK), 211 genomic data Cancer Genome Atlas and, 209 privacy and, 101 genomics autism and, 211 brain cancer and, 214 cancer research and, 213–214 chromatin in, 211 CRISPR and, 213 Deep Genomics and, 203, 218 DeepBind and, 212 DeepCpG and, 212 Deep-SEA and, 210–211 DeepSequence and, 212 DeepVariant and, 211–212 DeFine and, 212 drug discovery and, 218 Elevation and, 212–213 Human Genome Project and, 266–267 nutrigenomics in, 242 ghost cytometry, 229 Gladwell, Malcolm, 297 global AI healthcare initiatives, 207 glycemic response, 242, 243 (fig.), 244–246, 247 (fig.), 248, 249 (fig.), 250 Goldman, Brian, 275–276 Goodfellow, Ian, 67, 101 Google, 127, 148 (fig.), 149, 228–229 Aravind Eye Hospital and, 204 diabetic retinopathy and, 145–146 image recognition and, 75 jobs and, 108 skin cancer diagnosis and, 133–134 Verb Surgical by, 161 See also DeepMind Google Assistant, 256 Google Duplex, 259 Google Photos AI, 98 GPUs. See graphic processing units graphic processing units (GPUs), 77 Gratch, Jonathan, 165–167 Greenberg, Ben, 256 Greulich, Peter, 55–56 grid cells, 222–223 Groopman, Jerome, 294 Groupon, 158 Guangzhou Hospital, 205 Gundotra, Vic, 61–62 gut microbiome, 244–246, 250, 251 (fig.), 253–254, 279–280 hacking driverless cars and, 103–104 virtual assistants, 261 hand hygiene, 195–196, 197 (fig.)

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“big”) datasets for training, such as ImageNet’s 15 million labeled images; YouTube’s vast video library, which grows by three hundred hours of video every minute; Tesla’s collection of driving data, which adds 1 million miles of driving data every hour; the airlines’ collection of flight data, which grows by 500 Gb with each plane flight; or Facebook’s library of billions of images or 4.5 billion language translations a day.21 Second are the dedicated graphic processing units (GPUs) to run computationally intensive functions with massive parallel architecture, which originated in the video gaming industry. A 2018 publication of optical, diffractive deep neural network (D2NN) prompted Pedro Domingos to say, “Move over GPUs. We can now do deep learning at the speed of light.”22 Third are cloud computing and its ability to store massive data economically.

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A mixed hardware-software approach for artificial synapses was undertaken by IBM Research, creating a neural network with over 200,000 two-tiered (short- and long-term) synapses for image recognition, requiring one hundred times less power and, at the same time, creating efficiency of more than 28 billion operations per second per watt (which, compared with current graphic processing units, is more than two orders of magnitude greater). This accomplishment bodes well for future jumps in efficiency and reduced power requirements for artificial neural networks.65 So, as efforts to recapitulate the functioning of the human brain in computers—indeed, to make them more powerful—pushes forward, it brings us back to the question that opened this chapter: Will science ever be done by computers alone?

When Computers Can Think: The Artificial Intelligence Singularity
by Anthony Berglas , William Black , Samantha Thalind , Max Scratchmann and Michelle Estes
Published 28 Feb 2015

First developed in the early 1990s, substantial progress has been made towards building practical circuits using them. More fundamentally, existing hardware technologies are probably already sufficiently powerful if the correct software could be written. Moreover, much more computation can be obtained with existing transistor technology by using more parallel architectures such as those now seen in graphics processing units and associative memories. Certainly hardware is not a limiting factor in being able to produce intelligent agents at this time. So this argument seems both highly speculative and irrelevant. Bootstrap fallacy In 1950, at the dawn of computing, Alan Turing considered the question of whether computers could think.

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If there are thousands of training cases that are processed in thousands of epochs, and each individual training case requires thousands of computations, it is easy to require many billions of operations to train a network. The training may then need to be repeated many times for various parameters such as the number of hidden nodes and the conditioning algorithms. Fortunately, much of this processing can be performed in parallel, and so modern graphical processing units can be used with good effect. The primary result of training a network is the creation of two matrices of weight, one for each layer. These weights can then be applied to new cases very efficiently. This involves little more than the multiplication of two possibly sparse matrices. The numbers in the weight matrices encapsulate much of the raw information that is in the training data.

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But this analysis cannot be cleanly separated from the phonetic processing due to the need to handle phonetic ambiguity. 3D graphics Before considering the problem of computer vision, it is worth considering the inverse but relatively simpler problem of producing the 3D graphics that have become commonplace in movies and games. These advanced graphics have become possible due to the availability of specialized hardware and graphics processing units that can perform the billions of calculations per second that are required to produce quality animations. The objects that are displayed are generally represented as a hierarchical model known as a scene graph. So a truck could contain ten zombies, each of which has arms, legs and heads, which in turn have fingers, an eye and maggots.

pages: 339 words: 92,785

I, Warbot: The Dawn of Artificially Intelligent Conflict
by Kenneth Payne
Published 16 Jun 2021

That relationship is captured in Moore’s Law which predicts a doubling in processing power every two years, and a halving in costs. Cheap computer power was essential for deep learning, because the new connectionism made astonishing demands—both for physical processors, and also for the electricity to run them during training. Happily, in the graphics processing unit (GPU) of modern games consoles, AI researchers found the ideal artificial brain cells for their neural networks: cheap, very powerful and widely available. The second big development was a sudden abundance of data on which to train the nets. More information was being stored digitally, which could be shared at low cost.

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A-10 Warthog abacuses Abbottabad, Pakistan Able Archer (1983) acoustic decoys acoustic torpedoes Adams, Douglas Aegis combat system Aerostatic Corps affective empathy Affecto Afghanistan agency aircraft see also dogfighting; drones aircraft carriers algorithms algorithm creation Alpha biases choreography deep fakes DeepMind, see DeepMind emotion recognition F-117 Nighthawk facial recognition genetic selection imagery analysis meta-learning natural language processing object recognition predictive policing alien hand syndrome Aliens (1986 film) Alpha AlphaGo Altered Carbon (television series) Amazon Amnesty International amygdala Andropov, Yuri Anduril Ghost anti-personnel mines ants Apple Aristotle armour arms races Army Research Lab Army Signal Corps Arnalds, Ólafur ARPA Art of War, The (Sun Tzu) art Artificial Intelligence agency and architecture autonomy and as ‘brittle’ connectionism definition of decision-making technology expert systems and feedback loops fuzzy logic innateness intelligence analysis meta-learning as ‘narrow’ needle-in-a-haystack problems neural networks reinforcement learning ‘strong AI’ symbolic logic and unsupervised learning ‘winters’ artificial neural networks Ashby, William Ross Asimov, Isaac Asperger syndrome Astute class boats Atari Breakout (1976) Montezuma’s Revenge (1984) Space Invaders (1978) Athens ATLAS robots augmented intelligence Austin Powers (1997 film) Australia authoritarianism autonomous vehicles see also drones autonomy B-21 Raider B-52 Stratofortress B2 Spirit Baby X BAE Systems Baghdad, Iraq Baidu balloons ban, campaigns for Banks, Iain Battle of Britain (1940) Battle of Fleurus (1794) Battle of Midway (1942) Battle of Sedan (1940) batwing design BBN Beautiful Mind, A (2001 film) beetles Bell Laboratories Bengio, Yoshua Berlin Crisis (1961) biases big data Bin Laden, Osama binary code biological weapons biotechnology bipolarity bits Black Lives Matter Black Mirror (television series) Blade Runner (1982 film) Blade Runner 2049 (2017 film) Bletchley Park, Buckinghamshire blindness Blunt, Emily board games, see under games boats Boden, Margaret bodies Boeing MQ-25 Stingray Orca submarines Boolean logic Boston Dynamics Bostrom, Nick Boyd, John brain amygdala bodies and chunking dopamine emotion and genetic engineering and language and mind merge and morality and plasticity prediction and subroutines umwelts and Breakout (1976 game) breathing control brittleness brute force Buck Rogers (television series) Campaign against Killer Robots Carlsen, Magnus Carnegie Mellon University Casino Royale (2006 film) Castro, Fidel cat detector centaur combination Central Intelligence Agency (CIA) centre of gravity chaff Challenger Space Shuttle disaster (1986) Chauvet cave, France chemical weapons Chernobyl nuclear disaster (1986) chess centaur teams combinatorial explosion and creativity in Deep Blue game theory and MuZero as toy universe chicken (game) chimeras chimpanzees China aircraft carriers Baidu COVID-19 pandemic (2019–21) D-21 in genetic engineering in GJ-11 Sharp Sword nuclear weapons surveillance in Thucydides trap and US Navy drone seizure (2016) China Lake, California Chomsky, Noam choreography chunking Cicero civilians Clarke, Arthur Charles von Clausewitz, Carl on character on culmination on defence on genius on grammar of war on materiel on nature on poker on willpower on wrestling codebreaking cognitive empathy Cold War (1947–9) arms race Berlin Crisis (1961) Cuban Missile Crisis (1962) F-117 Nighthawk Iran-Iraq War (1980–88) joint action Korean War (1950–53) nuclear weapons research and SR-71 Blackbird U2 incident (1960) Vienna Summit (1961) Vietnam War (1955–75) VRYAN Cole, August combinatorial creativity combinatorial explosion combined arms common sense computers creativity cyber security games graphics processing unit (GPU) mice Moore’s Law symbolic logic viruses VRYAN confirmation bias connectionism consequentialism conservatism Convention on Conventional Weapons ConvNets copying Cormorant cortical interfaces cost-benefit analysis counterfactual regret minimization counterinsurgency doctrine courageous restraint COVID-19 pandemic (2019–21) creativity combinatorial exploratory genetic engineering and mental disorders and transformational criminal law CRISPR, crows Cruise, Thomas Cuban Missile Crisis (1962) culmination Culture novels (Banks) cyber security cybernetics cyborgs Cyc cystic fibrosis D-21 drones Damasio, Antonio dance DARPA autonomous vehicle research battlespace manager codebreaking research cortical interface research cyborg beetle Deep Green expert system programme funding game theory research LongShot programme Mayhem Ng’s helicopter Shakey understanding and reason research unmanned aerial combat research Dartmouth workshop (1956) Dassault data DDoS (distributed denial-of-service) dead hand system decision-making technology Deep Blue deep fakes Deep Green DeepMind AlphaGo Atari playing meta-learning research MuZero object recognition research Quake III competition (2019) deep networks defence industrial complex Defence Innovation Unit Defence Science and Technology Laboratory defence delayed gratification demons deontological approach depth charges Dionysus DNA (deoxyribonucleic acid) dodos dogfighting Alpha domains dot-matrix tongue Dota II (2013 game) double effect drones Cormorant D-21 GJ-11 Sharp Sword Global Hawk Gorgon Stare kamikaze loitering munitions nEUROn operators Predator Reaper reconnaissance RQ-170 Sentinel S-70 Okhotnik surveillance swarms Taranis wingman role X-37 X-47b dual use technology Eagleman, David early warning systems Echelon economics Edge of Tomorrow (2014 film) Eisenhower, Dwight Ellsberg, Daniel embodied cognition emotion empathy encryption entropy environmental niches epilepsy epistemic community escalation ethics Asimov’s rules brain and consequentialism deep brain stimulation and deontological approach facial recognition and genetic engineering and golden rule honour hunter-gatherer bands and identity just war post-conflict reciprocity regulation surveillance and European Union (EU) Ex Machina (2014 film) expert systems exploratory creativity extra limbs Eye in the Sky (2015 film) F-105 Thunderchief F-117 Nighthawk F-16 Fighting Falcon F-22 Raptor F-35 Lightning F/A-18 Hornet Facebook facial recognition feedback loops fighting power fire and forget firmware 5G cellular networks flow fog of war Ford forever wars FOXP2 gene Frahm, Nils frame problem France Fukushima nuclear disaster (2011) Future of Life Institute fuzzy logic gait recognition game theory games Breakout (1976) chess, see chess chicken Dota II (2013) Go, see Go Montezuma’s Revenge (1984) poker Quake III (1999) Space Invaders (1978) StarCraft II (2010) toy universes zero sum games gannets ‘garbage in, garbage out’ Garland, Alexander Gates, William ‘Bill’ Gattaca (1997 film) Gavotti, Giulio Geertz, Clifford generalised intelligence measure Generative Adversarial Networks genetic engineering genetic selection algorithms genetically modified crops genius Germany Berlin Crisis (1961) Nuremburg Trials (1945–6) Russian hacking operation (2015) World War I (1914–18) World War II (1939–45) Ghost in the Shell (comic book) GJ-11 Sharp Sword Gladwell, Malcolm Global Hawk drone global positioning system (GPS) global workspace Go (game) AlphaGo Gödel, Kurt von Goethe, Johann golden rule golf Good Judgment Project Google BERT Brain codebreaking research DeepMind, see DeepMind Project Maven (2017–) Gordievsky, Oleg Gorgon Stare GPT series grammar of war Grand Challenge aerial combat autonomous vehicles codebreaking graphics processing unit (GPU) Greece, ancient grooming standard Groundhog Day (1993 film) groupthink guerilla warfare Gulf War First (1990–91) Second (2003–11) hacking hallucinogenic drugs handwriting recognition haptic vest hardware Harpy Hawke, Ethan Hawking, Stephen heat-seeking missiles Hebrew Testament helicopters Hellfire missiles Her (2013 film) Hero-30 loitering munitions Heron Systems Hinton, Geoffrey Hitchhiker’s Guide to the Galaxy, The (Adams) HIV (human immunodeficiency viruses) Hoffman, Frank ‘Holeshot’ (Cole) Hollywood homeostasis Homer homosexuality Hongdu GJ-11 Sharp Sword honour Hughes human in the loop human resources human-machine teaming art cyborgs emotion games King Midas problem prediction strategy hunter-gatherer bands Huntingdon’s disease Hurricane fighter aircraft hydraulics hypersonic engines I Robot (Asimov) IARPA IBM identity Iliad (Homer) image analysis image recognition cat detector imagination Improbotics nformation dominance information warfare innateness intelligence analysts International Atomic Energy Agency International Criminal Court international humanitarian law internet of things Internet IQ (intelligence quotient) Iran Aegis attack (1988) Iraq War (1980–88) nuclear weapons Stuxnet attack (2010) Iraq Gulf War I (1990–91) Gulf War II (2003–11) Iran War (1980–88) Iron Dome Israel Italo-Turkish War (1911–12) Jaguar Land Rover Japan jazz JDAM (joint directed attack munition) Jeopardy Jobs, Steven Johansson, Scarlett Johnson, Lyndon Joint Artificial Intelligence Center (JAIC) de Jomini, Antoine jus ad bellum jus in bello jus post bellum just war Kalibr cruise missiles kamikaze drones Kasparov, Garry Kellogg Briand Pact (1928) Kennedy, John Fitzgerald KGB (Komitet Gosudarstvennoy Bezopasnosti) Khrushchev, Nikita kill chain King Midas problem Kissinger, Henry Kittyhawk Knight Rider (television series) know your enemy know yourself Korean War (1950–53) Kratos XQ-58 Valkyrie Kubrick, Stanley Kumar, Vijay Kuwait language connectionism and genetic engineering and natural language processing pattern recognition and semantic webs translation universal grammar Law, Jude LeCun, Yann Lenat, Douglas Les, Jason Libratus lip reading Litvinenko, Alexander locked-in patients Lockheed dogfighting trials F-117 Nighthawk F-22 Raptor F-35 Lightning SR-71 Blackbird logic loitering munitions LongShot programme Lord of the Rings (2001–3 film trilogy) LSD (lysergic acid diethylamide) Luftwaffe madman theory Main Battle Tanks malum in se Manhattan Project (1942–6) Marcus, Gary Maslow, Abraham Massachusetts Institute of Technology (MIT) Matrix, The (1999 film) Mayhem McCulloch, Warren McGregor, Wayne McNamara, Robert McNaughton, John Me109 fighter aircraft medical field memory Merkel, Angela Microsoft military industrial complex Mill, John Stuart Milrem mimicry mind merge mind-shifting minimax regret strategy Minority Report (2002 film) Minsky, Marvin Miramar air base, San Diego missiles Aegis combat system agency and anti-missile gunnery heat-seeking Hellfire missiles intercontinental Kalibr cruise missiles nuclear warheads Patriot missile interceptor Pershing II missiles Scud missiles Tomahawk cruise missiles V1 rockets V2 rockets mission command mixed strategy Montezuma’s Revenge (1984 game) Moore’s Law mosaic warfare Mueller inquiry (2017–19) music Musk, Elon Mutually Assured Destruction (MAD) MuZero Nagel, Thomas Napoleon I, Emperor of the French Napoleonic France (1804–15) narrowness Nash equilibrium Nash, John National Aeronautics and Space Administration (NASA) National Security Agency (NSA) National War College natural language processing natural selection Nature navigation computers Nazi Germany (1933–45) needle-in-a-haystack problems Netflix network enabled warfare von Neumann, John neural networks neurodiversity nEUROn drone neuroplasticity Ng, Andrew Nixon, Richard normal accident theory North Atlantic Treaty Organization (NATO) North Korea nuclear weapons Cuban Missile Crisis (1962) dead hand system early warning systems F-105 Thunderchief and game theory and Hiroshima and Nagasaki bombings (1945) Manhattan Project (1942–6) missiles Mutually Assured Destruction (MAD) second strike capability submarines and VRYAN and in WarGames (1983 film) Nuremburg Trials (1945–6) Obama, Barack object recognition Observe Orient Decide and Act (OODA) offence-defence balance Office for Naval Research Olympic Games On War (Clausewitz), see Clausewitz, Carl OpenAI optogenetics Orca submarines Ottoman Empire (1299–1922) pain Pakistan Palantir Palmer, Arnold Pandemonium Panoramic Research Papert, Seymour Parkinson’s disease Patriot missile interceptors pattern recognition Pearl Harbor attack (1941) Peloponnesian War (431–404 BCE) Pentagon autonomous vehicle research codebreaking research computer mouse development Deep Green Defence Innovation Unit Ellsberg leaks (1971) expert system programme funding ‘garbage in, garbage out’ story intelligence analysts Project Maven (2017–) Shakey unmanned aerial combat research Vietnam War (1955–75) perceptrons Perdix Pershing II missiles Petrov, Stanislav Phalanx system phrenology pilot’s associate Pitts, Walter platform neutrality Pluribus poker policing polygeneity Portsmouth, Hampshire Portuguese Man o’ War post-traumatic stress disorder (PTSD) Predator drones prediction centaur teams ‘garbage in, garbage out’ story policing toy universes VRYAN Prescience principles of war prisoners Project Improbable Project Maven (2017–) prosthetic arms proximity fuses Prussia (1701–1918) psychology psychopathy punishment Putin, Vladimir Pyeongchang Olympics (2018) Qinetiq Quake III (1999 game) radar Rafael RAND Corporation rational actor model Rawls, John Re:member (Arnalds) Ready Player One (Cline) Reagan, Ronald Reaper drones reciprocal punishment reciprocity reconnaissance regulation ban, campaigns for defection self-regulation reinforcement learning remotely piloted air vehicles (RPAVs) revenge porn revolution in military affairs Rid, Thomas Robinson, William Heath Robocop (1987 film) Robotics Challenge robots Asimov’s rules ATLAS Boston Dynamics homeostatic Shakey symbolic logic and Rome Air Defense Center Rome, ancient Rosenblatt, Frank Royal Air Force (RAF) Royal Navy RQ-170 Sentinel Russell, Stuart Russian Federation German hacking operation (2015) Litvinenko murder (2006) S-70 Okhotnik Skripal poisoning (2018) Ukraine War (2014–) US election interference (2016) S-70 Okhotnik SAGE Said and Done’ (Frahm) satellite navigation satellites Saudi Arabia Schelling, Thomas schizophrenia Schwartz, Jack Sea Hunter security dilemma Sedol, Lee self-actualisation self-awareness self-driving cars Selfridge, Oliver semantic webs Shakey Shanahan, Murray Shannon, Claude Shogi Silicon Valley Simon, Herbert Single Integrated Operations Plan (SIOP) singularity Siri situational awareness situationalist intelligence Skripal, Sergei and Yulia Slaughterbots (2017 video) Slovic, Paul smartphones Smith, Willard social environments software Sophia Sorcerer’s Apprentice, The (Goethe) South China Sea Soviet Union (1922–91) aircraft Berlin Crisis (1961) Chernobyl nuclear disaster (1986) Cold War (1947–9), see Cold War collapse (1991) Cuban Missile Crisis (1962) early warning systems Iran-Iraq War (1980–88) Korean War (1950–53) nuclear weapons radar technology U2 incident (1960) Vienna Summit (1961) Vietnam War (1955–75) VRYAN World War II (1939–45) Space Invaders (1978 game) SpaceX Sparta Spike Firefly loitering munitions Spitfire fighter aircraft Spotify Stanford University Stanley Star Trek (television series) StarCraft II (2010 game) stealth strategic bombing strategic computing programme strategic culture Strategy Robot strategy Strava Stuxnet sub-units submarines acoustic decoys nuclear Orca South China Sea incident (2016) subroutines Sukhoi Sun Tzu superforecasting surveillance swarms symbolic logic synaesthesia synthetic operation environment Syria Taliban tanks Taranis drone technological determinism Tempest Terminator franchise Tesla Tetlock, Philip theory of mind Threshold Logic Unit Thucydides TikTok Tomahawk cruise missiles tongue Top Gun (1986 film) Top Gun: Maverick (2021 film) torpedoes toy universes trade-offs transformational creativity translation Trivers, Robert Trump, Donald tumours Turing, Alan Twitter 2001: A Space Odyssey (1968 film) Type-X Robotic Combat Vehicle U2 incident (1960) Uber Uexküll, Jacob Ukraine ultraviolet light spectrum umwelts uncanny valley unidentified flying objects (UFOs) United Kingdom AI weapons policy armed force, size of Battle of Britain (1940) Bletchley Park codebreaking Blitz (1940–41) Cold War (1947–9) COVID-19 pandemic (2019–21) DeepMind, see DeepMind F-35 programme fighting power human rights legislation in Litvinenko murder (2006) nuclear weapons principles of war Project Improbable Qinetiq radar technology Royal Air Force Royal Navy Skripal poisoning (2018) swarm research wingman concept World War I (1914–18) United Nations United States Afghanistan War (2001–14) Air Force Army Research Lab Army Signal Corps Battle of Midway (1942) Berlin Crisis (1961) Bin Laden assassination (2011) Black Lives Matter protests (2020) centaur team research Central Intelligence Agency (CIA) Challenger Space Shuttle disaster (1986) Cold War (1947–9), see Cold War COVID-19 pandemic (2019–21) Cuban Missile Crisis (1962) culture cyber security DARPA, see DARPA Defense Department drones early warning systems F-35 programme Gulf War I (1990–91) Gulf War II (2003–11) IARPA Iran Air shoot-down (1988) Korean War (1950–53) Manhattan Project (1942–6) Marines Mueller inquiry (2017–19) National Security Agency National War College Navy nuclear weapons Office for Naval Research Patriot missile interceptor Pearl Harbor attack (1941) Pentagon, see Pentagon Project Maven (2017–) Rome Air Defense Center Silicon Valley strategic computing programme U2 incident (1960) Vienna Summit (1961) Vietnam War (1955–75) universal grammar Universal Schelling Machine (USM) unmanned aerial vehicles (UAVs), see drones unsupervised learning utilitarianism UVision V1 rockets V2 rockets Vacanti mouse Valkyries Van Gogh, Vincent Vietnam War (1955–75) Vigen, Tyler Vincennes, USS voice assistants VRYAN Wall-e (2008 film) WannaCry ransomware War College, see National War College WarGames (1983 film) warrior ethos Watson weapon systems WhatsApp Wiener, Norbert Wikipedia wingman role Wittgenstein, Ludwig World War I (1914–18) World War II (1939–45) Battle of Britain (1940) Battle of Midway (1942) Battle of Sedan (1940) Bletchley Park codebreaking Blitz (1940–41) Hiroshima and Nagasaki bombings (1945) Pearl Harbor attack (1941) radar technology V1 rockets V2 rockets VRYAN and Wrangham, Richard Wright brothers WS-43 loitering munitions Wuhan, China X-37 drone X-drone X-rays YouTube zero sum games

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Driverless: Intelligent Cars and the Road Ahead
by Hod Lipson and Melba Kurman
Published 22 Sep 2016

See General Motors Corporation Google Google’s car accidents, 62, 63 Google’s Chauffeur project, 48, 58, 62, 102, 168–169 Google’s HD maps, 237, 238 Google’s self-driving prototype, 45, 77 Government oversight. See Federal policy on autonomous vehicles) GPS. See Global positioning system GPUs. See Graphics processing units Graphic processing units (GPUs), 221, 222 Hackers, 99, 195, 249, 274 Handoff problem, See also Human in the loop, 57, 58, 62, 63 Hardware sensors, 171–185. See also Digital cameras; High-definition digital maps; IMU; LIDAR; Radar sensor Hebb, Donald, 201 Hebbian learning, 201 Herculano-Houzel, Suzana, 73 High-definition digital maps, 11, 171–173, 238, 239 High-level controls, 75, 76, 81, 82 Highway infrastructure Dumb highways, 141–143 See also Electronic Highways Hinton, Geoffrey, 224 Hubel, David, 229 Human in the loop, 55–62 Humansafe rating, 102–104 ILSVRC.

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The gaming industry solved its processing bottleneck in another way: by running several processes in parallel. Instead of making faster processors, hardware manufacturers catering to gaming companies created special graphics cards containing thousands of processors running in parallel. Graphics cards contain massively parallel graphics processors called graphics processing units, or GPUs (to distinguish them from the more traditional general-purpose central processing units, CPUs). For years, GPUs were confined to niche applications in graphic design and gaming. Performance improvement curves for CPUs are exponential. GPU performance curves have even steeper exponentials, a trend that did not go unnoticed.

RDF Database Systems: Triples Storage and SPARQL Query Processing
by Olivier Cure and Guillaume Blin
Published 10 Dec 2014

See First-order logic (FOL) Franz Inc., 6 Fuseki SPARQL server, 120 G Garlik, 120 Generalized search trees (GiSTs), 14 GiSTs. See Generalized search trees (GiSTs) Gnutella, 174 Google, 3, 23, 52 Google’s Bigtable, 119, 140 Gossip, 174 GPUs. See Graphics processing units (GPUs) GraphDB family. See OWLIM Graphical user interface (GUI), 187 Graphics processing units (GPUs), 38 Greenplum system, 20 Gremlin query language, 35 GRIN system, 116 Groovy, 79 gStore system, 116 GUI. See Graphical user interface (GUI) H Hadoop DFS, 37, 175 Hadoop framework, 102 HadoopRDF, 180 Handshake, 20 Hard-disk drives (HDDs), 13 HashB-dh solution, 85 HBase system, 28, 138, 139, 180 HDDs.

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The needed adaptations have to consider the evolution of hardware that has happened during the last few years—for example, the cost of main memory is decreasing so rapidly that servers with hundreds of gigabytes is not uncommon; SSDs are getting less expensive and are starting to replace disks in some situations; faster CPUs and networks are arising; computing with graphics processing units (GPUs) is easier through APIs and programming languages; and dominance of shared-nothing architecture is being confirmed. The main components responsible for the performance bottleneck of current RDBMS systems have been identified in Harizopoulos et al. (2008) and are related to ACID transactions (i.e., logging, locking, and latching), as well as buffer management operations.

pages: 350 words: 98,077

Artificial Intelligence: A Guide for Thinking Humans
by Melanie Mitchell
Published 14 Oct 2019

classification; in convolutional neural networks classification module Clever Hans Clune, Jeff cognitron common sense; in babies; knowledge in Cyc; for self-driving cars concepts as mental simulations conceptual slippage connectionism connectionist networks, see connectionism consciousness ConvNet, see convolutional neural networks convolution convolutional filter convolutional kernel convolutional neural networks; abstraction abilities; activation maps in; classification module; commercial applications of; comparison with humans on object recognition; in deep Q-learning; fully connected layers of; input to; output of; structure of; training; tuning hyperparameters of Cope, David Copycat; letter-string microworld core knowledge; for self-driving cars Crawford, Kate creativity cybernetics Cyc D DARPA, see Defense Advanced Research Projects Agency Dartmouth AI workshop data snooping Davis, Ernest decoder network Deep Blue deep learning; adversarial examples for, see adversarial examples; as “Clever Hans”; difference from human perception; explainability of; inspiration from neuroscience; lack of reliability; as narrow AI; need for big data; see also convolutional neural networks; encoder-decoder system; encoder networks; neural machine translation; recurrent neural networks DeepMind; acquisition by Google; see also AlphaGo; Breakout deep neural networks, see deep learning deep Q-learning; adversarial examples for; on Breakout; compared with random search; convolutional network in; on Go; transfer abilities deep Q-network Defense Advanced Research Projects Agency depth of a neural network Diamandis, Peter distributional semantics Domingos, Pedro Dowd, Maureen Dreyfus, Hubert E edge cases ELIZA (chatbot) embodiment hypothesis EMI, see Experiments in Musical Intelligence encoder-decoder system encoder networks episode epoch Etzioni, Oren Eugene Goostman (chatbot) Evans, Claire Experiments in Musical Intelligence expert systems explainable AI exploration versus exploitation exponential function exponential growth exponential progress F face recognition; adversarial attacks on; biases in; ethics of Fan, Hui Farhadi, Ali Ferrucci, David Firth, John Foundalis, Harry French, Robert Fukushima, Kunihiko Future of Humanity Institute Future of Life Institute G game tree; in checkers; in chess; in Go Gates, Bill GEB, see Gödel, Escher, Bach general or human-level AI General Problem Solver genetic art geofencing Gershwin, Ira Go (board game); see also AlphaGo Gödel, Escher, Bach (book) GOFAI Good, I. J. Goodfellow, Ian Google DeepMind, see DeepMind Google Translate; see also neural machine translation Gottschall, Jonathan GPS, see General Problem Solver GPUs, see graphical processing units gradient descent graphical processing units H HAL Hassabis, Demis Hawking, Stephen Hearst, Eliot hidden layers hidden units, see hidden layers Hinton, Geoffrey Hofstadter, Douglas Horvitz, Eric Hubel, David human-level AI, see general or human-level AI hyperparameters I IBM Watson image captioning ImageNet; cheating incident; competitions; human performance on; localization challenge; pretraining on; relation to top-1 accuracy metric; top-5 accuracy metric; WordNet imitation game, see Turing test intuitive knowledge, see core knowledge J Jefferson, Geoffrey Jennings, Ken Jeopardy!

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Moreover, there’s an excellent chance it was “pretrained” on images from ImageNet to learn generic visual features before being “fine-tuned” for more specific tasks. Given that the extensive training required by ConvNets is feasible only with specialized computer hardware—typically, powerful graphical processing units (GPUs)—it is not surprising that the stock price of the NVIDIA Corporation, the most prominent maker of GPUs, increased by over 1,000 percent between 2012 and 2017. Have ConvNets Surpassed Humans at Object Recognition? As I learned more about the remarkable success of ConvNets, I wondered how close they were to rivaling our own human object-recognition abilities.

pages: 346 words: 97,890

The Road to Conscious Machines
by Michael Wooldridge
Published 2 Nov 2018

A A* 77 À la recherche du temps perdu (Proust) 205–8 accountability 257 Advanced Research Projects Agency (ARPA) 87–8 adversarial machine learning 190 AF (Artificial Flight) parable 127–9, 243 agent-based AI 136–49 agent-based interfaces 147, 149 ‘Agents That Reduce Work and Information Overload’ (Maes) 147–8 AGI (Artificial General Intelligence) 41 AI – difficulty of 24–8 – ethical 246–62, 284, 285 – future of 7–8 – General 42, 53, 116, 119–20 – Golden Age of 47–88 – history of 5–7 – meaning of 2–4 – narrow 42 – origin of name 51–2 – strong 36–8, 41, 309–14 – symbolic 42–3, 44 – varieties of 36–8 – weak 36–8 AI winter 87–8 AI-complete problems 84 ‘Alchemy and AI’ (Dreyfus) 85 AlexNet 187 algorithmic bias 287–9, 292–3 alienation 274–7 allocative harm 287–8 AlphaFold 214 AlphaGo 196–9 AlphaGo Zero 199 AlphaZero 199–200 Alvey programme 100 Amazon 275–6 Apple Watch 218 Argo AI 232 arithmetic 24–6 Arkin, Ron 284 ARPA (Advanced Research Projects Agency) 87–8 Artificial Flight (AF) parable 127–9, 243 Artificial General Intelligence (AGI) 41 artificial intelligence see AI artificial languages 56 Asilomar principles 254–6 Asimov, Isaac 244–6 Atari 2600 games console 192–6, 327–8 augmented reality 296–7 automated diagnosis 220–1 automated translation 204–8 automation 265, 267–72 autonomous drones 282–4 Autonomous Vehicle Disengagement Reports 231 autonomous vehicles see driverless cars autonomous weapons 281–7 autonomy levels 227–8 Autopilot 228–9 B backprop/backpropagation 182–3 backward chaining 94 Bayes nets 158 Bayes’ Theorem 155–8, 365–7 Bayesian networks 158 behavioural AI 132–7 beliefs 108–10 bias 172 black holes 213–14 Blade Runner 38 Blocks World 57–63, 126–7 blood diseases 94–8 board games 26, 75–6 Boole, George 107 brains 43, 306, 330–1 see also electronic brains branching factors 73 Breakout (video game) 193–5 Brooks, Rodney 125–9, 132, 134, 243 bugs 258 C Campaign to Stop Killer Robots 286 CaptionBot 201–4 Cardiogram 215 cars 27–8, 155, 223–35 certainty factors 97 ceteris paribus preferences 262 chain reactions 242–3 chatbots 36 checkers 75–7 chess 163–4, 199 Chinese room 311–14 choice under uncertainty 152–3 combinatorial explosion 74, 80–1 common values and norms 260 common-sense reasoning 121–3 see also reasoning COMPAS 280 complexity barrier 77–85 comprehension 38–41 computational complexity 77–85 computational effort 129 computers – decision making 23–4 – early developments 20 – as electronic brains 20–4 – intelligence 21–2 – programming 21–2 – reliability 23 – speed of 23 – tasks for 24–8 – unsolved problems 28 ‘Computing Machinery and Intelligence’ (Turing) 32 confirmation bias 295 conscious machines 327–30 consciousness 305–10, 314–17, 331–4 consensus reality 296–8 consequentialist theories 249 contradictions 122–3 conventional warfare 286 credit assignment problem 173, 196 Criado Perez, Caroline 291–2 crime 277–81 Cruise Automation 232 curse of dimensionality 172 cutlery 261 Cybernetics (Wiener) 29 Cyc 114–21, 208 D DARPA (Defense Advanced Research Projects Agency) 87–8, 225–6 Dartmouth summer school 1955 50–2 decidable problems 78–9 decision problems 15–19 deduction 106 deep learning 168, 184–90, 208 DeepBlue 163–4 DeepFakes 297–8 DeepMind 167–8, 190–200, 220–1, 327–8 Defense Advanced Research Projects Agency (DARPA) 87–8, 225–6 dementia 219 DENDRAL 98 Dennett, Daniel 319–25 depth-first search 74–5 design stance 320–1 desktop computers 145 diagnosis 220–1 disengagements 231 diversity 290–3 ‘divide and conquer’ assumption 53–6, 128 Do-Much-More 35–6 dot-com bubble 148–9 Dreyfus, Hubert 85–6, 311 driverless cars 27–8, 155, 223–35 drones 282–4 Dunbar, Robin 317–19 Dunbar’s number 318 E ECAI (European Conference on AI) 209–10 electronic brains 20–4 see also computers ELIZA 32–4, 36, 63 employment 264–77 ENIAC 20 Entscheidungsproblem 15–19 epiphenomenalism 316 error correction procedures 180 ethical AI 246–62, 284, 285 European Conference on AI (ECAI) 209–10 evolutionary development 331–3 evolutionary theory 316 exclusive OR (XOR) 180 expected utility 153 expert systems 89–94, 123 see also Cyc; DENDRAL; MYCIN; R1/XCON eye scans 220–1 F Facebook 237 facial recognition 27 fake AI 298–301 fake news 293–8 fake pictures of people 214 Fantasia 261 feature extraction 171–2 feedback 172–3 Ferranti Mark 1 20 Fifth Generation Computer Systems Project 113–14 first-order logic 107 Ford 232 forward chaining 94 Frey, Carl 268–70 ‘The Future of Employment’ (Frey & Osborne) 268–70 G game theory 161–2 game-playing 26 Gangs Matrix 280 gender stereotypes 292–3 General AI 41, 53, 116, 119–20 General Motors 232 Genghis robot 134–6 gig economy 275 globalization 267 Go 73–4, 196–9 Golden Age of AI 47–88 Google 167, 231, 256–7 Google Glass 296–7 Google Translate 205–8, 292–3 GPUs (Graphics Processing Units) 187–8 gradient descent 183 Grand Challenges 2004/5 225–6 graphical user interfaces (GUI) 144–5 Graphics Processing Units (GPUs) 187–8 GUI (graphical user interfaces) 144–5 H hard problem of consciousness 314–17 hard problems 84, 86–7 Harm Assessment Risk Tool (HART) 277–80 Hawking, Stephen 238 healthcare 215–23 Herschel, John 304–6 Herzberg, Elaine 230 heuristic search 75–7, 164 heuristics 91 higher-order intentional reasoning 323–4, 328 high-level programming languages 144 Hilbert, David 15–16 Hinton, Geoff 185–6, 221 HOMER 141–3, 146 homunculus problem 315 human brain 43, 306, 330–1 human intuition 311 human judgement 222 human rights 277–81 human-level intelligence 28–36, 241–3 ‘humans are special’ argument 310–11 I image classification 186–7 image-captioning 200–4 ImageNet 186–7 Imitation Game 30 In Search of Lost Time (Proust) 205–8 incentives 261 indistinguishability 30–1, 37, 38 Industrial Revolutions 265–7 inference engines 92–4 insurance 219–20 intelligence 21–2, 127–8, 200 – human-level 28–36, 241–3 ‘Intelligence Without Representation’ (Brooks) 129 Intelligent Knowledge-Based Systems 100 intentional reasoning 323–4, 328 intentional stance 321–7 intentional systems 321–2 internal mental phenomena 306–7 Internet chatbots 36 intuition 311 inverse reinforcement learning 262 Invisible Women (Criado Perez) 291–2 J Japan 113–14 judgement 222 K Kasparov, Garry 163 knowledge bases 92–4 knowledge elicitation problem 123 knowledge graph 120–1 Knowledge Navigator 146–7 knowledge representation 91, 104, 129–30, 208 knowledge-based AI 89–123, 208 Kurzweil, Ray 239–40 L Lee Sedol 197–8 leisure 272 Lenat, Doug 114–21 lethal autonomous weapons 281–7 Lighthill Report 87–8 LISP 49, 99 Loebner Prize Competition 34–6 logic 104–7, 121–2 logic programming 111–14 logic-based AI 107–11, 130–2 M Mac computers 144–6 McCarthy, John 49–52, 107–8, 326–7 machine learning (ML) 27, 54–5, 168–74, 209–10, 287–9 machines with mental states 326–7 Macintosh computers 144–6 magnetic resonance imaging (MRI) 306 male-orientation 290–3 Manchester Baby computer 20, 24–6, 143–4 Manhattan Project 51 Marx, Karl 274–6 maximizing expected utility 154 Mercedes 231 Mickey Mouse 261 microprocessors 267–8, 271–2 military drones 282–4 mind modelling 42 mind-body problem 314–17 see also consciousness minimax search 76 mining industry 234 Minsky, Marvin 34, 52, 180 ML (machine learning) 27, 54–5, 168–74, 209–10, 287–9 Montezuma’s Revenge (video game) 195–6 Moore’s law 240 Moorfields Eye Hospital 220–1 moral agency 257–8 Moral Machines 251–3 MRI (magnetic resonance imaging) 306 multi-agent systems 160–2 multi-layer perceptrons 177, 180, 182 Musk, Elon 238 MYCIN 94–8, 217 N Nagel, Thomas 307–10 narrow AI 42 Nash, John Forbes Jr 50–1, 161 Nash equilibrium 161–2 natural languages 56 negative feedback 173 neural nets/neural networks 44, 168, 173–90, 369–72 neurons 174 Newell, Alan 52–3 norms 260 NP-complete problems 81–5, 164–5 nuclear energy 242–3 nuclear fusion 305 O ontological engineering 117 Osborne, Michael 268–70 P P vs NP problem 83 paperclips 261 Papert, Seymour 180 Parallel Distributed Processing (PDP) 182–4 Pepper 299 perception 54 perceptron models 174–81, 183 Perceptrons (Minsky & Papert) 180–1, 210 personal healthcare management 217–20 perverse instantiation 260–1 Phaedrus 315 physical stance 319–20 Plato 315 police 277–80 Pratt, Vaughan 117–19 preference relations 151 preferences 150–2, 154 privacy 219 problem solving and planning 55–6, 66–77, 128 programming 21–2 programming languages 144 PROLOG 112–14, 363–4 PROMETHEUS 224–5 protein folding 214 Proust, Marcel 205–8 Q qualia 306–7 QuickSort 26 R R1/XCON 98–9 radiology 215, 221 railway networks 259 RAND Corporation 51 rational decision making 150–5 reasoning 55–6, 121–3, 128–30, 137, 315–16, 323–4, 328 regulation of AI 243 reinforcement learning 172–3, 193, 195, 262 representation harm 288 responsibility 257–8 rewards 172–3, 196 robots – as autonomous weapons 284–5 – Baye’s theorem 157 – beliefs 108–10 – fake 299–300 – indistinguishability 38 – intentional stance 326–7 – SHAKEY 63–6 – Sophia 299–300 – Three Laws of Robotics 244–6 – trivial tasks 61 – vacuum cleaning 132–6 Rosenblatt, Frank 174–81 rules 91–2, 104, 359–62 Russia 261 Rutherford, Ernest (1st Baron Rutherford of Nelson) 242 S Sally-Anne tests 328–9, 330 Samuel, Arthur 75–7 SAT solvers 164–5 Saudi Arabia 299–300 scripts 100–2 search 26, 68–77, 164, 199 search trees 70–1 Searle, John 311–14 self-awareness 41, 305 see also consciousness semantic nets 102 sensors 54 SHAKEY the robot 63–6 SHRDLU 56–63 Simon, Herb 52–3, 86 the Singularity 239–43 The Singularity is Near (Kurzweil) 239 Siri 149, 298 Smith, Matt 201–4 smoking 173 social brain 317–19 see also brains social media 293–6 social reasoning 323, 324–5 social welfare 249 software agents 143–9 software bugs 258 Sophia 299–300 sorting 26 spoken word translation 27 STANLEY 226 STRIPS 65 strong AI 36–8, 41, 309–14 subsumption architecture 132–6 subsumption hierarchy 134 sun 304 supervised learning 169 syllogisms 105, 106 symbolic AI 42–3, 44, 181 synapses 174 Szilard, Leo 242 T tablet computers 146 team-building problem 78–81, 83 Terminator narrative of AI 237–9 Tesla 228–9 text recognition 169–71 Theory of Mind (ToM) 330 Three Laws of Robotics 244–6 TIMIT 292 ToM (Theory of Mind) 330 ToMnet 330 TouringMachines 139–41 Towers of Hanoi 67–72 training data 169–72, 288–9, 292 translation 204–8 transparency 258 travelling salesman problem 82–3 Trolley Problem 246–53 Trump, Donald 294 Turing, Alan 14–15, 17–19, 20, 24–6, 77–8 Turing Machines 18–19, 21 Turing test 29–38 U Uber 168, 230 uncertainty 97–8, 155–8 undecidable problems 19, 78 understanding 201–4, 312–14 unemployment 264–77 unintended consequences 263 universal basic income 272–3 Universal Turing Machines 18, 19 Upanishads 315 Urban Challenge 2007 226–7 utilitarianism 249 utilities 151–4 utopians 271 V vacuum cleaning robots 132–6 values and norms 260 video games 192–6, 327–8 virtue ethics 250 Von Neumann and Morgenstern model 150–5 Von Neumann architecture 20 W warfare 285–6 WARPLAN 113 Waymo 231, 232–3 weak AI 36–8 weapons 281–7 wearable technology 217–20 web search 148–9 Weizenbaum, Joseph 32–4 Winograd schemas 39–40 working memory 92 X XOR (exclusive OR) 180 Z Z3 computer 19–20 PELICAN BOOKS Economics: The User’s Guide Ha-Joon Chang Human Evolution Robin Dunbar Revolutionary Russia: 1891–1991 Orlando Figes The Domesticated Brain Bruce Hood Greek and Roman Political Ideas Melissa Lane Classical Literature Richard Jenkyns Who Governs Britain?

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Training a deep neural net requires a huge amount of computer-processing time. The work that needs to be done in training is not particularly complex – but there is an enormous amount of it. A new type of computer processor that became common earlier this century proved to be ideal for the computational heavy lifting. Graphics Processing Units (GPUs) were originally developed to handle computer graphics problems, such as providing high-quality animations in computer games. But these chips turned out to be perfect for training neural nets. Every deep learning lab worth its name now has a stock of GPUs – but however many they have, their students will complain it is not enough.

pages: 309 words: 54,839

Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum & Smart Contracts
by David Gerard
Published 23 Jul 2017

The mining difficulty is adjusted automatically every 14 days to keep the block rate at about one every ten minutes, and in the early days the difficulty was very low indeed. Mining works by calculating one specific function over and over, as absolutely fast as possible. As far back as 2009, people had realised that graphics cards would be much more efficient128 – a graphics processing unit (GPU) is designed to run simple calculations very fast to compute video game pixels, and the same sort of processing was able to compute Bitcoin hashes eight hundred times as fast as a general CPU. By 2010, this had become the normal mining method. These were consumer graphics cards, so mining was still accessible to anyone with a few hundred dollars, and it was quite feasible to come out ahead while the price was on the upward slope of the first bubble.

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The crypto version is to take a particularly good trade and execute it yourself, before executing the customer’s order. This is illegal on conventional regulated security exchanges. Gold standard: an economy with a known and limited money supply. Bitcoin aims to implement this digitally and hark back to the days countries backed their currency with actual piles of gold. GPU: Graphics Processing Unit, the bit of a computer graphics card that computes video game pixels very fast and can also compute hashes very fast. Used to be the favoured mining method for Bitcoin before being superseded by FPGAs and ASICs; remains the favoured mining method for Ethereum. Hal Finney: Cypherpunks mailing list participant and Bitcoin’s first beta tester.

pages: 371 words: 108,317

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future
by Kevin Kelly
Published 6 Jun 2016

To recognize a spoken word, a program must be able to hear all the phonemes in relation to one another; to identify an image, it needs to see every pixel in the context of the pixels around it—both deeply parallel tasks. But until recently, the typical computer processor could ping only one thing at a time. That began to change more than a decade ago, when a new kind of chip, called a graphics processing unit, or GPU, was devised for the intensely visual—and parallel—demands of video games, in which millions of pixels in an image had to be recalculated many times a second. That required a specialized parallel computing chip, which was added as a supplement to the PC motherboard. The parallel graphics chips worked fantastically, and gaming soared in popularity.

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See also advertising commodity attention, 177–79 commodity prices, 189 communications and decentralization, 118–19, 129–31 and dematerialization, 110–11 and free markets, 146 inevitable aspects of, 3 oral communication, 204 and platforms, 125 complexity and digital storage capacity, 265–66 computers, 128, 231 connectivity, 276, 292, 294–95 consumer data, 256 content creation advertisements, 184–85 custom music, 77 early questions about, 17 and editors, 148–51, 152, 153 and emergence of user-generated content, 19, 21–22, 184–85, 269–74, 276 and Google search engines, 146–47 and hierarchical/nonhierarchical infrastructures, 148–54 impulse for, 22–23 and screen culture, 88 and sharing economy, 139 value of, 149 convergence, 291, 296 cookies, 180, 254 cooperation, 139–40, 146, 151 copper prices, 189 copying digital data and copy protection, 73 and creative remixing, 206–9 and file sharing sites, 136 free/ubiquitous flow of, 61–62, 66–68, 80, 256 generatives that add value to, 68–73 and reproductive imperative, 87 and uncopiable values, 67–68 copyright, 207–8 corporate monopolies, 294 coveillance, 259–64 Cox, Michael, 286–87 Craigslist, 145 Creative Commons licensing, 136, 139 crowdfunding, 156–61 crowdsourcing, 185 Cunningham, Ward, 135–36 curators, 150, 167, 183 customer support, 21 cyberconflict, 252, 275 dark energy and matter, 284 “dark” information, 258 Darwin, Charles, 243 data analysis and lifelogging, 250–51 “database cinema,” 200 data informing artificial intelligence, 39, 40 decentralization, 118–21 and answer-generating technologies, 289 and bottom-up participation, 154 and collaboration, 142, 143 of communication systems, 129–31 and digital socialism, 137 and emergence of the “holos,” 295 and online advertising, 182–85 and platforms, 125 and startups, 116–17 and top-down vs. bottom-up management, 153 Deep Blue, 41 deep-learning algorithms, 40 DeepMind, 32, 37, 40 deep reinforcement machine learning, 32–33 dematerialization, 110–14, 125, 131 diagnoses and diagnostic technology, 31–32, 239, 243–44 diaries and lifelogging, 248–49 Dick, Philip K., 255 diet tracking, 238 Digg, 136, 149 digitization of data, 258 directional sense, 243 discoverability, 72–73, 101 DNA sequencing, 69 documentaries, updating of, 82 domain names, 25–26 Doritos, 185 Downton Abbey (series), 282 drones, 227, 252 Dropbox, 32 drug research, 241 DVDs, 205 Dyson, Esther, 186 Eagleman, David, 225 e-banks, 254 eBay, 154, 158, 185, 263, 272, 274 ebooks and readers, 91–96 and accessibility vs. ownership, 112 advantages of, 93–95 bookshelves for, 100 fluidities of, 79 interconnectedness of, 95–96, 98, 99–100, 101–2, 104 and just-in-time purchasing, 65 liquidity of, 93 tagging content in, 98 and tracking technology, 254 echo chambers, 170 economy, 21, 65, 67–68, 136–38, 193 ecosystems of interdependent products and services, 123–24 editors, 148–51, 152, 153 education, 90, 232 Einstein, Albert, 288 electrical outlets, 253 email, 186–87, 239–40 embedded technology, 221 embodiment, 71, 224 emergent phenomena, 276–77, 295–97 emotion recognition, 220 employment and displaced workers, 49–50, 57–58 Eno, Brian, 221 entertainment costs, 190 epic failures, 278 e-retailers, 253 etiquette, social, 3–4 evolution, 247 e-wallets, 254 experience, value of, 190 expertise, 279 exports, U.S., 62 extraordinary events, 277–79 eye tracking, 219–20 Facebook and aggregated information, 147 and artificial intelligence, 32, 39, 40 and “click-dreaming,” 280 cloud of, 128, 129 and collaboration, 273 and consumer attention system, 179, 184 and creative remixing, 199, 203 face recognition of, 39, 254 and filtering systems, 170, 171 flows of posts through, 63 and future searchability, 24 and interactivity, 235 and intermediation of content, 150 and lifestreaming, 246 and likes, 140 nonhierarchical infrastructure of, 152 number of users, 143, 144 as platform ecosystem, 123 and sharing economy, 139, 144, 145 and tracking technology, 239–40 and user-generated content, 21–22, 109, 138 facial recognition, 39, 40, 43, 220, 254 fan fiction, 194, 210 fear of technology, 191 Felton, Nicholas, 239–40 Fifield, William, 288 films and film industry, 196–99, 201–2 filtering, 165–91 and advertising, 179–89 differing approaches to, 168–75 filter bubble, 170 and storage capacity, 165–67 and superabundance of choices, 167–68 and value of attention, 175–79 findability of information, 203–7 firewalls, 294 first-in-line access, 68 first-person view (FPV), 227 fitness tracking, 238, 246, 255 fixity, 78–81 Flickr, 139, 199 Flows and flowing, 61–83 and engagement of users, 81–82 and free/ubiquitous copies, 61–62, 66–68 and generative values, 68–73 move from fixity to, 78–81 in real time, 64–65 and screen culture, 88 and sharing, 8 stages of, 80–81 streaming, 66, 74–75, 82 and users’ creations, 73–74, 75–78 fluidity, 66, 79, 282 food as service (FaS), 113–14 footnotes, 201 411 information service, 285 Foursquare, 139, 246 fraud, 184 freelancers (prosumers), 113, 115, 116–17, 148, 149 Freeman, Eric, 244–45 fungibility of digital data, 195 future, blindness to, 14–22 Galaxy phones, 219 gatekeepers, 167 Gates, Bill, 135, 136 gaze tracking, 219–20 Gelernter, David, 244–46 General Electric, 160 generatives, 68–73 genetics, 69, 238, 284 Gibson, William, 214 gifs, 195 global connectivity, 275, 276, 292 gluten, 241 GM, 185 goods, fixed, 62, 65 Google AdSense ads, 179–81 and artificial intelligence, 32, 36–37, 40 book scanning projects, 208 cloud of, 128, 129 and consumer attention system, 179, 184 and coveillance, 262 and facial recognition technology, 254 and filtering systems, 172, 188 and future searchability, 24 Google Drive, 126 Google Glass, 217, 224, 247, 250 Google Now, 287 Google Photo, 43 and intellectual property law, 208–9 and lifelogging, 250–51, 254 and lifestreaming, 247–48 and photo captioning, 51 quantity of searches, 285–86 and smart technology, 223–25 translator apps of, 51 and users’ usage patterns, 21, 146–47 and virtual reality technology, 215, 216–17 and visual intelligence, 203 government, 167, 175–76, 252, 255, 261–64 GPS technology, 226, 274 graphics processing units (GPU), 38–39, 40 Greene, Alan, 31–32, 238 grocery shopping, 62, 253 Guinness Book of World Records, 278 hackers, 252 Hall, Storrs, 264–65 Halo, 227 Hammerbacher, Jeff, 280 hand motion tracking, 222 haptic feedback, 233–34 harassment, online, 264 hard singularity, 296 Harry Potter series, 204, 209–10 Hartsell, Camille, 252 hashtags, 140 Hawking, Stephen, 44 health-related websites, 179–81 health tracking, 173, 238–40, 250 heat detection, 226 hierarchies, 148–54, 289 High Fidelity, 219 Hinton, Geoff, 40 historical documents, 101 hive mind, 153, 154, 272, 281 Hockney, David, 155 Hollywood films, 196–99 holodeck simulations, 211–12 HoloLens, 216 the “holos,” 292–97 home surveillance, 253 HotWired, 18, 149, 150 humanity, defining, 48–49 hyperlinking antifacts highlighted by, 279 of books, 95, 99 of cloud data, 125–26 and creative remixing, 201–2 early theories on, 18–19, 21 and Google search engines, 146–47 IBM, 30–31, 40, 41, 128, 287 identity passwords, 220, 235 IMAX technology, 211, 217 implantable technology, 225 indexing data, 258 individualism, 271 industrialization, 49–50, 57 industrial revolution, 189 industrial robots, 52–53 information production, 257–64.

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See also Uber travel data, 239–40 trust, 67, 264 Tumblr, 136, 139 TV Guide, 72 Twitter and tweeting and aggregated information, 147 and anonymity, 263–64 and artificial intelligence, 32 and creative remixing, 194 and etiquette, 3 and filtering systems, 169, 170 and hashtags, 140 influence of, on public conversation, 140 and self-tracking technology, 239–40 sharing information on, 145 as streaming technology, 63 and user-generated content, 21, 138 Uber on-demand services of, 62, 114 and filtering systems, 172 model of, 115 peer-to-peer networking, 183–84 and social impact of connectivity, 273, 274 success of, 154 and tracking technology, 252 Uncharted 2, 227 Underkoffler, John, 222 upgrading technology, 10, 62–63 US Supreme Court, 270 Varian, Hal, 286 video and video technology ease of creating videos, 166 and filtering systems, 196–99 and lifelogging, 249 and new media fluency, 201–3 and rewindability, 204–7 streaming, 205 and user-created content, 82 video games and industry and artificial intelligence, 32–33, 230 and creative remixing, 195 and depth of content, 282 and graphics processing units (GPU), 38 interactivity of, 103, 227–34 narrative in virtual reality games, 229 and rewindability, 206 and virtual reality technology, 215–16 vigilantes, 263 Vine, 76, 194 virtual reality and computing practices, 222–23 and digital storage capacity, 265 and immersion, 226–27 and interactivity, 211–15, 218–27 revolutionary nature of, 231 social effects of, 234–35 varied uses for, 229 VR headsets, 219 Wachter, Udo, 243 Warhol, Andy, 209 Watson, 30–31, 40, 287 wearable devices growth of industry, 283 and interactivity, 224–25 and lifelogging, 248, 251 and lifestreaming, 244 and rewindability, 207 and synthetic senses, 243–44 web anticipation of users’ needs, 25 blindness to evolution of, 15–22 and context of time, 24–25 and fear of commercialization, 17–18 and future searchability, 24 genesis of, 19 and Google search engines, 146–47 hyperlinked architecture of, 18–19, 21, 146–47 page content on, 89 surfing, 188–89, 280–82 and tracking technology, 254 ubiquity of, 25 and website design, 220 See also internet WeChat, 63, 76, 124, 246 Weiswasser, Stephen, 16 Wells, H.

pages: 414 words: 109,622

Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World
by Cade Metz
Published 15 Mar 2021

But Deng could see that he and Hinton had created a system that could grow more powerful as it learned from larger amounts of data. What their prototype still lacked was the extra processing power needed to analyze all that data. In Toronto, Hinton made use of a very particular kind of computer chip called a GPU, or graphics processing unit. Silicon Valley chip makers like Nvidia originally designed these chips as a way of quickly rendering graphics for popular video games like Halo and Grand Theft Auto, but somewhere along the way, deep learning researchers realized GPUs were equally adept at running the math that underpinned neural networks.

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Department of Defense Devlin, Jacob, 273 diabetic retinopathy, using deep learning to test for, 179–80, 183–85 digital assistants, talking, 140, 169 Digital Equipment Corporation (DEC), 85 digital utopianism, 160 DistBelief, 91, 134, 135–36, 191–92, 193–94, 221 diversity issues bias within deep learning technology, 10–11, 231–32 Black in AI, 233 facial recognition systems, 231–32, 234–36 racial profiling by machines, 233 within training data, 231–32 DNNresearch acquisition by Google, 9, 198 auction for, 5–9, 11, 197 founding by Geoff Hinton, 2, 5, 98, 193 Domingos, Pedro, 192 Dota/Dota 2 (game), 281, 297 dreaming as a way of learning, 200 Duan, Rocky, 283 Duda, Richard, 24–25 Edge.org, 154 Efros, Alexei, 96–97 Eidos, 103–04 Elixir, 103–04 empiricists, 266, 268–89 Ensign, Jordi, 178 EPAC (electronic profile-analyzing computer), 17 Epstein, Jeffrey, 154 ethical issues AI Now Institute at NYU, 247 autonomous weapons, 240, 242, 244, 308 deceiving people into thinking a bot is human, 265 ethical AI team at Google, 237–38 GDPR (General Data Protection Regulation) for data privacy, 248 Google employee petition against Project Maven, 247–50 use of AI technology by bad actors, 243 Etzioni, Oren, 207, 271, 274, 291–92 Eustace, Alan acquisition of deep learning researchers and companies, 99–101, 134 at the NIPS Conference, 5, 10 problem-solving philosophy, 133–35 reflections on DeepMind’s progress, 113–14, 301 skydiving feat, 133–34, 135 “exclusive-or” concept, 25, 38–39 Facebook AI lab, 255 Applied Machine Learning Team, 255–56, 257 bid for DeepMind, 116, 121–24 building an AI machine to win at Go, 167–70, 255 Cambridge Analytica data breach, 251–52 campus, 121, 254 corporate reputation, 122–23, 125–26, 254–58 Facebook M, 169 FAIR (Facebook Artificial Intelligence Research), 119–21, 124–28 fake news scandals, 209, 252 flagged content, 253 hate speech, 253 identifying and removing unwanted content, 258–60 international presence, 207 key players, 323 live-streaming of 2019 Christchurch mosque attack, 258–59 work with natural language and complex ideas, 169, 271–72 facial recognition technology at Amazon (Amazon Rekognition), 236–38 bias, 231–32, 234–36, 308 government’s use of, 239–40, 308 at IBM, 235 law enforcement’s use of, 235–36 at Microsoft, 235–36 Fahlman, Scott, 40 FAIR (Facebook Artificial Intelligence Research), 119–21, 124–28 fake news, 208–09, 252 “Fake News Challenge,” 256–57 Fanon, Frantz, 235 Farabet, Clément, 58–59, 119–21, 197 Feinstein, Dianne, 258 Fergus, Rob, 128 Feynman, Richard, 205–06 Foo Camp, 196 Ford, Jackie, 61–62, 150–51, 309 foreign interference terrorist propaganda, 253, 256 in U.S. elections, 252, 258 the Founders Fund, 110, 112 Fukushima, Kunihiko, 51 Future of Life Institute, 157–60, 244, 291–92 games Breakout, 111–12, 113–14 capture the flag, 295–96 Dota/Dota 2, 281, 297 Go, 167–78, 214–17, 223–24, 269 Quake III, 295–97 StarCraft, 296–97 as the starting point for artificial intelligence, 111–12 GANs (generative adversarial networks) origin of, 205–06 Progressive GANs, 210 success of, 259–60 Garlock, Chris, 174 Gatsby Unit, 105–07 GDPR (General Data Protection Regulation), 248 Gebru, Timnit, 232–34, 237–38 gender issues female representation, 231–32, 237–38 male-only work environment, 130 selecting training data, 231–32 generative models, 114, 204–05 Geometric Intelligence, 197, 267–68 Ghahramani, Zoubin, 267–68 Giannandrea, John (“J.G.”), 136, 139, 241, 243, 307 Gibson, Garth, 208 Girschick, Ross, 130, 132 global platforms, 225–26 Go (game), 167–78, 214–17, 223–24, 269 Goodfellow, Ian (“the GANfather”), 56–57, 90–91, 124, 141–42, 203–06, 210–13, 282, 290, 308 Google 20 percent time, 87, 180 ability to read any Gmail message, 6 acquisition of DeepMind, 100, 112–16, 300–01 acquisition of DNNresearch, 5–7, 9, 198 Advanced Solutions Lab, 246 AdWords, 138 BERT universal language model, 273–74 building technologies to meet future needs, 197–98 campus, 83, 246 the Cat Paper, 88, 91 Chauffeur project, 137–38, 142 in China, 215–17, 220–26 corporate culture, 241–42 corporate priorities, 125 data centers, 76–77, 136, 138–39, 146–48 Duplex technology, 265–66 ethical AI, 237–38 Gang of Nine, 247 Google AI China Center, 225 Google Assistant, 264–65 Google Brain, 88–90, 124, 144, 146, 180, 183, 185–87, 212–13, 279–81 Google Cardboard, 180 Google Cloud Platform (GCP), 221–22, 245 Google Earth, 242 Google Photos, 228–30 Google Search, 83–84 Google Translate, 148–49 Google X, 83 “gorilla” tag incident, 229–30 GPU chips and cards usage, 134–36, 147 hardware development, 146–50, 215 international presence, 207, 215–17, 220–26 I/O conference, 263–65 key players, 321–22 machine translation, 144, 183 Moffett Field and Hangar One activities, 246 motivations for open-sourcing resources, 221 mythology and lore, 86–87 objections to Chinese censorship, 215–17 open research philosophy, 129–30 power consumption improvements in the data centers, 139 Project Mack Truck, 138–39 Project Marvin, 83 Project Maven, 240–50, 308 RankBrain, 139 robotics group, 279–81 speech recognition project, 74–79 tension between DeepMind and Google Brain, 185–86 TensorFlow, 220–22, 225 TPU (tensor processing unit), 148–50, 215, 222 government, U.S. Google employee petition against Project Maven, 247–50 investment in AI, 224–25 JEDI (Joint Enterprise Defense Infrastructure) contract, 243 use of facial recognition technology, 239–40 work with private industry, 241, 243, 249–50 GPU (graphics processing unit) chips and cards, 71–74, 76, 90–91, 98, 134–36, 138–40, 147 Graham, Paul, 292 Graves, Alex, 56, 95, 110–11, 114–15, 141, 204 Gulshan, Varun, 180 handwritten checks, technology to read, 52 Hassabis, Demis chess and games prowess, 101–04, 109 as cofounder of Elixir, 103–04 compared to Robert Oppenheimer, 171 and David Silver, 101–02, 103, 104–05 DeepMind’s AI system AlphaGo, 169–78, 198, 214–17, 223–24 as the founder of DeepMind, 5, 10, 123–24, 186–87 meeting with Google, 112–16 and Mustafa Suleyman, 186–87 neuroscience research, 104–05, 301 online diary, 103–04 power consumption improvements in Google’s data centers, 139 preparing for future technologies, 302 response to OpenAI, 165–66 role in the tech industry’s global arms race, 12, 244, 302 and Shane Legg, 105–07, 186–87, 300 the Singularity Summit, 109 at University College London, 104–05 hate speech, 253 Hawkins, Jeff, 82 Hebb, Donald, 31–32 Hebb’s Law, 32 Hinton, Geoff adaptability of neural networks, 181–82 and Andrew Ng, 81–82 auction for acquiring DNNresearch, 5–9 back problems, 1–2, 69–70, 306 the Boltzmann Machine, 28–30, 39–40, 41 capsule networks, 150–51, 304–05, 311 at Carnegie Mellon, 40–41 concerns about Microsoft, 192–93 deep learning movement, 64–65, 67–68, 141–42 Distillation project, 150 education, 31–34 family history and upbringing, 30–31 at Gatsby Unit, 105 at Google, 88–91, 150 ImageNet photo identification contest, 92–96 investment in Covariant, 284 and Li Deng, 69–73, 218–19 marriage and family life, 61–62, 150–51, 185, 309 and Marvin Minsky, 28–30 neural network research, 3–4, 58, 267 personal beliefs, 60–61, 244, 249, 274–75, 307 personality and style, 62–63 pyramid puzzle, 303–04 speech recognition work, 71–75 and the tech industry’s global arms race, 11–12 as a technical advisor to DeepMind, 110 trip to bid for DeepMind, 100–01 Turing Award, 305–10 at the University of Edinburgh, 32–34 at the University of Toronto, 45, 62, 129 Vector Institute for Artificial Intelligence, 207–08 work with the PDP group, 35–39 and Yann LeCun, 49–52 Hölzle, Urs, 146–48 Huang, Aja, 171, 174 Huang, Jensen, 156 Hughes, Macduff, 149 Hui, Fan, 170, 171, 172, 175 human intelligence, 288, 296–97 Hyvärinen, Aapo, 62 IBM Deep Blue supercomputer, 111, 171–72 facial recognition technology, 235 speech recognition project, 75, 77 Watson, 111 ImageNet photo identification contest, 92–96 image recognition GANs (generative adversarial networks), 205–06, 259–60 generative models, 114, 204–05 Google Photos, 228–30 “gorilla” tag incident, 229–30 importance of human judgment, 257–58 LeNet system, 46–48 and memory creation, 105 photo manipulation, 209–11 photo-realistic faces, 210 pornography, 231 Progressive GANs, 210 for teaching self-driving vehicles, 137–38 Institute of Deep Learning, 219 intelligence.

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Windows OS, 131 Livingstone, Ian, 103–04 Longuet-Higgins, Christopher, 33 Loopt, 292 LSTM (Long Short-Term Memory), 60, 141, 144–45 Lu, Qi auction for DNNresearch, 197 backwards bike incident, 189–91 at Baidu, 226–27, 308 at Carnegie Mellon, 195 at Microsoft, 192, 195–96, 198–200 plan to bring deep learning to Microsoft, 197–200 and Robin Li, 219 self-driving car research, 197–98, 226–27 upbringing, 195 machine intelligence, 288–89 machine learning ALVINN project, 43–44 NETtalk project for reading aloud, 49–50 and the Perceptron machine, 48 and racial profiling, 233 machine translation, 144, 183 Madbits, 121, 197 Maddison, Chris, 171 Malik, Jitendra, 51–52, 58, 91–92, 96 Maluuba, 199–200, 207 Marcus, Gary as cofounder of Geometric Intelligence, 197, 267–68 criticism of Google Duplex and other technologies, 265–66, 274–75 debate against Yann LeCun, 268–72 nativist background, 266–67 Mark I machine ability to learn, 19–20 initial concept, 18 letter-reading capabilities, 19–20 physical description, 19 Massachusetts Institute of Technology (MIT), 24–25, 234–36 The Master Algorithm (Domingos), 192 Mattis, James, 240, 241–43 McCarthy, John, 21–22 media relations Elon Musk’s relationship with the press, 159, 281–82, 293 Google’s objections to Chinese censorship, 215–17 industry hype about AI, 270–71 medical applications of deep learning acute kidney injury, 187–88 analyzing healthcare records, 183 diabetic retinopathy screening, 179–80, 183–85 early and accurate diagnoses, 185 National Health Service (NHS), 187–88 pharmaceutical industry, 181–83, 271 QSAR (quantitative structure-activity relationship), 182–83 Merck & Co., 181–83 microchips ANNA, 52–53 CPUs (central processing units), 90–91 GPU (graphics processing unit) chips and cards, 71–74, 76, 90–91, 98, 134–36, 138–40, 147 TPU (tensor processing unit), 148–50, 215, 222 for training neural networks, 298 Microsoft attempts to remain a leader in AI, 298–99 auction for acquiring DNNresearch, 6–7, 11, 197 Bing search engine, 195–96 Building 99 (Microsoft Research), 70–71, 192, 195–97 commitment to Windows OS instead of Linux, 131 efforts to get into the smartphone market, 190 facial recognition technology, 235–36 image recognition research, 130–31 investment in AI, 192, 298 key players, 324 natural language research, 54–56 and OpenAI, 298–99 rejection of deep learning, 192–93, 197 self-driving car project proposal from Qi Lu, 197–98 speech recognition project, 70–74, 77 “stack ranking” performance evaluations, 193 “Microsoft’s Lost Decade” (article), 192–93 military applications of AI.

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Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone
by Satya Nadella , Greg Shaw and Jill Tracie Nichols
Published 25 Sep 2017

Some early supercomputers ran on around 13,000 transistors; the Xbox One in your living room contains 5 billion. But Intel in recent years has reported that the pace of advancement has slowed, creating tremendous demand for alternative ways to provide faster and faster processing to fuel the growth of AI. The short-term results are innovative accelerators like graphics-processing unit (GPU) farms, tensor-processing unit (TPU) chips, and field-programmable gate arrays (FPGAs) in the cloud. But the dream is a quantum computer. Today we have an urgent need to solve problems that would tie up classical computers for centuries, but that could be solved by a quantum computer in a few minutes or hours.

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See also United Kingdom British Raj, 16, 186–87 broadband infrastructure, 225 Buddha, Gautama, 9 Burgum, Doug, 47–48 cable TV, 30 Cairo, 214, 218 cameras, 150 Canada, 230 cancer, 142, 159, 214 Candidate, The (film), 75 capabilities, 122–23, 141 capitalism, 237–38 late-stage, 221 Capossela, Chris, 3, 71, 81–82 Carnegie Mellon, 3 Carney, Susan L., 177 Carroll, Pete, 4 Case, Anne, 236 Cavium Networks, 20 CD-ROM, 28 CEO as curator of culture, 100, 241 “disease,” 92 panoramic view of, 118 cerebral palsy, 8–10 Chang, Emily, 129 charter city, 229 Cheng, Lili, 197 chess, 198–99 Chik, Joy, 58 child exploitation, 190 Chile, 223, 230 China, 86, 195, 220, 222, 229, 232, 236 chip design, 25 CIA, 169 Cisco, 174 civil liberties, 172–73 civil rights, 24 civil society, 179 Civil War, 188 clarity, 119 Clayton, Steve, 155 client/server era, 45 climate change, 142, 214 Clinton, Hillary, 230 cloud, 13, 41–47, 49, 51–62, 68, 70, 73, 81, 88, 110, 125, 129, 131, 137, 140, 150, 164, 166, 172, 180–81, 186, 189–92, 216, 219, 223–25, 228 cloud-first mission and, 57–58, 70, 76, 79, 83 public, 42–43, 57 Cloud for Global Good, 240–41 Codapalooza, 104 cognition, 89, 150, 152–53 Cohen, Leonard, 10 collaboration, 88, 102–3, 106–8, 126, 135, 163–64, 166, 200 collaborative robots (co-bots), 204 collective IQ, 142, 143 Colombia, 78 Columbia University, 165 Comin, Diego, 216–17, 226 commitment, shared, 77, 119 Common (hip hop artist), 71 Common Objects in Context challenge, 151 communication, 76–77 Compaq, 29 comparative advantage, 222, 228 competition, internal, 52 competitive zeal, 38–39, 70–71, 102 competitors, 39 partnerships and, 78, 125–38 complexity, 25, 224 computers early, 21–22, 24–26 future platforms, 110–11 programs by, 153–54 computing power, massive, 150–51 Conard, Edward, 220 concepts, 122–23, 141 consistency, 77–78, 182 Constitution Today, The (Amar), 186–87 constraints, 119 construction companies, 153 consumers, 49–50, 222 context, shared, 56–57 Continental Congress, 185 Continuum, 73 Convent of Jesus and Mary (India), 19 Cook, Tim, 177 cook stoves, 43 coolness, 75–76 core business, 142 Cortana, 125, 152, 156–58, 195, 201 Couchbase company, 58 counterintuitive strategy, 56–57 Coupland, Douglas, 74 Courtois, Jean-Philippe, 82 courts, 184–85 Covington and Burling lawyers, 3 Cranium games, 7 creativity, 58, 101, 119, 201, 207, 242 credit rating, 43, 204–5 Creed (film), 44–45 cricket, 18–22, 31, 35–40, 115 Cross-country Historical Adoption of Technology (CHAT), 217 culture bias and, 205 “live site first,” 61 three Cs and, 122–23, 141 transforming, 2, 11, 16, 40, 76–78, 81–82, 84, 90–92, 98–103, 105, 108–10, 113–18, 120, 122–23, 241–42 Culture (Eagleton), 91 Curiosity (Mars rover), 144 customer needs, 42, 59, 73, 80, 83, 88, 99, 101–2, 108, 126, 138 customization, 151 cybersecurity, 171, 190 cyberworld, rules for, 184 data, 60, 151 data analytics, 50 databases, 26 Data General company, 68 data management, 54 data platform, 59 data security, 175–76, 188–89 Deaton, Angus, 236 Deep Blue, 198–99 deep neural networks, 153 Delbene, Kurt, 3, 81–82 Delhi, India, 19, 31, 37 Dell, 63, 87, 127, 129–30 Dell, Michael, 129 democracy, 180 democratization, 4, 13, 69, 127, 148, 151–52 Deng Xiaoping, 229 Depardieu, Gerard, 33 design, 50, 69, 141, 239 desktop software, 27 Detroit, 15, 225, 233 developed economies, 99–100 share of world income, 236 developing economies, 99–100, 217, 225 device management solutions, 58 digital assistants, 142, 156–58, 195–98, 201 digital cable, 28 digital evidence, 191–92 Digital Geneva Convention, 171–72 digital ink, 142 digital literacy, 226–27 digital publishing laws, 185 digital transformation, 70, 126–27, 132, 235 dignity, 205 disabilities, 103, 200 disaster relief, 44 Disney, 150 disruption, 13 distributed systems, 49 diversity, 101–2, 108, 111–17, 205–6, 238, 241 Donne, John, 57 drones, 209, 226 Drucker, Peter, 90 dual users, 79 Dubai, 214, 228 Duke University, 3 Dupzyk, Kevin, 147 D-Wave, 160 Dweck, Carol, 92 dynamic learning, 100 Dynamics, 121 Dynamics 365, 152 dyslexia, 44, 103–4 Eagleton, Terry, 91 earthquakes, 44 EA Sports, 127 economic growth, 211–34 economic inequality, 12, 207–8, 214, 219–21, 225, 227, 236–41 Edge browser, 104 education, 42–44, 78, 97, 104, 106–7, 142, 145, 206–7, 224, 226–28, 234, 236–38 Egypt, 218–19, 223, 225 E-health companies, 222–23 8080 microprocessor, 21 elasticity, 49 electrical engineering (EE), 21–22 elevator and escalator business, 60 Elop, Stephen, 64, 72 email, 27, 169–73, 176 EMC, 129 emotion, 89, 197, 201 emotional intelligence (EQ), 158, 198 empathy, 6–12, 16, 40, 42–43, 93, 101, 133–34, 149, 157, 182, 197, 201, 204, 206, 226, 239, 241 employee resource groups (ERGs), 116–17 employees, 66–68, 75, 138 diversity and, 101, 111–17 empowerment and, 79–80, 126 global summit of, 86–87 hackathon, 10–11 talent development and, 117–18 empowerment, 87–88, 98–99, 106, 108–10, 126 encryption, 161–62, 175, 192–93 energy, generating across company, 119 energy costs, 237 Engelbart, Doug, 142 Engelbart’s Law, 142–43 engineers, 108–9 Enlightiks, 222–23 Enterprise Business, 81 entertainment industry, 126 ethics, 195-210, 239 Europe, 193 Excel, 121 experimental physicists, 162–64 eye-gaze tracking, 10 Facebook, 15, 44, 51, 125, 144, 174, 200, 222 failures, overcoming, 92, 111 Fairfax Financial Holdings, 20 fairness, 236 Federal Bureau of Investigation (FBI), 170, 177–78, 189 Federal Communications Commission (FCC), 28 fear of unknown, 110–11 feedback loop, 53 fertilizer, 164 Feynman, Richard, 160 fiefdoms, 52 field-programmable gate arrays (FPGAs), 161 Fields Medal, 162 firefighters, 43, 56 First Amendment, 185, 190 Flash, 136 focus, 135–36, 138 Foley, Mary Jo, 52 Ford Motor Company, 64 foreign direct investment, 219, 225, 229 Foreign Intelligence Surveillance Act (FISA), 173 Fourth Amendment, 185–88, 190, 193 France, 223, 236 Franco, James, 169 Franklin, Benjamin, 186 Freedman, Michael, 162, 166 free speech, 170–72, 175, 179, 185, 190, 238 Fukushima nuclear plant, 44 G20 nations, 219 Galaxy Explorer, 148 game theory, 123–24 Gandhi, Mohandas Mahatma, 16 Gartner Inc., 145 Gates, Bill, 4, 12, 21, 28, 64, 46, 67–69, 73–75, 87, 91, 127, 146, 183, 203 Gavasker, Sunil, 36 GE, 3, 126–27, 237 Gelernter, David, 143, 183 Geneva Convention, Fourth (1949), 171 Georgia Pacific, 29 Germany, 220, 223, 227–36 Gervais, Michael, 4–5 Gini, Corrado, 219 Gini coefficient, 219–21 GLEAM, 117 Gleason, Steve, 10–11 global competitiveness, 78–79, 100–102, 215 global information, policy and, 191 globalization, 222, 227, 235–37 global maxima, 221–22 goals, 90, 136 Goethe, J.W. von, 155 Go (game), 199 Goldman Sachs, 3 Google, 26, 45, 70–72, 76, 127, 160, 173–74, 200 partnership with, 125, 130–32 Google DeepMind, 199 Google Glass, 145 Gordon, Robert, 234 Gosling, James, 26 government, 138, 160 cybersecurity and, 171–79 economic growth and, 12, 223–24, 226–28 policy and, 189–92, 223–28 surveillance and, 173–76, 181 Grace Hopper, 111–14 graph coloring, 25 graphical user interfaces (GUI), 26–27 graphics-processing unit (GPU), 161 Great Convergence, the (Baldwin), 236 Great Recession (2008), 46, 212 Greece, 43 Green Card (film), 33 Guardians of Peace, 169 Gutenberg Bible, 152 Guthrie, Scott, 3, 58, 60, 82, 171 H1B visa, 32–33 habeas corpus, 188 Haber, Fritz, 165 Haber process, 165 hackathon, 103–5 hackers, 169–70, 177, 189, 193 Hacknado, 104 Halo, 156 Hamaker, Jon, 157 haptics, 148 Harvard Business Review, 118 Harvard College, 3 Harvey Mudd College, 112 Hawking, Stephen, 13 Hazelwood, Charles, 180 head-mounted computers, 144–45 healthcare, 41–42, 44, 142, 155–56, 159, 164, 198, 218, 223, 225, 237 Healthcare.gov website, 3, 81, 238 Heckerman, David, 158 Hewlett Packard, 63, 87, 127, 129 hierarchy, 101 Himalayas, 19 Hindus, 19 HIV/AIDS, 159, 164 Hobijn, Bart, 217 Hoffman, Reid, 232, 233 Hogan, Kathleen, 3, 80–82, 84 Holder, Eric, 173–74 Hollywood, 159 HoloLens, 69, 89, 125, 144–49, 236 home improvement, 149 Hong Kong, 229 Hood, Amy (CFO), 3, 5, 82, 90 Horvitz, Eric, 154, 208 hospitals, 42, 78, 145, 153, 223 Hosseini, Professor, 23 Huang, Xuedong, 151 human capital, 223, 226 humanistic approach, 204 human language recognition, 150–51, 154–55 human performance, augmented by technology, 142–43, 201 human rights, 186 Hussain, Mumtaz, 36, 37 hybrid computing, 89 Hyderabad, 19, 36–37, 92 Hyderabad Public School (HPS), 19–20, 22, 37–38, 136 hyper-scale, cloud-first services, 50 hypertext, 142 IBM, 1, 160, 174, 198 IBM Watson, 199–200 ideas, 16, 42 Illustrator, 136 image processing, 24 images, moving, 109 Imagine Cup competition, 149 Immelt, Jeff, 237 Immigration and Naturalization Act (1965), 24, 32–33 import taxes, 216 inclusiveness, 101–2, 108, 111, 113–17, 202, 206, 238 independent software vendor (ISV), 26 India, 6, 12, 17–22, 35–37, 170, 186–87, 222–23, 236 immigration from, 22–26, 32–33, 114–15 independence and, 16–17, 24 Indian Administrative Service (IAS), 16–17, 31 Indian Constitution, 187 Indian Institutes of Technology (IIT), 21, 24 Indian Premier League, 36 IndiaStack, 222–23 indigenous peoples, 78 Indonesia, 223, 225 industrial policy, 222 Industrial Revolution, 215 Fourth or future, 12, 239 information platforms, 206 information technology, 191 Infosys, 222 infrastructure, 88–89, 152–53, 213 innovation, 1–2, 40, 56, 58, 68, 76, 102, 111, 120, 123, 142, 212, 214, 220, 224, 234 innovator’s dilemma, 141–42 insurance industry, 60 Intel, 21, 45, 160, 161 intellectual property, 230 intelligence, 13, 88–89, 126, 150, 154–55, 160, 169, 173, 239 intelligence communities, 173 intensity of use, 217, 219, 221, 224–26 International Congress of the International Mathematical Union, 162 Internet, 28, 30, 48, 79, 97–98, 222 access and, 225–26, 240 security and privacy and, 172–73 Internet Explorer, 127 Internet of Things (IoT), 79, 134, 142, 228 Internet Tidal Wave, 203 Intersé, 3 Interview, The (film), 169–71 intimidation, 38 investment strategy, 90, 142 iOS devices, 59, 72, 123, 132 iPad, 70, 141 iPad Pro, 123–25 iPhone, 70, 72, 85, 121–22, 125, 177–79 Irish data center, 176, 184 Islamic State (ISIS), 177 Istanbul, 214 Jaisimha, M.L., 18, 36–37 Japan, 44, 223, 230 Japanese-American internment, 188 JAVA, 26 Jeopardy (TV show), 199 Jha, Rajesh, 82 jobs, 214, 231, 239–40.

pages: 52 words: 13,257

Bitcoin Internals: A Technical Guide to Bitcoin
by Chris Clark
Published 16 Jun 2013

Since shorter rounds pay more per share, this maximizes the payout per share. Many pools now have adjustments that discourage pool hopping by making later shares worth more. 9.6 Mining Hardware Initially, Satoshi’s Bitcoin client did mining on a user’s PC, but now CPUs have been eclipsed by more efficient mining hardware. GPUs (Graphics Processing Unit - Graphics cards) are designed for doing lots of simple calculations in parallel and are orders of magnitude faster than CPUs. Recently, ASICs (Application-Specific Integrated Circuits) have been developed that are orders of magnitude faster than GPUs. At this point, miners need to have custom hardware to make mining a profitable investment.

pages: 960 words: 125,049

Mastering Ethereum: Building Smart Contracts and DApps
by Andreas M. Antonopoulos and Gavin Wood Ph. D.
Published 23 Dec 2018

This is part of the security and privacy design of P2P networks, especially as applied to blockchain networks. Recording on the Blockchain While all the nodes in Ethereum are equal peers, some of them are operated by miners and are feeding transactions and blocks to mining farms, which are computers with high-performance graphics processing units (GPUs). The mining computers add transactions to a candidate block and attempt to find a proof of work that makes the candidate block valid. We will discuss this in more detail in Chapter 14. Without going into too much detail, valid transactions will eventually be included in a block of transactions and, thus, recorded in the Ethereum blockchain.

…

The purpose of the DAG is to make the Ethash PoW algorithm dependent on maintaining a large, frequently accessed data structure. This in turn is intended to make Ethash “ASIC resistant,” which means that it is more difficult to make application-specific integrated circuits (ASIC) mining equipment that is orders of magnitude faster than a fast graphics processing unit (GPU). Ethereum’s founders wanted to avoid centralization in PoW mining, where those with access to specialized silicon fabrication factories and big budgets could dominate the mining infrastructure and undermine the security of the consensus algorithm. Use of consumer-level GPUs for carrying out the PoW on the Ethereum network means that more people around the world can participate in the mining process.

…

EVM opcodes and gas consumption, Ethereum EVM Opcodes and Gas Consumption negative costs, Negative gas costs on test networks, Sending Ether from MetaMask tokens and, Issues with ERC20 Tokens transactions and, Transaction Gas-Transaction Gas gas cost, gas price vs., Gas Cost Versus Gas Price gas limit, Quick Glossary gasLimit field, Transaction Gas gasPrice field, Transaction Gas generator point, Public Keys, Generating a Public Key genesis block, Quick Glossary Geth (Go-Ethereum), Software Requirements for Building and Running a Client (Node)basics, Go-Ethereum (Geth)-Building Geth from source code building from source code, Building Geth from source code cloning Git repo for, Cloning the repository defined, Quick Glossary for first synchronization, Running Geth or Parity git, Software Requirements for Building and Running a Client (Node) global state trie, Reading and Writing Data Go, Software Requirements for Building and Running a Client (Node)(see also Geth (Go-Ethereum)) GovernMental Ponzi schemeblock timestamp-based attack, Real-World Example: GovernMental DoS vulnerability, Real-World Examples: GovernMental graphics processing unit (GPU), mining and, Ethash: Ethereum’s Proof-of-Work Algorithm H halting problem, Ethereum and Turing Completeness, Turing Completeness and Gas hard forks, Quick Glossary, Ethereum’s Four Stages of Development, Ethereum Fork History-Other Notable Ethereum Forks, The DAO Hard Fork-Timeline of the DAO Hard Fork(see also DAO; other specific hard forks, e.g.: Spurious Dragon) hardened derivationfor child private keys, Hardened child key derivation index numbers for, Index numbers for normal and hardened derivation hardware wallets, Wallet Best Practices, Extended public and private keys hash collision, Cryptographic Hash Functions hash functions, Cryptographic Hash Functions-Which Hash Function Am I Using?

pages: 156 words: 15,746

Personal Finance with Python
by Max Humber

Solve a problem. Get a Dogecoin. Easy-peasy. While you can run mining applications on your laptop, those serious about mining—for Dogecoin or otherwise—opt to run these sorts of applications on specialized rigs. A decent rig can be incredibly expensive, though. A mining rig requires specialized graphical processing units (GPUs), a good motherboard, a lot of RAM, a decent CPU, a case, some fans, and a bunch of other components. Casually dropping $3,000 on parts for a mining setup is pretty standard these days. For the purposes of this chapter, let’s pretend that we front $3,000 for the necessary components.

pages: 273 words: 72,024

Bitcoin for the Befuddled
by Conrad Barski
Published 13 Nov 2014

The incredible rise in computational power used for Bitcoin mining derives from a combination of wider adoption and the use of increasingly specialized hardware. In the first year, most miners used the CPUs on their laptops to mine bitcoins. Then people realized they could repurpose graphics cards designed originally for demanding computer games to mine bitcoins. The graphics cards, specifically the graphics-processing units (GPUs) on them, were thousands of times faster and more energy efficient than CPUs. Not long thereafter, hardware developers discovered they could use field programmable gate arrays (FPGAs), which are specialized devices used for computer chip prototype development, to mine bitcoins even faster than GPUs.

…

SPV wallets, 193–195 fungibility, of currency, 118 G generator point, elliptic curve cryptography, 152 genesis block, 113, 165 German mark, 2n Git, installing, 227 git checkout command, 228 Gnutella, 119, 127 gold, wealth stored as, 121 gold coins, 1 goods, first exchange for bitcoins, 114 Go programming language, 226 government digital currency companies and, 111–112 risk of Bitcoin destruction by, 119 stability, and Bitcoin, 126–127 graphics-processing units (GPUs), for mining, 174 H hacker theft, likelihood of, 38 hardware, for mining, 174–175 2030 requirements, 202 energy efficiency of, 178 profitability threshold curves for comparing, 179 hardware wallets, 42–43 hash, 98, 132–133 of transactions in block, 172 hash functions, 131 for verifying information, 132–133 hash rate projecting future, 177 theoretical limits, 178–179 Hayek, Friedrich, 126 health of network, SPV wallets vs. full wallets, 195 heavyweight wallets, 191 hellomoney.js file, 220 Hello Money!

pages: 434 words: 77,974

Mastering Blockchain: Unlocking the Power of Cryptocurrencies and Smart Contracts
by Lorne Lantz and Daniel Cawrey
Published 8 Dec 2020

Mining Is About Incentives Over time, as the price per bitcoin grew and interest in more professional mining hardware resulted in new equipment, the “difficulty” of mining also went up. It did not take long before just using a regular computer to mine was not enough. Miners needed special computer hardware known as graphics processing units (GPUs) to compete. Then they started using special microprocessors called application-specific integrated circuits (ASICs) to improve efficiency. Today, most cryptocurrency mining is done in huge data centers, with racks upon racks of machines requiring large amounts of power and cooling.

…

Gox-Bitfinex jurisdiction over cryptocurrency exchanges, Jurisdiction order types in cryptocurrency exchanges, The Role of Exchanges risks of, in cryptocurrency trading, Exchange Risk types of cryptocurrency exchanges, Jurisdiction externally owned account (EOA) wallets, Multisignature Contracts F Fabric (Hyperledger), Hyperledger FacebookLibra Association, The Libra Association Novi wallet, Novi false stake attacks, Proof-of-Stake faucets (Ethereum testnets), Authoring a smart contract Federal Reserve (see US Federal Reserve) federated sidechains, Sidechains fiat currencies, Electronic Systems and Trustblockchain-based assets pegged to, Stablecoins mint-based model, The Whitepaper file storage in web applications, Web 3.0 Financial Action Task Force (FATF), Travel Rule, The FATF and the Travel Rule Financial Crimes Enforcement Network (FinCEN), FinCEN Guidance and the Beginning of Regulation financial crisis of 2008, Electronic Systems and Trust, The 2008 Financial Crisis financial transactions, reliance on trust, Electronic Systems and Trust flash loans, Flash Loans-The Fulcrum Exploitcreating a smart contract for, Creating a Flash Loan Contract-Deploying the Contract deploying the smart contract, Deploying the Contract executing, Executing a Flash Loan-Executing a Flash Loan floatconfiguration 1, Float Configuration 1 configuration 2, Float Configuration 2 configuration 3, Float Configuration 3 timing and managing, Timing and Managing Float Force, Carl, Skirting the Laws forks, Understanding Forks-Replay attacks, Altcoins(see also altcoins) contentious hard forks, Contentious Hard Forks-Replay attacksfork of Bitcoin Cash into Bitcoin SV, The Bitcoin Cash Fork replay attacks vulnerability, Replay attacks different types of, Understanding Forks Ethereum Classic, The Ethereum Classic Fork, Forking Ethereum and the creation of Ethereum Classic fork choice rule in Ethereum 2.0, Ethereum Scaling other Ethereum forks, Other Ethereum forks in proof-of-stake networks, Proof-of-Stake fraud risk as seen by banking audits, Banking Risk Fulcrum attack, The Fulcrum Exploit full nodes (Libra), How the Libra Protocol Works funding amount, Lightning funding transactions, Funding transactions fungible tokens, Fungible and Nonfungible TokensERC-20 standard for, ERC-20 ERC-777 proposed standard for, ERC-777 futures, Derivatives G gambling, on Web 3.0, Web 3.0 gamingpermissioned ledger uses of blockchain, Gaming tracking virtual goods in games, ERC-1155 Garza, Homero Joshua, Skirting the Laws gas, Ether and GasETH Gas Station, Gas and Pricing list of gas prices by opcode, Gas and Pricing GAW Miners, Skirting the Laws GeistGeld, Altcoins Gemini, arbitrage trading on, Arbitrage Trading-Exchange APIs and Trading BotsAPI example, BTC/USD ticker call, Exchange APIs and Trading Bots Genesis block (Bitcoin), Achieving Consensus Gitcoin, Web 3.0 Gnosis, Tokenize Everything government-backed currencies (see fiat currencies) graphics processing units (GPUs), Mining Is About Incentives Grin, Mimblewimble, Beam, and Grin H halting problem, Ether and Gas hard forks, Understanding Forks hardware wallets, Wallet Type Variations, Wallets hash algorithms, Proof-of-Work hash power, Block discovery, How Omni Layer works hash rates, Proof-of-Work Hashcash, Hashcash hashes, Hashcash, Hashes-Custody: Who Holds the KeysBitcoin hash function, double SHA-256, The Merkle Root block, Storing Data in a Chain of Blocks, Block Hashes-Custody: Who Holds the Keys of information generated by transactions in Bitcoin, Introducing the Timestamp Server MD5 password hashes, Zero-Knowledge Proof Merkle root, The Merkle Root-The Merkle Root in proof-of-work cryptocurrency mining, Proof-of-Work public key hash on Bitcoin, Public and Private Keys in Cryptocurrency Systems in Satoshi Nakamoto's whitepaper, The Whitepaper health care, permissioned ledger implementations of blockchain, Health Care height number (block), Storing Data in a Chain of Blocks hex value arguments to smart contract calls, Custody and counterparty risk Honest validator framework, Ethereum Scaling Hong Kong, regulatory arbitrage, Hong Kong hot or cold storage wallets, Counterparty Risk hot wallets, Wallet Type Variations HotStuff algorithm, Borrowing from Existing Blockchains Hyperledger, Hyperledger I IBMIoT interaction by Watson and data storage in Blockchain Platform, Internet of Things toolset offering support for Hyperledger Fabric, Blockchain as a Service identifyverification of, Security Fundamentals identityand dangers of hacking, Identity and the Dangers of Hacking associating with Bitcoin addresses, The Evolution of Crypto Laundering identification services, Private Keys IDEX decentralized exchange, Decentralized Exchange Contracts illiquidity, signs of, Counterparty Risk infinite recursion, Forking Ethereum and the creation of Ethereum Classic information on blockchain industry, Information Infura, Interacting with Code initial coin offerings (ICOs), Mastercoin and Smart Contracts, Tokenize Everything, Initial Coin Offerings-Whitepaperas example of regulatory arbitrage, Initial Coin Offerings DAOs and, Decentralized Autonomous Organizations Ethereum, Tokenize Everything founder intentions, Founder Intentions funds collected into multisignature wallets, Multisignature Contracts illegal activities in, Skirting the Laws legal, regulatory, and other problems with, Tokenize Everything Mastercoin, Tokenize Everything motivations for founders versus venture-funding startups, Whitepaper other terms for, Initial Coin Offerings spectrum of ICO viability, Initial Coin Offerings token economics, Token Economics use of Ethereum platform, Use Cases: ICOs whitepaper, Whitepaper intermediary trust, Electronic Systems and Trust internetdata exchange protocols, evolution of, The More Things Change dot-com crash, Tulip Mania or the internet?

Programming Android
by Zigurd Mednieks , Laird Dornin , G. Blake Meike and Masumi Nakamura
Published 15 Jul 2011

placing applications in, Placing an Application for Distribution in the Android Market, Getting Paid Android Menu Editor, Extensions Android NDK, The Android Native Development Kit (NDK) (see NDK) Android Package Builder, Extensions android package tree, The Android Libraries Android Pre Compiler, Extensions Android projects, Making an Android Project (see projects) Android Resource Editor, Extensions Android Resource Manager, Extensions Android SDK, Installing the Android SDK and Prerequisites, Configuring the ADT plug-in, Installing the Android SDK and Prerequisites, Configuring the ADT plug-in, The Android SDK, The Android SDK, The Android SDK, The Android SDK, Adding Build Targets to the SDK, Test Drive: Confirm That Your Installation Works, Troubleshooting SDK Problems: No Build Targets, Troubleshooting SDK Problems: No Build Targets, Components of the SDK, android, Other SDK Tools, android, Keeping Up-to-Date, Example Code, Organizing Java Source, Organizing Java Source, JNI, NDK, and SDK: A Sample App, JNI, NDK, and SDK: A Sample App about, The Android SDK adding build targets, Adding Build Targets to the SDK components supported, Components of the SDK, android confirming installation, Test Drive: Confirm That Your Installation Works, Troubleshooting SDK Problems: No Build Targets downloading package, The Android SDK example code, Example Code folders for tools, The Android SDK installing, Installing the Android SDK and Prerequisites, Configuring the ADT plug-in, The Android SDK keeping up-to-date, Keeping Up-to-Date organizing Java source, Organizing Java Source, Organizing Java Source prerequisites, Installing the Android SDK and Prerequisites, Configuring the ADT plug-in sample application, JNI, NDK, and SDK: A Sample App, JNI, NDK, and SDK: A Sample App tools supported, Other SDK Tools, android troubleshooting problems, Troubleshooting SDK Problems: No Build Targets Android Virtual Device, Making an Android Virtual Device (AVD) (see AVD) Android XML Resources Editor, Extensions android.app library, The Android Libraries android.content library, The Android Libraries android.database library, The Android Libraries android.graphics library, The Android Libraries android.telephony library, The Android Libraries android.text library, The Android Libraries android.view library, The Android Libraries, Assembling a Graphical Interface android.webkit library, The Android Libraries android.widget library, The Android Libraries android.widgets package, Extending Android classes android:alwaysRetainTaskState attribute, Other activity attributes affecting task behavior android:finishOnTaskLaunch attribute, Other activity attributes affecting task behavior android:launchMode attribute, Launch mode android:name attribute, Task affinity android:noHistory attribute, Other activity attributes affecting task behavior android:process attribute, Other activity attributes affecting task behavior android:taskAffinity attribute, Task affinity AndroidManifest.xml file, Application Manifests, Application Manifests, Initialization Parameters in AndroidManifest.xml, Initialization Parameters in AndroidManifest.xml about, Application Manifests declarations in, Application Manifests initialization parameters in, Initialization Parameters in AndroidManifest.xml, Initialization Parameters in AndroidManifest.xml android_native_app_glue module, Native Activities animation, Bling, Animation, Transition animation, Animation, Background animation, Background animation, Background animation, Background animation, Surface view animation background, Background animation, Background animation frame-by-frame, Background animation, Background animation OpenGL example, Bling surface view, Surface view animation transition, Animation, Transition animation tweened, Animation Animation class, Animation, Transition animation, Transition animation, Transition animation about, Animation AnimationListener interface, Transition animation applyTransformation method, Transition animation, Transition animation AnimationDrawable class, Animation, Background animation, Background animation about, Animation, Background animation start method, Background animation AnimationListener interface, Transition animation, Transition animation about, Transition animation onAnimationEnd method, Transition animation AnimationSet class, Transition animation anonymous classes, Using Anonymous Classes, Using Anonymous Classes Apache HttpCore project, The Android Libraries APIs (application programming interfaces), Other Android Components, Specifying API-Level Compatibility, Defining a Provider Public API, Defining the CONTENT_URI, Implementing the Provider API, The delete method, Sensors, Other Sensors, Near Field Communication (NFC), P2P Mode, Gesture Input, Gesture Input, Accessibility, Accessibility accessibility, Accessibility, Accessibility Android applications and, Other Android Components application distribution and, Specifying API-Level Compatibility external sensors, Sensors, Other Sensors gesture input, Gesture Input, Gesture Input Near Field Communication, Near Field Communication (NFC), P2P Mode SimpleFinchVideoContentProvider example, Defining a Provider Public API, Defining the CONTENT_URI, Implementing the Provider API, The delete method .apk files, Running and debugging Android apps, Packaging an Android Application: The .apk File, Uploading Applications in the Market, Extensions about, Running and debugging Android apps, Packaging an Android Application: The .apk File building, Extensions uploading, Uploading Applications in the Market apkbuilder application, Packaging an Android Application: The .apk File Application class, Application Manifests, Life Cycle Methods of the Application Class, Life Cycle Methods of the Application Class about, Application Manifests life cycle methods, Life Cycle Methods of the Application Class, Life Cycle Methods of the Application Class application development, Modular Programming in Java, Modular Programming in Java, Traditional Programming Models Compared to Android, Java Coding in Eclipse, Refactoring, Editing Java Code and Code Completion, Refactoring, Applying Static Analysis to Android Code, Applying Static Analysis to Android Code, The Android Libraries, Extending Android classes, Rolling Your Own Widgets, Bitmaps, Bling, OpenGL Graphics, The SQL Language, The SQL Language, Database Design for Android Applications, Basic Structure of the SimpleVideoDbHelper Class, Basic Structure of the SimpleVideoDbHelper Class, Basic Structure of the SimpleVideoDbHelper Class, Using the Database API: MJAndroid, Using the execSQL method (see also Android applications; skeleton applications) additional information, The Android Libraries applying static analysis, Applying Static Analysis to Android Code, Applying Static Analysis to Android Code content assist, Editing Java Code and Code Completion database design, Database Design for Android Applications, Basic Structure of the SimpleVideoDbHelper Class Design for Extension coding rule, Extending Android classes graphics effects, Bling, OpenGL Graphics Java coding in Eclipse, Java Coding in Eclipse, Refactoring MJAndroid application example, Using the Database API: MJAndroid, Using the execSQL method modular programming, Modular Programming in Java, Modular Programming in Java refactoring, Refactoring rolling your own widgets, Rolling Your Own Widgets, Bitmaps SimpleVideoDbHelper class example, Basic Structure of the SimpleVideoDbHelper Class, Basic Structure of the SimpleVideoDbHelper Class SQL and, The SQL Language traditional programming models, Traditional Programming Models Compared to Android application distribution, Application Signing, Using a self-signed certificate to sign an application, Using a self-signed certificate to sign an application, Placing an Application for Distribution in the Android Market, Getting Paid, Google Maps API Keys, Specifying API-Level Compatibility, Compatibility with Many Kinds of Screens application signing, Application Signing, Using a self-signed certificate to sign an application exporting Android applications, Using a self-signed certificate to sign an application Google Maps API keys, Google Maps API Keys placing in Android Market, Placing an Application for Distribution in the Android Market, Getting Paid screen compatibility and, Compatibility with Many Kinds of Screens specifying API-level compatibility, Specifying API-Level Compatibility application programming interfaces, Other Android Components (see APIs) application signing, Application Signing, Public Key Encryption and Cryptographic Signing, Public Key Encryption and Cryptographic Signing, How Signatures Protect Software Users, Publishers, and Secure Communications, Self-signed certificates for Android software, Self-signed certificates for Android software, Signing an Application, Using a self-signed certificate to sign an application about, Application Signing cryptographic, Public Key Encryption and Cryptographic Signing, Public Key Encryption and Cryptographic Signing process overview, Signing an Application, Using a self-signed certificate to sign an application protection and, How Signatures Protect Software Users, Publishers, and Secure Communications, Self-signed certificates for Android software self-signed certificates, Self-signed certificates for Android software application template, The Android Framework (see skeleton applications) applications, Making an Android Project (see Android applications) ArrayList class, Collection implementation types, The Android Libraries Arrays class, The Android Libraries artifacts, Builders and Artifacts, Organizing Java Source defined, Builders and Artifacts projects and, Organizing Java Source assignment operator (=), Object Creation associations, defined, Associations asynchronous I/O mechanisms, Summary of Benefits AsyncTask class, Extending Android classes, AsyncTask and the UI Thread, AsyncTask and the UI Thread, AsyncTask and the UI Thread, AsyncTask and the UI Thread, AsyncTask and the UI Thread, AsyncTask and the UI Thread, AsyncTask and the UI Thread, AsyncTask and the UI Thread doInBackground method, AsyncTask and the UI Thread onClickListener method, AsyncTask and the UI Thread onPostExecute method, AsyncTask and the UI Thread onPreExecute method, AsyncTask and the UI Thread onProgressUpdate method, AsyncTask and the UI Thread publishProgress method, AsyncTask and the UI Thread subclassing and, Extending Android classes UI thread and, AsyncTask and the UI Thread, AsyncTask and the UI Thread audio, Audio and Video, Audio Playback, AudioTrack audio playback, MediaPlayer audio playback, AudioTrack audio playback, Audio Recording, AudioRecorder audio recording, MediaRecorder audio recording, MediaRecorder audio recording, Intent audio recording, AudioRecorder audio recording Android supported formats, Audio and Video AudioRecorder recording, AudioRecorder audio recording AudioTrack playback, AudioTrack audio playback Intent recording, Intent audio recording MediaPlayer playback, MediaPlayer audio playback MediaRecorder recording, MediaRecorder audio recording, MediaRecorder audio recording playback methods, Audio Playback, AudioTrack audio playback recording methods, Audio Recording, AudioRecorder audio recording AudioRecorder class, AudioRecorder audio recording, AudioRecorder audio recording audio recording, AudioRecorder audio recording startRecording method, AudioRecorder audio recording AudioTrack class, AudioTrack audio playback, AudioTrack audio playback, AudioTrack audio playback, AudioTrack audio playback, AudioTrack audio playback audio playback, AudioTrack audio playback pause method, AudioTrack audio playback play method, AudioTrack audio playback release method, AudioTrack audio playback stop method, AudioTrack audio playback AUTHENTICATE_ACCOUNTS permission, Authentication authenticating contact data, Authentication, Authentication AUTOINCREMENT constraint, Database constraints, Declaring Column Specification Strings AVD (Android Virtual Device), Making an Android Virtual Device (AVD), Making an Android Virtual Device (AVD), Making an Android Virtual Device (AVD), Making an Android Virtual Device (AVD), Making an Android Virtual Device (AVD), Running a Program on an AVD, Android Virtual Devices about, Making an Android Virtual Device (AVD), Android Virtual Devices additional information, Making an Android Virtual Device (AVD) creating, Making an Android Virtual Device (AVD), Making an Android Virtual Device (AVD) running programs on, Running a Program on an AVD setting parameters, Making an Android Virtual Device (AVD) avdmgr tool, Eclipse and Android B background animation, Background animation, Background animation BaseAdapter class, Extending Android classes Beaulieu, Alan, Additional Database Concepts bin directory, Organizing Java Source binary data, File Management and Binary Data Bitmap class, Canvas Drawing, Bitmaps BitmapDrawable class, Bitmaps BLOB type (SQLite), SQLite types Bloch, Joshua, Interfaces, Java Serialization block, defined, Final and Static Declarations Bluetooth standard, Bluetooth, The Bluetooth Protocol Stack, Bluez: The Linux Bluetooth Implementation, Using Bluetooth in Android Applications, The BtConsoleActivity class, Bluetooth and related I/O classes about, Bluetooth Android applications and, Using Bluetooth in Android Applications, The BtConsoleActivity class Linux implementation, Bluez: The Linux Bluetooth Implementation protocol stack and, The Bluetooth Protocol Stack SPP support, Bluetooth and related I/O classes BluetoothAdapter class, Bluetooth and related I/O classes BluetoothDevice class, Bluetooth and related I/O classes BluetoothSocket class, Bluetooth and related I/O classes Bluez Bluetooth stack, Bluez: The Linux Bluetooth Implementation boolean type, Primitive Types, Conventions on the Native Method Side BroadcastReceiver class, Other Android Components, BroadcastReceiver, Application Manifests, The Activity Class and Well-Behaved Applications about, Other Android Components, BroadcastReceiver manifest files and, Application Manifests well-behaved applications and, The Activity Class and Well-Behaved Applications builders, defined, Builders and Artifacts Bundle class, Serialization, Java Serialization, Java Serialization, Fragment Life Cycle, Saving and restoring instance state fragment life cycle and, Fragment Life Cycle getSerializable method, Java Serialization putSerializable method, Java Serialization serialization and, Serialization, Saving and restoring instance state Button class, Putting It Together, Wiring Up the Controller, Rolling Your Own Widgets about, Putting It Together setOnClickListener method, Wiring Up the Controller widgets and, Rolling Your Own Widgets byte type, Primitive Types, Conventions on the Native Method Side C Callback interface (Drawable), Background animation Callback interface (SurfaceHolder), Surface view animation, Surface view animation, Surface view animation about, Surface view animation surfaceCreated method, Surface view animation surfaceDestroyed method, Surface view animation callbacks, defined, Overrides and callbacks, Overrides and callbacks Camera class, Transition animation, Transition animation, Transition animation about, Transition animation rotate method, Transition animation translate method, Transition animation CAMERA permission, Recording Audio and Video Canvas class, Canvas Drawing, Canvas Drawing, Drawing text, Drawing text, Drawing text, Drawing text, Drawing text, Matrix transformations, Matrix transformations, Matrix transformations, Matrix transformations, Matrix transformations, Matrix transformations, Matrix transformations, Matrix transformations, Matrix transformations, Matrix transformations, Matrix transformations, Matrix transformations, Matrix transformations about, Canvas Drawing concatMatrix method, Matrix transformations coordinate transformation, Matrix transformations drawCircle method, Canvas Drawing drawing text, Drawing text, Drawing text drawPosText method, Drawing text drawText method, Drawing text drawTextOnPath method, Drawing text getMatrix method, Matrix transformations restore method, Matrix transformations rotate method, Matrix transformations, Matrix transformations save method, Matrix transformations scale method, Matrix transformations, Matrix transformations setMatrix method, Matrix transformations skew method, Matrix transformations, Matrix transformations translate method, Matrix transformations canvas drawing, Canvas Drawing, Canvas Drawing, Drawing text, Drawing text, Matrix transformations, Matrix transformations about, Canvas Drawing, Canvas Drawing drawing text, Drawing text, Drawing text matrix transformations, Matrix transformations, Matrix transformations cascading methods, Object Creation Cell ID, Location-Based Services certificate authority, How Signatures Protect Software Users, Publishers, and Secure Communications, Self-signed certificates for Android software certificate fingerprint, Debug certificates certificates, Self-signed certificates for Android software, Debug certificates, Creating a self-signed certificate, Using a self-signed certificate to sign an application, Using a self-signed certificate to sign an application debug, Debug certificates self-signed, Self-signed certificates for Android software, Creating a self-signed certificate, Using a self-signed certificate to sign an application, Using a self-signed certificate to sign an application char type, Primitive Types, Drawing text, Conventions on the Native Method Side CHECK constraint, Database constraints class attribute, Creating a Fragment .class files, The Java compiler, Builders and Artifacts classes, Objects and Classes, Object Creation, Object Creation, The Object Class and Its Methods, Final and Static Declarations, Final and Static Declarations, Abstract Classes, Using Anonymous Classes, Using Anonymous Classes, Extending Android classes, Extending Android classes, Classes That Support Serialization (see also specific classes) about, Objects and Classes abstract, Abstract Classes anonymous, Using Anonymous Classes, Using Anonymous Classes extending, Extending Android classes, Extending Android classes final and static declarations, Final and Static Declarations, Final and Static Declarations object creation, Object Creation, Object Creation serialization support, Classes That Support Serialization clip rectangle, Canvas Drawing ClipDrawable class, Drawables Cloneable interface, The Object Class and Its Methods code signing, Application Signing (see application signing) Collection interface, Collection interface types Collections Library, Collection interface types, Java generics ColorFilter class, Shadows, Gradients, and Filters com.android.ide.eclipse.adt plug-in, Plug-ins com.android.ide.eclipse.ddms plug-in, Plug-ins Comparable interface, Interfaces, Interfaces about, Interfaces compareTo method, Interfaces composition, defined, Using polymorphism and composition, Using polymorphism and composition compound queries, Additional Database Concepts concurrent programming, Basic Multithreaded Concurrent Programming in Java, Concurrency in Android, Concurrency in Android, AsyncTask and the UI Thread, AsyncTask and the UI Thread, Threads in an Android Process Android libraries and, Concurrency in Android AsyncTask and UI thread, AsyncTask and the UI Thread, AsyncTask and the UI Thread multi-threaded, Basic Multithreaded Concurrent Programming in Java, Concurrency in Android threads in Android processes, Threads in an Android Process constructors, Object Creation, Creating a Fragment defined, Object Creation Fragment class and, Creating a Fragment contact data, Account Contacts, Account Contacts, Authentication, Authentication, Synchronization about, Account Contacts, Account Contacts authenticating, Authentication, Authentication synchronizing, Synchronization Contacts class, Account Contacts, Account Contacts additional information, Account Contacts querying, Account Contacts ContactsContract content provider, Account Contacts, Account Contacts container views, Assembling a Graphical Interface, Layout content assist, Editing Java Code and Code Completion content providers, Content Providers, Content Providers, Using a content provider, Using a content provider, Content providers and the Internet, Organizing Java Source, Understanding Content Providers, Declaring Column Specification Strings, Implementing a Content Provider, Implementing a Content Provider, Defining the CONTENT_URI, Defining the CONTENT_URI, Writing and Integrating a Content Provider, File Management and Binary Data, File Management and Binary Data, Android MVC and Content Observation, Android MVC and Content Observation, A Complete Content Provider: The SimpleFinchVideoContentProvider Code, Determining How Often to Notify Observers, Declaring Your Content Provider, Exploring Content Providers, Developing RESTful Android Applications, A “Network MVC”, Summary of Benefits, Code Example: Dynamically Listing and Caching YouTube Video Content, File Management: Storing Thumbnails, Audio and Video, Stored Media Content, Account Contacts, Account Contacts about, Content Providers, Content Providers activities and, Organizing Java Source binary data, File Management and Binary Data building, Understanding Content Providers, Declaring Column Specification Strings ContactsContract, Account Contacts, Account Contacts CONTENT_URI constant, Implementing a Content Provider, Defining the CONTENT_URI, Defining the CONTENT_URI declaring, Declaring Your Content Provider developing RESTful applications, Developing RESTful Android Applications file management, File Management and Binary Data implementing, Implementing a Content Provider MediaStore, Audio and Video, Stored Media Content MVC architecture and, Content providers and the Internet, Android MVC and Content Observation, Android MVC and Content Observation network MVC and, A “Network MVC”, Summary of Benefits REST and, Exploring Content Providers SimpleFinchVideoContentProvider example, A Complete Content Provider: The SimpleFinchVideoContentProvider Code, Determining How Often to Notify Observers usage considerations, Using a content provider, Using a content provider writing/integrating, Writing and Integrating a Content Provider YouTube video example, Code Example: Dynamically Listing and Caching YouTube Video Content, File Management: Storing Thumbnails content:// URI, File Management and Binary Data ContentObserver.onChange method, Android MVC and Content Observation ContentProvider class, Other Android Components, Content Providers, Content Providers, Content Providers, Content Providers, Content Providers, Application Manifests, Serialization, The Activity Class and Well-Behaved Applications, Implementing a Content Provider, Defining the CONTENT_URI, Defining the CONTENT_URI, Defining the CONTENT_URI, Defining the CONTENT_URI, Extending ContentProvider, Extending ContentProvider, Extending ContentProvider, Extending ContentProvider, Extending ContentProvider, Extending ContentProvider, Extending ContentProvider, Extending ContentProvider, Extending ContentProvider, File Management and Binary Data, Implementing the onCreate Method, Implementing the getType Method, Implementing the Provider API, Implementing the Provider API, Implementing the Provider API, Implementing the Provider API, Developing RESTful Android Applications, File Management: Storing Thumbnails about, Other Android Components, Content Providers delete method, Content Providers, Defining the CONTENT_URI, Extending ContentProvider, Implementing the Provider API extending, Implementing a Content Provider, Extending ContentProvider, Extending ContentProvider getType method, Extending ContentProvider, Implementing the getType Method insert method, Content Providers, Defining the CONTENT_URI, Extending ContentProvider, Extending ContentProvider, Implementing the Provider API manifest files and, Application Manifests onCreate method, Extending ContentProvider, Implementing the onCreate Method openFile method, File Management: Storing Thumbnails openStream method, File Management and Binary Data query method, Content Providers, Defining the CONTENT_URI, Extending ContentProvider, Implementing the Provider API RESTful applications and, Developing RESTful Android Applications serialization and, Serialization update method, Content Providers, Defining the CONTENT_URI, Extending ContentProvider, Implementing the Provider API well-behaved applications and, The Activity Class and Well-Behaved Applications ContentProviderOperation class, Account Contacts, Account Contacts about, Account Contacts newInsert method, Account Contacts ContentProviderOperation.Builder class, Account Contacts ContentResolver class, Using a content provider, Content providers and the Internet, Content providers and the Internet, File Management and Binary Data, File Management and Binary Data, Android MVC and Content Observation, Android MVC and Content Observation, A “Network MVC”, File Management: Storing Thumbnails about, Using a content provider delete method, Android MVC and Content Observation insert method, A “Network MVC” notifyChange method, Content providers and the Internet, Android MVC and Content Observation openInputStream method, File Management and Binary Data, File Management: Storing Thumbnails openOutputStream method, File Management and Binary Data registerContentObserver method, Content providers and the Internet ContentUris.withAppendedId method, The insert method ContentValues class, Using the insert method, YouTubeHandler, Stored Media Content about, Using the insert method creating, YouTubeHandler stored media content, Stored Media Content Context class, Static Application Resources and Context, Resources, Connecting to a Location Provider and Getting Location Updates, Sensors, Accessibility about, Static Application Resources and Context getResources method, Resources getSystemService method, Connecting to a Location Provider and Getting Location Updates, Sensors, Accessibility ContextMenu class, The Menu contextual menus, The Menu Controller component (MVC), The Controller, Wiring Up the Controller, Wiring Up the Controller, Listening to the Model, Listening to the Model, Listening for Touch Events, Listening for Touch Events, Listening for Key Events, Advanced Wiring: Focus and Threading, Advanced Wiring: Focus and Threading about, The Controller focus and threading, Advanced Wiring: Focus and Threading, Advanced Wiring: Focus and Threading listening for key events, Listening for Key Events listening for touch events, Listening for Touch Events, Listening for Touch Events listening to the Model, Listening to the Model, Listening to the Model wiring up, Wiring Up the Controller, Wiring Up the Controller cpufeatures module, Android-Provided Native Libraries Create New Android Virtual Device (AVD) dialog, Making an Android Virtual Device (AVD) CREATE TABLE statement (SQL), SQL Data Definition Commands CRUD methodology, Inserting data into the database Ctrl-F11, Fragment Life Cycle Ctrl-space bar, Editing Java Code and Code Completion curly braces {}, Final and Static Declarations Currency class, The Android Libraries Cursor interface, Using a content provider, The Android Database Classes, The Android Database Classes, The Android Database Classes, Database Queries and Reading Data from the Database, Android MVC and Content Observation, The query method, A “Network MVC”, Account Contacts, Account Contacts about, Using a content provider, The Android Database Classes account contacts example, Account Contacts, Account Contacts moveToFirst method, Database Queries and Reading Data from the Database moveToNext method, The Android Database Classes moveToPrevious method, The Android Database Classes registerContentObserver method, Android MVC and Content Observation requery method, A “Network MVC” setNotificationUri method, The query method CycleInterpolator class, Transition animation D D-pads, Listening for Touch Events, Advanced Wiring: Focus and Threading Dalvik Debug Monitor Server, The Dalvik Debug Monitor Server (DDMS) (see DDMS) dalvik package tree, The Android Libraries Dalvik virtual machines (VMs), The Dalvik Debug Monitor Server (DDMS), The Dalvik VM, Zygote: Forking a New Process about, The Dalvik Debug Monitor Server (DDMS) Android runtime environment, The Dalvik VM Zygote process and, Zygote: Forking a New Process data structures, synchronization and, Synchronization and Data Structures data types, Primitive Types, SQLite types, Communication, Identity, Sync, and Social Media, Account Contacts, Conventions on the Native Method Side contact data, Communication, Identity, Sync, and Social Media, Account Contacts Java supported, Primitive Types JNI calls and, Conventions on the Native Method Side SQLite supported, SQLite types database schemas, SQL Data Definition Commands, Database constraints defined, SQL Data Definition Commands foreign key constraints, Database constraints database triggers, Additional Database Concepts databases, Relational Database Overview (see relational databases) Date class, The Android Libraries DatePicker class, Rolling Your Own Widgets DateTime class, Creating a Fragment, Fragment Life Cycle, Fragment Transactions DDMS (Dalvik Debug Monitor Server), The Dalvik Debug Monitor Server (DDMS), The Dalvik Debug Monitor Server (DDMS), The DDMS, Using DDMS to update location, Using DDMS to update location, Conventions on the Native Method Side about, The Dalvik Debug Monitor Server (DDMS), The Dalvik Debug Monitor Server (DDMS), The DDMS Emulator Control pane, Using DDMS to update location JNI conventions, Conventions on the Native Method Side location updates, Using DDMS to update location debug certificate, Debug certificates debuggable attribute, Using a self-signed certificate to sign an application debugging, Running a Program on an Android Device, Running and debugging Android apps Android applications, Running and debugging Android apps Android devices, Running a Program on an Android Device DecelerateInterpolator class, Transition animation default constructors, Object Creation DELETE operation (REST), Content Providers DELETE statement (SQL), Extending ContentProvider dependency injection, Overrides and callbacks deserializing data, Serialization Design for Extension coding rule, Extending Android classes developing applications, Modular Programming in Java (see application development) .dex files, Builders and Artifacts Dictionary class, The Java Collections Framework, The Android Libraries distributing applications, Getting Your Application into Users’ Hands (see application distribution) double type, Primitive Types, Conventions on the Native Method Side Draw9patch drawing program, Draw9patch Drawable class, Canvas Drawing, Drawables, Drawables, Drawables, Drawables, Background animation about, Canvas Drawing, Drawables, Drawables Callback interface, Background animation usage considerations, Drawables wrappers supporting, Drawables drawable directory, Resources drawing graphics, Rolling Your Own Widgets, Layout, Arrangement, Canvas Drawing, Matrix transformations, Drawables, Drawables, Bitmaps, Bling, OpenGL Graphics, Bling, Shadows, Gradients, and Filters, Animation, Surface view animation, OpenGL Graphics, OpenGL Graphics animations, Animation, Surface view animation Bitmap class support, Bitmaps Canvas class support, Canvas Drawing, Matrix transformations Drawable class support, Drawables, Drawables graphics effects examples, Bling, OpenGL Graphics layout considerations, Layout, Arrangement OpenGL support, Bling, OpenGL Graphics, OpenGL Graphics rolling your own widgets, Rolling Your Own Widgets shadows, gradients, filters, Shadows, Gradients, and Filters DROP TABLE statement (SQL), SQL Data Definition Commands dynamic declarations, Final and Static Declarations E Eclipse IDE, The Eclipse Integrated Development Environment (IDE), The Eclipse Integrated Development Environment (IDE), The Eclipse Integrated Development Environment (IDE), The Eclipse Integrated Development Environment (IDE), The Eclipse Integrated Development Environment (IDE), The Eclipse Integrated Development Environment (IDE), Adding Build Targets to the SDK, Using the Install New Software Wizard to download and install the ADT plug-in, Keeping Eclipse and the ADT Plug-in Up-to-Date, Eclipse for Android Software Development, Eclipse Concepts and Terminology, Associations, Plug-ins, Eclipse’s Java Runtime Environment, Extensions, Extensions, Associations, Eclipse Views and Perspectives, The Problems View, The Package Explorer View, The Task List View, The Outline View, The Problems View, Java Coding in Eclipse, Refactoring, Eclipse and Android, Eclipse and Android, Eclipse and Android, Eclipse and Android, Eclipse and Android, Eclipse and Android, Static Analyzers, Limitations of Static Analysis, Eclipse Idiosyncrasies and Alternatives, Eclipse Idiosyncrasies and Alternatives, Visualizing the Activity Life Cycle, Visualizing the Activity Life Cycle (see also ADT Eclipse plug-in) about, The Eclipse Integrated Development Environment (IDE) additional information, Using the Install New Software Wizard to download and install the ADT plug-in, Eclipse for Android Software Development concepts and terminology, Eclipse Concepts and Terminology, Associations confirming installation, The Eclipse Integrated Development Environment (IDE) downloading, The Eclipse Integrated Development Environment (IDE) Extensions view, Extensions File Explorer view, Eclipse and Android Heap view, Eclipse and Android idiosyncrasies and alternatives, Eclipse Idiosyncrasies and Alternatives, Eclipse Idiosyncrasies and Alternatives installing, The Eclipse Integrated Development Environment (IDE) Java coding in, Java Coding in Eclipse, Refactoring JRE requirements, The Eclipse Integrated Development Environment (IDE), Eclipse’s Java Runtime Environment keeping up-to-date, Keeping Eclipse and the ADT Plug-in Up-to-Date Layout view, Eclipse and Android LogCat view, Eclipse and Android, Visualizing the Activity Life Cycle, Visualizing the Activity Life Cycle Outline view, The Outline View Package Explorer view, Associations, The Package Explorer View Pixel Perfect view, Eclipse and Android Plug-ins view, Plug-ins, Extensions Problems view, The Problems View SDK and AVD Manager support, Adding Build Targets to the SDK static analyzers, Static Analyzers, Limitations of Static Analysis Task List view, The Task List View Threads view, Eclipse and Android views and perspectives, Eclipse Views and Perspectives, The Problems View eclipse.ini file, Eclipse’s Java Runtime Environment EditText class, Overrides and callbacks, Wiring Up the Controller, Alternative Ways to Handle Events addTextChangedListener method, Overrides and callbacks handling events, Alternative Ways to Handle Events invalidate method, Wiring Up the Controller encapsulation, Access Modifiers and Encapsulation, Encapsulation, Getters and setters about, Encapsulation access modifiers and, Access Modifiers and Encapsulation getter and setter methods, Getters and setters encryption, public key, Public Key Encryption and Cryptographic Signing, Public Key Encryption and Cryptographic Signing Enumeration interface, The Java Collections Framework, The Android Libraries Equinox framework, Eclipse Concepts and Terminology event queues, The Controller events, Listening for Touch Events, Listening for Touch Events, Listening for Key Events, Alternative Ways to Handle Events alternative ways to handle, Alternative Ways to Handle Events listening for key events, Listening for Key Events listening for touch events, Listening for Touch Events, Listening for Touch Events Exception class, Exceptions exceptions, Exceptions, Exceptions, Exceptions (see also specific exceptions) .exit command (SQLite), Example Database Manipulation Using sqlite3 exporting Android applications, Using a self-signed certificate to sign an application extends keyword, Objects, Inheritance, and Polymorphism extensions, defined, Extensions, Extensions external sensors, Sensors (see sensors) Eyes-Free open source project, Accessibility F File Explorer view (Eclipse), Eclipse and Android file management, File Management and Binary Data, File Management: Storing Thumbnails FileHandler class, File Management: Storing Thumbnails filters (drawing graphics), Shadows, Gradients, and Filters final declarations, Final and Static Declarations final keyword, Final and Static Declarations FindBugs tool, Type Safety in Java, FindBugs, FindBugs, Applying Static Analysis to Android Code, Applying Static Analysis to Android Code about, FindBugs, FindBugs applying static analysis, Applying Static Analysis to Android Code, Applying Static Analysis to Android Code type safety in Java, Type Safety in Java float type, Primitive Types, Conventions on the Native Method Side focusable attribute, Advanced Wiring: Focus and Threading FOREIGN KEY constraint, Database constraints forking processes, Zygote: Forking a New Process Fragment class, Fragments and Multiplatform Support, Creating a Fragment, Creating a Fragment, Creating a Fragment, Fragment Life Cycle, Fragment Life Cycle, Fragment Life Cycle, Fragment Transactions, Fragment Transactions, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle about, Fragments and Multiplatform Support creating fragments, Creating a Fragment getArguments method, Fragment Transactions onActivityCreated method, Visualizing the Fragment Life Cycle onAttach method, Visualizing the Fragment Life Cycle onCreate method, Creating a Fragment, Fragment Life Cycle, Visualizing the Fragment Life Cycle onCreateView method, Creating a Fragment, Visualizing the Fragment Life Cycle onPause method, Fragment Life Cycle, Visualizing the Fragment Life Cycle onResume method, Visualizing the Fragment Life Cycle onSaveInstanceState method, Fragment Life Cycle, Visualizing the Fragment Life Cycle onStart method, Visualizing the Fragment Life Cycle onStop method, Visualizing the Fragment Life Cycle setArguments method, Fragment Transactions visualizing life cycles, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle FragmentManager class, The Fragment Manager, The Fragment Manager about, The Fragment Manager findFragmentByTag method, The Fragment Manager fragments, Fragments and Multiplatform Support, Creating a Fragment, Creating a Fragment, Fragment Life Cycle, Fragment Life Cycle, The Fragment Manager, Fragment Transactions, Fragment Transactions, The Compatibility Package, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle about, Fragments and Multiplatform Support Android Compatibility Package and, The Compatibility Package creating, Creating a Fragment, Creating a Fragment life cycles of, Fragment Life Cycle, Fragment Life Cycle, Visualizing the Fragment Life Cycle, Visualizing the Fragment Life Cycle manipulating, The Fragment Manager transactions involving, Fragment Transactions, Fragment Transactions frame-by-frame animation, Background animation, Background animation FrameLayout class, The Fragment Manager, Gesture Input framework applications, A Framework for a Well-Behaved Application (see skeleton applications) G garbage collection, Garbage Collection gen directory, Organizing Java Source generics, Java generics Gennick, Jonathan, Additional Database Concepts geo utility, Using geo to update location Gesture class, Gesture Input gesture input, Listening for Touch Events, Listening for Touch Events, Gesture Input about, Gesture Input listening for, Listening for Touch Events, Listening for Touch Events GestureLibraries class, Gesture Input, Gesture Input about, Gesture Input fromRawResource method, Gesture Input GestureLibrary class, Gesture Input GestureOverlayView class, Gesture Input, Gesture Input about, Gesture Input OnGesturePerformedListener interface, Gesture Input GesturePoint class, Gesture Input GestureStore class, Gesture Input GestureStroke class, Gesture Input GET operation (REST), Content Providers getter methods, Getters and setters Global Positioning System (GPS), Location-Based Services, The Manifest and Layout Files, Using geo to update location GLSurfaceView class, OpenGL Graphics, OpenGL Graphics, OpenGL Graphics, OpenGL Graphics about, OpenGL Graphics Renderer interface, OpenGL Graphics sizeChanged method, OpenGL Graphics surfaceCreated method, OpenGL Graphics Goetz, Brian, Thread Control with wait() and notify() Methods Google Checkout, Becoming an Official Android Developer, Getting Paid Google Earth, Using DDMS to update location Google I/O conference, Developing RESTful Android Applications Google Maps, Google Maps API Keys, Mapping, The Google Maps Activity, The MapView and MapActivity about, Mapping API keys, Google Maps API Keys MapView class and, The MapView and MapActivity starting, The Google Maps Activity GPS (Global Positioning System), Location-Based Services, The Manifest and Layout Files, Using geo to update location GPU (Graphics Processing Unit), OpenGL Graphics gradients (drawing graphics), Shadows, Gradients, and Filters Graphics Processing Unit (GPU), OpenGL Graphics graphics, drawing, Rolling Your Own Widgets (see drawing graphics) gravity, Gravity GUI framework, Drawing 2D and 3D Graphics (see Android GUI framework) gyroscopes, Gyroscope H Handler class, Threads in an Android Process, Advanced Wiring: Focus and Threading about, Advanced Wiring: Focus and Threading Looper class and, Threads in an Android Process HashMap class, Collection implementation types, The Android Libraries, Using the insert method, The SimpleFinchVideoContentProvider Class and Instance Variables about, Collection implementation types Android libraries and, The Android Libraries ContentProvider class and, The SimpleFinchVideoContentProvider Class and Instance Variables ContentValues class and, Using the insert method HashSet class, Collection implementation types Hashtable class, The Java Collections Framework, The Android Libraries hcidump utility, Using Bluetooth in Android Applications Heap view (Eclipse), Eclipse and Android .help command (SQLite), Example Database Manipulation Using sqlite3 Hibernate framework, Serialization Hierarchy Viewer tool, Hierarchy Viewer HttpEntity interface, RESTfulContentProvider: A REST helper I ia32-libs package, The Android SDK iBATIS framework, Serialization IllegalStateException, AsyncTask and the UI Thread, Fragment Transactions, Measurement inheritance, Objects and Classes, Objects, Inheritance, and Polymorphism, Interfaces interfaces and, Interfaces Java support, Objects, Inheritance, and Polymorphism Java types and, Objects and Classes inner joins, Additional Database Concepts InputStream class, Bluetooth-specific protocols and adopted protocols INSERT statement (SQL), SQL Data Manipulation Commands, Inserting data into the database Install New Software Wizard, Using the Install New Software Wizard to download and install the ADT plug-in instance variables, The SimpleFinchVideoContentProvider Class and Instance Variables, The SimpleFinchVideoContentProvider Class and Instance Variables int type, Primitive Types, Conventions on the Native Method Side INTEGER type (SQLite), SQLite types, Declaring Column Specification Strings IntelliJ IDEA, Installing the Android SDK and Prerequisites Intent class, Activities, Intents, and Tasks, Launch mode, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags, Intent audio recording, Intent video recording, The Google Maps Activity about, Activities, Intents, and Tasks android:launchMode attribute and, Launch mode audio recording, Intent audio recording FLAG_ACTIVITY_BROUGHT_TO_FRONT constant, Modifying task behavior with intent flags FLAG_ACTIVITY_CLEAR_TASK constant, Modifying task behavior with intent flags FLAG_ACTIVITY_CLEAR_TOP constant, Modifying task behavior with intent flags FLAG_ACTIVITY_CLEAR_WHEN_TASK_RESET constant, Modifying task behavior with intent flags FLAG_ACTIVITY_EXCLUDE_FROM_RECENTS constant, Modifying task behavior with intent flags FLAG_ACTIVITY_FORWARD_RESULT constant, Modifying task behavior with intent flags FLAG_ACTIVITY_LAUNCHED_FROM_HISTORY constant, Modifying task behavior with intent flags FLAG_ACTIVITY_MULTIPLE_TASK constant, Modifying task behavior with intent flags FLAG_ACTIVITY_NEW_TASK constant, Modifying task behavior with intent flags FLAG_ACTIVITY_NO_ANIMATION constant, Modifying task behavior with intent flags FLAG_ACTIVITY_NO_HISTORY constant, Modifying task behavior with intent flags FLAG_ACTIVITY_NO_USER_ACTION constant, Modifying task behavior with intent flags FLAG_ACTIVITY_PREVIOUS_IS_TOP constant, Modifying task behavior with intent flags FLAG_ACTIVITY_REORDER_TO_FRONT constant, Modifying task behavior with intent flags setting flags, Modifying task behavior with intent flags, Modifying task behavior with intent flags starting Google Maps, The Google Maps Activity video recording, Intent video recording interfaces, Interfaces, Interfaces, Interfaces, Interfaces (see also specific interfaces) about, Interfaces, Interfaces additional information, Interfaces Interpolator class, Transition animation IOException, Writing to a Tag ISO (International Organization for Standardization), Relational Database Overview Iterator interface, Collection interface types, The Android Libraries, The Android Libraries J Java Collections Framework, The Java Collections Framework, Collection interface types, The Android Libraries about, The Java Collections Framework collection interface types, Collection interface types java.util package and, The Android Libraries Java Collections Library, The Android Libraries Java compiler, The Java compiler, Organizing Java Source Java Cryptography Architecture, Debug certificates Java Development Kit, The Java Development Kit (JDK) (see JDK) .java files, organizing, Organizing Java Source, Organizing Java Source Java language, Java for Android, The Java Type System, Primitive Types, Objects and Classes, Object Creation, Object Creation, The Object Class and Its Methods, The Object Class and Its Methods, Objects, Inheritance, and Polymorphism, Objects, Inheritance, and Polymorphism, Objects, Inheritance, and Polymorphism, Final and Static Declarations, Final and Static Declarations, Final and Static Declarations, Abstract Classes, Interfaces, Interfaces, Exceptions, Exceptions, Java generics, Garbage Collection, Type Safety in Java, Getters and setters, Using Anonymous Classes, Using Anonymous Classes, Modular Programming in Java, Modular Programming in Java, Basic Multithreaded Concurrent Programming in Java, Synchronization and Thread Safety, Synchronization and Thread Safety, Thread Control with wait() and notify() Methods, Synchronization and Data Structures, Java Coding in Eclipse, Refactoring, Java Serialization abstract classes, Abstract Classes additional information, Java for Android anonymous classes, Using Anonymous Classes, Using Anonymous Classes coding in Eclipse, Java Coding in Eclipse, Refactoring exceptions support, Exceptions, Exceptions final and static declarations, Final and Static Declarations, Final and Static Declarations garbage collection, Garbage Collection generics, Java generics inheritance support, Objects, Inheritance, and Polymorphism interface support, Interfaces, Interfaces modular programming in, Modular Programming in Java, Modular Programming in Java multi-threaded concurrent programming, Basic Multithreaded Concurrent Programming in Java Object class and its methods, The Object Class and Its Methods, The Object Class and Its Methods object creation, Object Creation, Object Creation objects and classes, Objects and Classes passing parameters by value, Final and Static Declarations polymorphism support, Objects, Inheritance, and Polymorphism, Objects, Inheritance, and Polymorphism primitive types, Primitive Types serialization support, Java Serialization synchronization and data structures, Synchronization and Data Structures synchronization and thread safety, Synchronization and Thread Safety, Synchronization and Thread Safety thread control, Thread Control with wait() and notify() Methods type system, The Java Type System, Type Safety in Java, Getters and setters Java Native Interface, The Android Native Development Kit (NDK) (see JNI) Java packages, Java Packages, Java Packages, The Android Libraries, The Android Libraries (see also specific packages) about, The Android Libraries namespaces and, Java Packages scope and, Java Packages Java Runtime Environment (JRE), The Java Development Kit (JDK), The Eclipse Integrated Development Environment (IDE), Eclipse’s Java Runtime Environment about, The Java Development Kit (JDK) Eclipse requirements, The Eclipse Integrated Development Environment (IDE), Eclipse’s Java Runtime Environment Java Virtual Machine (JVM), The Dalvik Debug Monitor Server (DDMS), Traditional Programming Models Compared to Android DDMS support, The Dalvik Debug Monitor Server (DDMS) process overview, Traditional Programming Models Compared to Android java.awt package, The Android Libraries java.io package, Bluetooth-specific protocols and adopted protocols java.lang package, Java Packages, The Android Libraries java.rmi package, The Android Libraries java.util package, The Java Collections Framework, Java Packages, The Android Libraries java.util.concurrent package, Thread Control with wait() and notify() Methods javac command, The Java Development Kit (JDK) javax package, The Android Libraries javax.sound package, The Android Libraries javax.swing package, The Android Libraries JDK (Java Development Kit), The Java Development Kit (JDK), The Java Development Kit (JDK), The Java Development Kit (JDK), Keeping the JDK Up-to-Date confirming installation, The Java Development Kit (JDK) downloading, The Java Development Kit (JDK) installing, The Java Development Kit (JDK) keeping up-to-date, Keeping the JDK Up-to-Date JNI (Java Native Interface), The Android Native Development Kit (NDK), The Android Native Development Kit (NDK), Native Methods and JNI Calls, JNI, NDK, and SDK: A Sample App, JNI, NDK, and SDK: A Sample App about, The Android Native Development Kit (NDK) additional information, The Android Native Development Kit (NDK) conventions for method calls, Native Methods and JNI Calls sample application, JNI, NDK, and SDK: A Sample App, JNI, NDK, and SDK: A Sample App JRE (Java Runtime Environment), The Java Development Kit (JDK), The Eclipse Integrated Development Environment (IDE), Eclipse’s Java Runtime Environment about, The Java Development Kit (JDK) Eclipse requirements, The Eclipse Integrated Development Environment (IDE), Eclipse’s Java Runtime Environment JVM (Java Virtual Machine), The Dalvik Debug Monitor Server (DDMS), Traditional Programming Models Compared to Android DDMS support, The Dalvik Debug Monitor Server (DDMS) process overview, Traditional Programming Models Compared to Android K keycodes, KeyEvent class, Controlling the Map with the Keypad KeyEvent class, Listening for Key Events, Alternative Ways to Handle Events, Advanced Wiring: Focus and Threading, Controlling the Map with the Keypad focus and threading, Advanced Wiring: Focus and Threading getRepeatCount method, Listening for Key Events handling events, Alternative Ways to Handle Events keycodes, Controlling the Map with the Keypad KeyHandler.handleKey method, Using Anonymous Classes keystore, Debug certificates, Creating a self-signed certificate, Using a self-signed certificate to sign an application about, Debug certificates, Using a self-signed certificate to sign an application remembering password, Creating a self-signed certificate keystrokes, Listening for Key Events, Controlling the Map with the Keypad controlling map with, Controlling the Map with the Keypad listening for, Listening for Key Events keytool command, keytool, Debug certificates, Creating a self-signed certificate, Google Maps API Keys about, keytool creating private keys, Creating a self-signed certificate list option, Debug certificates, Google Maps API Keys L layout directory, Resources layout process, Layout, Layout, Measurement, Measurement, Arrangement about, Layout, Layout arrangement phase, Arrangement measurement phase, Measurement, Measurement Layout view (Eclipse), Eclipse and Android Layoutopt static analyzer, Layoutopt LBS (location-based services), Location and Mapping, Location-Based Services, Location-Based Services, Location-Based Services about, Location and Mapping Cell ID, Location-Based Services GPS, Location-Based Services triangulation, Location-Based Services libraries, Android, The Android Libraries (see Android libraries) life cycles, Component Life Cycles, The Activity Life Cycle, Serialization, Serialization and the Application Life Cycle, Creating a Fragment, Fragment Life Cycle, Fragment Life Cycle, Visualizing the Activity Life Cycle, Minor life cycle methods of the Activity class, Memory recovery and life cycles, Memory recovery and life cycles, Configuration changes and the activity life cycle, Configuration changes and the activity life cycle, Visualizing the Fragment Life Cycle, The Activity Class and Well-Behaved Applications, The Activity Life Cycle and the User Experience, The Activity Life Cycle and the User Experience, Life Cycle Methods of the Application Class, Life Cycle Methods of the Application Class Activity class and, Visualizing the Activity Life Cycle, Minor life cycle methods of the Activity class Android components, Component Life Cycles, The Activity Life Cycle Application class and, Life Cycle Methods of the Application Class, Life Cycle Methods of the Application Class configuration changes and, Configuration changes and the activity life cycle, Configuration changes and the activity life cycle fragment, Creating a Fragment, Fragment Life Cycle, Fragment Life Cycle Fragment class and, Visualizing the Fragment Life Cycle managing, Serialization memory recovery and, Memory recovery and life cycles, Memory recovery and life cycles serialization and, Serialization and the Application Life Cycle user experience and, The Activity Life Cycle and the User Experience well-behaved applications and, The Activity Class and Well-Behaved Applications, The Activity Life Cycle and the User Experience light sensors, Other Sensors LIKE keyword, Example Database Manipulation Using sqlite3 linear acceleration, Linear acceleration LinearGradient class, Shadows, Gradients, and Filters LinearInterpolator class, Transition animation LinearLayout class, Assembling a Graphical Interface, Fragments and Multiplatform Support, Layout, Measurement, Measurement, Measurement about, Assembling a Graphical Interface, Fragments and Multiplatform Support, Layout measurement process, Measurement onMeasure method, Measurement setGravity method, Measurement LinkedList class, Collection implementation types Linux environment, The Java Development Kit (JDK), The Eclipse Integrated Development Environment (IDE), The Android SDK, The Android SDK, Running a Program on an Android Device, Sandboxing: Processes and Users, Bluez: The Linux Bluetooth Implementation, Using Bluetooth in Android Applications, Setting Up the NDK Environment Bluetooth implementation, Bluez: The Linux Bluetooth Implementation hcidump utility, Using Bluetooth in Android Applications installing Android SDK, The Android SDK, The Android SDK installing Eclipse, The Eclipse Integrated Development Environment (IDE) installing JDK, The Java Development Kit (JDK) NDK requirements, Setting Up the NDK Environment running programs on Android devices, Running a Program on an Android Device sandboxing and, Sandboxing: Processes and Users List interface, Collection interface types, The Android Libraries ListView class, Fragments and Multiplatform Support, Android MVC and Content Observation, Android MVC and Content Observation, Account Contacts about, Fragments and Multiplatform Support account contacts example, Account Contacts notifications and, Android MVC and Content Observation setAdapter method, Android MVC and Content Observation location and mapping, Location and Mapping, Location-Based Services, Mapping, Mapping, The MapView and MapActivity, The MapView and MapActivity, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, Pausing and Resuming a MapActivity, Controlling the Map with Menu Buttons, Controlling the Map with Menu Buttons, Controlling the Map with the Keypad, Location Without Maps, Using DDMS to update location about, Mapping accessing without maps, Location Without Maps, Using DDMS to update location controlling with keypad, Controlling the Map with the Keypad controlling with menu buttons, Controlling the Map with Menu Buttons, Controlling the Map with Menu Buttons Google Maps, Mapping location-based services, Location-Based Services MapActivity class, The MapView and MapActivity, Pausing and Resuming a MapActivity MapView class, The MapView and MapActivity, MapView and MyLocationOverlay Initialization mobile phones and, Location and Mapping MyLocationOverlay class, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization zooming in, MapView and MyLocationOverlay Initialization location-based services, Location and Mapping (see LBS) LocationListener interface, Connecting to a Location Provider and Getting Location Updates LocationManager class, Connecting to a Location Provider and Getting Location Updates, Connecting to a Location Provider and Getting Location Updates getLastKnownLocation method, Connecting to a Location Provider and Getting Location Updates requestLocationUpdates method, Connecting to a Location Provider and Getting Location Updates LocationProvider class, Connecting to a Location Provider and Getting Location Updates, Using geo to update location LogCat view (Eclipse), Eclipse and Android, Visualizing the Activity Life Cycle, Visualizing the Activity Life Cycle long type, Primitive Types, Conventions on the Native Method Side Looper class, Threads in an Android Process ls command, The SQL Language M Macintosh environment, The Java Development Kit (JDK), The Android SDK, Running a Program on an Android Device, Setting Up the NDK Environment installing Android SDK, The Android SDK installing JDK, The Java Development Kit (JDK) NDK requirements, Setting Up the NDK Environment running programs on Android devices, Running a Program on an Android Device magnetic sensors, Other Sensors manifest files, The Android Manifest Editor, Application Manifests, Initialization Parameters in AndroidManifest.xml, The Manifest and Layout Files, Authentication about, The Android Manifest Editor AndroidManifest.xml, Application Manifests, Initialization Parameters in AndroidManifest.xml authentication example, Authentication location without maps example, The Manifest and Layout Files Map interface, Collection interface types, The Android Libraries MapActivity class, Assembling a Graphical Interface, The MapView and MapActivity, MapView and MyLocationOverlay Initialization, Pausing and Resuming a MapActivity, Pausing and Resuming a MapActivity about, The MapView and MapActivity graphical interfaces and, Assembling a Graphical Interface isRouteDisplayed method, MapView and MyLocationOverlay Initialization onPause method, Pausing and Resuming a MapActivity onResume method, Pausing and Resuming a MapActivity MapController class, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization about, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization setZoom method, MapView and MyLocationOverlay Initialization zoomIn method, MapView and MyLocationOverlay Initialization zoomInFixing method, MapView and MyLocationOverlay Initialization zoomOut method, MapView and MyLocationOverlay Initialization zoomToSpan method, MapView and MyLocationOverlay Initialization mapping, Location and Mapping (see location and mapping) MapView class, The MapView and MapActivity, The MapView and MapActivity, Working with MapViews, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization about, The MapView and MapActivity initializing, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization prerequisites, The MapView and MapActivity setClickable attribute, MapView and MyLocationOverlay Initialization setEnabled attribute, MapView and MyLocationOverlay Initialization setSatellite attribute, MapView and MyLocationOverlay Initialization setStreetView attribute, MapView and MyLocationOverlay Initialization setTraffic attribute, MapView and MyLocationOverlay Initialization usage suggestions, Working with MapViews marshaling data, Serialization MaskFilter class, Shadows, Gradients, and Filters Matrix class, Matrix transformations, Transition animation, Transition animation Canvas class and, Matrix transformations postTranslate method, Transition animation preTranslate method, Transition animation MeasureSpec class, Measurement, Measurement, Measurement, Measurement, Measurement AT_MOST constant, Measurement EXACTLY constant, Measurement getMode method, Measurement getSize method, Measurement UNSPECIFIED constant, Measurement Media Store content provider, Audio and Video MediaPlayer class, Playing Audio and Video, Playing Audio and Video, Playing Audio and Video, Playing Audio and Video, Playing Audio and Video, Playing Audio and Video, Playing Audio and Video, Playing Audio and Video, MediaPlayer audio playback, MediaPlayer audio playback, MediaPlayer audio playback, MediaPlayer audio playback, MediaPlayer audio playback, MediaPlayer audio playback, Video Playback additional information, Playing Audio and Video audio playback, MediaPlayer audio playback create method, Playing Audio and Video getCurrentPosition method, MediaPlayer audio playback life cycle states, Playing Audio and Video pause method, Playing Audio and Video prepare method, Playing Audio and Video, MediaPlayer audio playback release method, Playing Audio and Video reset method, MediaPlayer audio playback setDataSource method, MediaPlayer audio playback start method, Playing Audio and Video, MediaPlayer audio playback stop method, Playing Audio and Video video playback, Video Playback MediaRecorder class, Recording Audio and Video, Recording Audio and Video, MediaRecorder audio recording, MediaRecorder audio recording, MediaRecorder audio recording, MediaRecorder audio recording, MediaRecorder audio recording, MediaRecorder audio recording, MediaRecorder audio recording, MediaRecorder video recording audio recording, MediaRecorder audio recording, MediaRecorder audio recording life cycle states, Recording Audio and Video permissions supported, Recording Audio and Video prepare method, MediaRecorder audio recording release method, MediaRecorder audio recording reset method, MediaRecorder audio recording start method, MediaRecorder audio recording stop method, MediaRecorder audio recording video recording, MediaRecorder video recording MediaStore content provider, Stored Media Content memory recovery and life cycles, Memory recovery and life cycles, Memory recovery and life cycles Menu interface, Controlling the Map with Menu Buttons, Controlling the Map with Menu Buttons add method, Controlling the Map with Menu Buttons NONE constant, Controlling the Map with Menu Buttons MenuItem interface, Controlling the Map with Menu Buttons menus, The Menu, The Menu, Controlling the Map with Menu Buttons, Controlling the Map with Menu Buttons, Controlling the Map with Menu Buttons controlling maps with, Controlling the Map with Menu Buttons, Controlling the Map with Menu Buttons implementing, The Menu, The Menu types of, Controlling the Map with Menu Buttons merchant accounts, Getting Paid methods, Object Creation, Final and Static Declarations, Final and Static Declarations, Exceptions, Getters and setters, Native Methods and JNI Calls cascading, Object Creation final and static declarations, Final and Static Declarations, Final and Static Declarations getters and setters, Getters and setters JNI conventions, Native Methods and JNI Calls throwing exceptions, Exceptions MJAndroid sample application, Android and Social Networking, Android and Social Networking, The Source Folder (src), Loading and Starting the Application, Database Queries and Reading Data from the Database, Using the query method, Database Queries and Reading Data from the Database, Using the query method, Modifying the Database, Using the execSQL method, The MapView and MapActivity, MapView and MyLocationOverlay Initialization, The MapView and MapActivity, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, Pausing and Resuming a MapActivity, Controlling the Map with Menu Buttons, Controlling the Map with Menu Buttons, Controlling the Map with the Keypad about, Android and Social Networking, Android and Social Networking controlling map with keypad, Controlling the Map with the Keypad controlling map with menu buttons, Controlling the Map with Menu Buttons, Controlling the Map with Menu Buttons database queries, Database Queries and Reading Data from the Database, Using the query method loading and starting, Loading and Starting the Application MapActivity class, The MapView and MapActivity, Pausing and Resuming a MapActivity MapView class, The MapView and MapActivity, MapView and MyLocationOverlay Initialization modifying database, Modifying the Database, Using the execSQL method MyLocationOverlay class, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization reading data from database, Database Queries and Reading Data from the Database, Using the query method source folder, The Source Folder (src) Model component (MVC), The Model, Listening to the Model, Listening to the Model Model-View-Controller architecture, Content providers and the Internet (see MVC architecture) modular programming, Modular Programming in Java, Modular Programming in Java Monkey test automation tool, Monkey MotionEvent class, Putting It Together, Listening for Touch Events, Listening for Touch Events, Listening for Touch Events, Listening for Touch Events, Advanced Wiring: Focus and Threading, Native Activities ACTION_MOVE constant, Listening for Touch Events creating, Putting It Together focus and threading, Advanced Wiring: Focus and Threading getHistoricalX method, Listening for Touch Events getHistoricalY method, Listening for Touch Events getHistorySize method, Listening for Touch Events native activities and, Native Activities multimedia, Audio and Video, Playing Audio and Video, Recording Audio and Video, Intent video recording, Stored Media Content audio and video formats, Audio and Video playing audio and video, Playing Audio and Video recording audio and video, Recording Audio and Video, Intent video recording stored content, Stored Media Content MVC (Model-View-Controller) architecture, Content providers and the Internet, Threads in an Android Process, Android GUI Architecture, Putting It Together, The Model, The View, The Controller, Putting It Together, Putting It Together, Wiring Up the Controller, Advanced Wiring: Focus and Threading, Listening to the Model, Listening to the Model, Rolling Your Own Widgets, Canvas Drawing, SQL and the Database-Centric Data Model for Android Applications, SQL and the Database-Centric Data Model for Android Applications, SQL and the Database-Centric Data Model for Android Applications, Android MVC and Content Observation, Android MVC and Content Observation, A “Network MVC”, Summary of Benefits additional information, SQL and the Database-Centric Data Model for Android Applications Android GUI and, Android GUI Architecture, Putting It Together content providers and, Content providers and the Internet, Android MVC and Content Observation, Android MVC and Content Observation Controller component, The Controller, Wiring Up the Controller, Advanced Wiring: Focus and Threading essential design rules, Canvas Drawing Model component, The Model, Listening to the Model, Listening to the Model RESTful applications and, A “Network MVC”, Summary of Benefits SQL support, SQL and the Database-Centric Data Model for Android Applications, SQL and the Database-Centric Data Model for Android Applications threads in Android process and, Threads in an Android Process tying concepts together, Putting It Together, Putting It Together View component, The View, Rolling Your Own Widgets MyLocationOverlay class, Assembling a Graphical Interface, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization getMyLocation method, MapView and MyLocationOverlay Initialization graphical interfaces and, Assembling a Graphical Interface initializing, MapView and MyLocationOverlay Initialization, MapView and MyLocationOverlay Initialization runOnFirstFix method, MapView and MyLocationOverlay Initialization N namespaces, Java packages and, Java Packages Native Development Kit, The Android Native Development Kit (NDK) (see NDK) native keyword, Conventions on the Java Side NativeActivity class, Native Activities, Native Activities NDEF (NFC Data Exchange Format), Near Field Communication (NFC) Ndef.writeNdefMessage method, Writing to a Tag NdefMessage class, Reading a Tag, Writing to a Tag NdefRecord class, Reading a Tag, Reading a Tag, Reading a Tag, Reading a Tag, Reading a Tag, Reading a Tag, Writing to a Tag reading tags, Reading a Tag, Reading a Tag RTD_SMART_POSTER constant, Reading a Tag RTD_TEXT constant, Reading a Tag RTD_URI constant, Reading a Tag TNF_ABSOLUTE_URI constant, Reading a Tag writing tags, Writing to a Tag NDK (Native Development Kit), The Android Native Development Kit (NDK), The Android NDK, Setting Up the NDK Environment, Compiling with the NDK, JNI, NDK, and SDK: A Sample App, JNI, NDK, and SDK: A Sample App, Android-Provided Native Libraries, Building Your Own Custom Library Modules, Native Activities, Native Activities about, The Android Native Development Kit (NDK), The Android NDK building custom library modules, Building Your Own Custom Library Modules compiling with, Compiling with the NDK native activities, Native Activities, Native Activities native libraries, Android-Provided Native Libraries sample application, JNI, NDK, and SDK: A Sample App, JNI, NDK, and SDK: A Sample App setting up environment, Setting Up the NDK Environment Near Field Communication, Near Field Communication (NFC) (see NFC) NetworkException, Exceptions New Android Project dialog, Making an Android Project New Android Project Wizard, Making an Android Virtual Device (AVD) new keyword, Object Creation NFC (Near Field Communication), Near Field Communication (NFC), Reading a Tag, Reading a Tag, Writing to a Tag, P2P Mode about, Near Field Communication (NFC) P2P mode, P2P Mode reading tags, Reading a Tag, Reading a Tag writing tags, Writing to a Tag NFC Data Exchange Format (NDEF), Near Field Communication (NFC) NfcAdapter class, Reading a Tag, Reading a Tag, Reading a Tag, Reading a Tag, Reading a Tag, Reading a Tag, Reading a Tag, Reading a Tag, Reading a Tag, P2P Mode, P2P Mode, P2P Mode ACTION_NDEF_DISCOVERED constant, Reading a Tag ACTION_TAG_DISCOVERED constant, Reading a Tag ACTION_TECH_DISCOVERED constant, Reading a Tag disableForegroundDispatch method, Reading a Tag, P2P Mode enableForegroundDispatch method, Reading a Tag, Reading a Tag, P2P Mode enableForegroundNdefPush method, P2P Mode EXTRA_ID constant, Reading a Tag EXTRA_NDEF_MESSAGES constant, Reading a Tag getDefaultAdapter method, Reading a Tag 9 patch (Android resource), Draw9patch, Drawables NIST (National Institute of Standards and Technology), Relational Database Overview no-arg constructors, Object Creation NOT NULL constraint, Database constraints O Object class, The Object Class and Its Methods, The Object Class and Its Methods, The Object Class and Its Methods, The Object Class and Its Methods, The Object Class and Its Methods, The Object Class and Its Methods, The Object Class and Its Methods, The Object Class and Its Methods, The Object Class and Its Methods, Thread Control with wait() and notify() Methods, Thread Control with wait() and notify() Methods, The Android Libraries about, The Object Class and Its Methods clone method, The Object Class and Its Methods equals method, The Object Class and Its Methods finalize method, The Object Class and Its Methods hashCode method, The Object Class and Its Methods java.lang package and, The Android Libraries notify method, The Object Class and Its Methods, Thread Control with wait() and notify() Methods notifyAll method, The Object Class and Its Methods toString method, The Object Class and Its Methods wait method, The Object Class and Its Methods, Thread Control with wait() and notify() Methods object-relational mapping (ORM), Serialization, Database Queries and Reading Data from the Database ObjectInputStream class, Java Serialization ObjectOutputStream class, Java Serialization objects, Objects and Classes, Object Creation, Object Creation about, Objects and Classes creating, Object Creation, Object Creation OnClickListener.onClick method, Wiring Up the Controller OnCreateContextMenuListener interface, The Menu, Fragments and Multiplatform Support OnFocusChangeListener interface, Advanced Wiring: Focus and Threading OnGesturePerformedListener interface, Gesture Input OnKeyListener interface, Using Anonymous Classes, Alternative Ways to Handle Events, Alternative Ways to Handle Events, The Menu handling events, Using Anonymous Classes, Alternative Ways to Handle Events onKey method, Alternative Ways to Handle Events troubleshooting, The Menu OnTouchListener interface, Listening for Touch Events, Alternative Ways to Handle Events handling events, Alternative Ways to Handle Events onTouch method, Listening for Touch Events Open With command, Associations OpenGL, The Android Libraries, Bling, OpenGL Graphics, OpenGL Graphics, OpenGL Graphics about, OpenGL Graphics animation example, Bling graphics support, OpenGL Graphics, OpenGL Graphics javax package support, The Android Libraries org.apache.http package tree, The Android Libraries org.json package, The Android Libraries org.w3c.dom package, The Android Libraries org.xml.sax package, The Android Libraries org.xmlpull package, The Android Libraries ORM (object-relational mapping), Serialization, Database Queries and Reading Data from the Database OSGi bundles, Eclipse Concepts and Terminology, Plug-ins OutOfMemoryException, Exceptions OutputStream class, File Management and Binary Data, Bluetooth-specific protocols and adopted protocols overrides, defined, Overrides and callbacks, Overrides and callbacks P P2P (peer-to-peer) communication, P2P Mode packaging Android applications, Packaging an Android Application: The .apk File Paint class, Canvas Drawing, Shadows, Gradients, and Filters, Shadows, Gradients, and Filters about, Canvas Drawing attributes of, Shadows, Gradients, and Filters setShadowLayer method, Shadows, Gradients, and Filters PAN (personal area network), The Bluetooth Protocol Stack parameters, Final and Static Declarations, Initialization Parameters in AndroidManifest.xml, Initialization Parameters in AndroidManifest.xml AndroidManifest.xml file, Initialization Parameters in AndroidManifest.xml, Initialization Parameters in AndroidManifest.xml passing by value, Final and Static Declarations Parcelable interface, Parcelable, Parcelable, Parcelable, Classes That Support Serialization, Saving and restoring instance state serialization support, Parcelable, Parcelable, Classes That Support Serialization, Saving and restoring instance state writeToParcel method, Parcelable password, remembering for keystore, Creating a self-signed certificate PATH environment variable, The Java Development Kit (JDK), The Android SDK PathEffect class, Shadows, Gradients, and Filters peer-to-peer (P2P) communication, P2P Mode PendingIntent class, Reading a Tag percent sign (%), Example Database Manipulation Using sqlite3 period (.), Example Database Manipulation Using sqlite3 permissions, Recording Audio and Video, The Manifest and Layout Files, Account Contacts, Authentication, Synchronization account contacts, Account Contacts authentication, Authentication GPS location providers, The Manifest and Layout Files MediaRecorder class, Recording Audio and Video synchronization, Synchronization persistence, applications and, Serialization, SQL and the Database-Centric Data Model for Android Applications personal area network (PAN), The Bluetooth Protocol Stack phone coordinate systems, Position, Accelerometer, Gyroscope, Rotation vector, Linear acceleration, Gravity about, Position accelerometers, Accelerometer gravity, Gravity gyroscopes, Gyroscope linear acceleration, Linear acceleration rotation vector, Rotation vector piconet, The Bluetooth Protocol Stack pipe character (|), Example Database Manipulation Using sqlite3 Pixel Perfect view (Eclipse), Eclipse and Android playback, Playing Audio and Video, Audio Playback, Video Playback audio methods, Audio Playback life cycle states, Playing Audio and Video video methods, Video Playback plug-ins, Plug-ins, Plug-ins, Extensions, Extensions (see also ADT Eclipse plug-in) defined, Plug-ins extensions and, Extensions, Extensions polymorphism, Objects, Inheritance, and Polymorphism, Objects, Inheritance, and Polymorphism, Using polymorphism and composition, Using polymorphism and composition porting software to Android, On Porting Software to Android POST operation (REST), Content Providers Prediction class, Gesture Input Preferences dialog, Configuring the ADT plug-in, Troubleshooting SDK Problems: No Build Targets preorder traversal, The View pressure sensors, Other Sensors PRIMARY KEY constraint, Database constraints, Declaring Column Specification Strings primitive types, defined, Primitive Types private keys, Creating a self-signed certificate, Creating a self-signed certificate, Don’t lose it!

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Thus far, this chapter has discussed the intricate View framework that Android uses to organize and represent objects on the screen. OpenGL is a language in which an application describes an entire scene that will be rendered by an engine that is not only outside the JVM, but probably running on another processor altogether (the Graphics Processing Unit, or GPU). Coordinating the two processors’ views of the screen is tricky. The SurfaceView, discussed earlier, is nearly sufficient. Its purpose is to create a surface on which a thread other than the UI graphics thread can draw. The tool we’d like is an extension of SurfaceView that has a bit more support for concurrency combined with support for OpenGL.

pages: 506 words: 151,753

The Cryptopians: Idealism, Greed, Lies, and the Making of the First Big Cryptocurrency Craze
by Laura Shin
Published 22 Feb 2022

Joe had, by this point, gotten a number of people in New York excited about Ethereum. Its earliest offices at a coworking space in Bushwick, Brooklyn, were grungy, with murals lining the walls and hipsters roaming the halls. Everyone shared a massive box of Soylent. Before launch, people began snapping up GPUs (graphics processing units, which would be used to mine ether) to start mining the second the network went live. Once the company got its own offices in a heavily graffitied building nearby, they had a mini putt-putt green, and on Saturdays Joe sometimes played squash against the walls. There, Vitalik, Casey, Ming, and a C++ dev met with Joe and his deputy, Andrew Keys.

…

In the case of the DAO attacker, this 0.6931 ETC came from an address that began with eleven As: 0xaaaaaaaaaaaf7376faade1dcd50b104e8b70f3f2. Such an address would have required the user’s computer to run, on average, calculations 8.8 trillion times in order to derive such a public key.54 This indicated that the owner of the address likely had a lot of computer power, or GPUs (graphic processing units), which are more powerful than the chips on normal computers and were often used to mine Ethereum at that time. Perhaps the transaction from that 0xaaaaaaaaaaa address was the attacker him- or herself, making a “haha, suckers!” gesture. (The attacker seems to have been a connoisseur of vanity addresses.

…

A slang term, often used to dismiss criticism of a cryptocurrency as invalid but sometimes used to describe fake criticism about a cryptocurrency stoked by fans of a rival coin Game of Thrones Day the day Ethereum leadership removed Charles Hoskinson and Amir Chetrit gas the fee paid to have transactions processed or computation executed on the Ethereum decentralized computer Geth the Go Ethereum software client GitHub a website for software development GmbH the German version of an LLC (limited liability company) GPU graphics processing unit; a type of computer chip from a gaming computer that is more powerful than a typical computer’s central processing unit, or CPU, making it a more efficient and profitable way to mine cryptocurrencies (though not the most efficient and profitable) hard fork a non-backward-compatible software upgrade to a crypto network; usually refers to a “contentious” hard fork, in which one portion of the nodes on a crypto network make the upgrade and another portion of the nodes do not.

pages: 625 words: 167,349

The Alignment Problem: Machine Learning and Human Values
by Brian Christian
Published 5 Oct 2020

See artificial general intelligence Gergely, György, 218 Ghahramani, Zoubin, 283, 285–86, 287 Girouard, Mark J., 39 Giving What We Can, 379n67 Glimcher, Paul, 135 Global Catastrophic Risks (Bostrom and Ćirković), 262 GloVe, 37 Go, 145, 243–46, 380nn84–86 See also AlphaGo; AlphaGoZero Gödel, Escher, Bach (Hofstadter), 204–05 Goel, Sharad, 68, 348n47 Go-Explore, 373n54 Goh, Gabriel, 111, 357n69 Goldbert, Yoav, 44 Gonen, Hila, 44 Goodman, Bryce, 89–90 Goodman, Kenneth, 289 Google AdSense, 343n72 Artists and Machine Intelligence program, 112 BERT model, 344–45n94 Chrome, 347n33 DeepDream, 110, 112, 116 GoogLeNet, 115–16 Inception v3, 104–05, 107–08, 115–16 interpretability, 113–14 Photos, 25–26, 29, 339n24 word2vec, 5, 6–7, 9, 36, 37, 39, 316, 342n61 See also Google research Google Brain, 113, 167, 373n53 Google research differential privacy, 347n33 fairness, 73 feature visualization, 110 multitask learning models, 107 reinforcement learning, 167 selective classification, 390n29 value alignment, 247 word embedding, 44 Gopnik, Alison, 194, 215 gorilla tag incident, 25–26, 316, 339n24 GPT-2, 344–45n94 GPUs. See graphics processing units gradients. See stochastic gradient descent Graeber, David, 82 graphics processing units (GPUs), 21, 23, 339nn18, 20 gridworlds, 292–93, 294, 295, 390n29 Griffiths, Tom, 165–66, 177, 178–79 ground truth gaps clinical vs. statistical predictive models and, 97–98 fairness and, 317 human judgments and, 97–98 in image-recognition systems, 75 raw data and, 102–03 in risk-assessment models, 75–76 saliency methods and, 355n54 simple models and, 99, 101–02 TD learning and, 140 Gunning, Dave, 87–89 Hadfield-Menell, Dylan, 266–68, 273, 296, 297–98, 299, 300 Han, Hu, 31 Hand, Learned, 277 Handbook of Moral Theology (Prümmer), 303 Hands on a Hard Body, 312 happiness, 147–50 Harcourt, Bernard, 78–79, 346–47n25, 350n85 Hardt, Moritz, 29, 51, 63, 65–66, 73, 81, 351n87 Harlow, Harry, 188 Harno, Albert, 52 Harvard University, 254, 290 Hass, Robert, 235 Hastie, Trevor, 85 Hebb, Donald, 338n2 Hedonic Treadmill, 148, 149–50 helicopter flying, 168, 257–60, 383nn22–23 Herzberg, Elaine, 326–27 Hinton, Geoffrey, 21, 22, 24, 338–39n12 hiring, 22, 39–40, 48, 343nn72–74, 76–77 Hoare, C.A.R., 395n2 Hobbes, Thomas, 187 Hofstadter, Douglas, 204–05 Holder, Eric, 350n81 Holmes, Oliver Wendell, Jr., 98 Holt, Gregory, 289 Holte, Robert, 94 Hsu, Feng-hsiung, 241–42 Hui, Fan, 243 human judgments bias in, 39, 68 consensus and, 315, 396n9 as fallible, 54 independence from extrinsic motivation and, 209 interpretability and, 113–14 intrinsic motivation and, 186–87 irrational, 386–87n55 rationality enhancement and, 176–77 self-training, 174–80, 369n76 vs. statistical prediction, 91–94, 97–98 transparency and, 319, 397n19 word embedding and, 342n61 See also imitation; value alignment human-machine cooperation, 267–76 AI safety and, 268–69 aspiration and, 275–76, 386–87n55 CIRL, 267–68, 385nn40, 43–44 dangers of, 274–76 demonstration learning for, 271 feedback learning and, 270–71, 386n48 human-human team studies on, 271–73, 386n53 human irrationality and, 366–67n55 legibility and, 269–70, 386n47 parenting and, 269, 385–86n45 Hursthouse, Rosalind, 367n71 Hutter, Marcus, 262–63 Huxley, Aldous, 313 i.i.d.

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This quote is attributed to Hinton in “A ‘Brief’ History of Neural Nets and Deep Learning, Part 4,” https://www.andreykurenkov.com/writing/ai/a-brief-history-of-neural-nets-and-deep-learning-part-4/. It seems the original source, a video of one of Hinton’s talks, has since been removed from YouTube. 18. Nvidia, founded in 1993, launched its consequential GeForce 256, “the world’s first graphics processing unit (GPU),” on August 31, 1999 (see https://www.nvidia.com/object/IO_20020111_5424.html), although other similar technology, and indeed the term “GPU,” already existed—for instance, in the 1994 Sony PlayStation (see https://www.computer.org/publications/tech-news/chasing-pixels/is-it-time-to-rename-the-gpu). 19.

pages: 571 words: 105,054

Advances in Financial Machine Learning
by Marcos Lopez de Prado
Published 2 Feb 2018

It is also the deployment team's responsibility to optimize the implementation sufficiently, such that production latency is minimized. As production calculations often are time sensitive, this team will rely heavily on process schedulers, automation servers (Jenkins), vectorization, multithreading, multiprocessing, graphics processing unit (GPU-NVIDIA), distributed computing (Hadoop), high-performance computing (Slurm), and parallel computing techniques in general. Chapters 20–22 touch on various aspects interesting to this station, as they relate to financial ML. 1.3.1.6 Portfolio Oversight Once a strategy is deployed, it follows a cursus honorum, which entails the following stages or lifecycle: Embargo: Initially, the strategy is run on data observed after the end date of the backtest.

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Let us describe the computing elements, storage system, and networking system in turn. Figure 22.1 is a high-level schematic diagram representing the key components of the Magellan cluster around year 2010 (Jackson et al. [2010]; Yelick et al. [2011]). The computer elements include both CPUs and graphics processing units (GPUs). These CPUs and GPUs are commercial products in almost all the cases. For example, the nodes on dirac1 use a 24-core 2.2Ghz Intel processor, which is common to cloud computing systems. Currently, dirac1 does not contain GPUs. Figure 22.1 Schematic of the Magellan cluster (circa 2010), an example of HPC computer cluster The networking system consists of two parts: the InfiniBand network connecting the components within the cluster, and the switched network connection to the outside world.

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Python Data Analytics: With Pandas, NumPy, and Matplotlib
by Fabio Nelli
Published 27 Sep 2018

Even today the calculation with the CPUs, although considerably improved, requires long processing times. This inefficiency is due to the particular architecture of the CPUs, which have been designed to efficiently perform mathematical operations that are not those required by neural networks. But a new kind of hardware has developed in recent decades, the Graphics Processing Unit (GPU) , thanks to the enormous commercial drive of the videogames market. In fact this type of processor has been designed to manage more and more efficient vector calculations, such as multiplications between matrices, which is necessary for 3D reality simulations and rendering. Thanks to this technological innovation, many deep learning techniques have been realized.

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Index A Accents, LaTeX Advanced Data aggregation apply() functions transform() function Anaconda Anderson Iris Dataset, see Iris flower dataset Array manipulation joining arrays column_stack() and row_stack() hstack() function vstack() function splitting arrays hsplit() function split() function vsplit() function Artificial intelligence schematization of Artificial neural networks biological networks edges hidden layer input and output layer multi layer perceptron nodes schematization of SLP ( see Single layer perceptron (SLP)) weight B Bar chart 3D error bars horizontal matplotlib multiserial multiseries stacked bar pandas DataFrame representations stacked bar charts x-axis xticks() function Bayesian methods Big Data Bigrams Biological neural networks Blending operation C Caffe2 Chart typology Choropleth maps D3 library geographical representations HTML() function jinja2 JSON and TSV JSON TopoJSON require.config() results US population data source census.gov file TSV, codes HTML() function jinja2.Template pop2014_by_county dataframe population.csv render() function SUMLEV values Classification and regression trees Classification models Climatic data Clustered bar chart IPython Notebook jinja2 render() function Clustering models Collocations Computer vision Concatenation arrays combining concat() function dataframe keys option pivoting hierarchical indexing long to wide format stack() function unstack() function removing Correlation Covariance Cross-validation Cython D Data aggregation apply() functions GroupBy groupby() function operations output of SPLIT-APPLY-COMBINE hierarchical grouping merge() numeric and string values price1 column transform() function Data analysis charts data visualization definition deployment phase information knowledge knowledge domains computer science disciplines fields of application machine learning and artificial intelligence mathematics and statistics problems of open data predictive model process data sources deployment exploration/visualization extraction model validation planning phase predictive modeling preparation problem definition stages purpose of Python and quantitative and qualitative types categorical data numerical data DataFrame pandas definition nested dict operations structure transposition structure Data manipulation aggregation ( see Data aggregation) concatenation discretization and binning group iteration permutation phases of preparation ( see Data preparation) string ( see String manipulation) transformation Data preparation DataFrame merging operation pandas.concat() pandas.DataFrame.combine_first() pandas.merge() procedures of Data structures, operations DataFrame and series flexible arithmetic methods Data transformation drop_duplicates() function mapping adding values axes dict objects replacing values remove duplicates Data visualization adding text axis labels informative label mathematical expression modified of text() function bar chart ( see Bar chart) chart typology contour plot/map data analysis 3D surfaces grid grids, subplots handling date values histogram installation IPython and IPython QtConsole kwargs figures and axes horizontal subplots linewidth plot() function vertical subplots legend chart of legend() function multiseries chart upper-right corner line chart ( see Line chart) matplotlib architecture and NumPy matplotlib library ( see matplotlib library) mplot3d multi-panel plots grids, subplots subplots pie charts axis() function modified chart pandas Dataframe pie() function shadow kwarg plotting window buttons of commands matplotlib and NumPy plt.plot() function properties QtConsole polar chart pyplot module saving, charts HTML file image file source code scatter plot, 3D Decision trees Deep learning artificial ( see Artificial neural networks) artificial intelligence data availability machine learning neural networks and GPUs Python frameworks programming language schematization of TensorFlow ( see TensorFlow) Digits dataset definition digits.images array digit.targets array handwritten digits handwritten number images matplotlib library scikit-learn library Discretization and binning any() function categorical type cut() function describe() function detecting and filtering outliers qcut() std() function value_counts() function Django Dropping E Eclipse (pyDev) Element-wise computation Expression-oriented programming F Financial data Flexible arithmetic methods Fonts, LaTeX G Gradient theory Graphics Processing Unit (GPU) Grouping Group iteration chain of transformations functions on groups mark() function quantiles() function GroupBy object H Handwriting recognition digits dataset handwritten digits, matplotlib library learning and predicting OCR software scikit-learn svc estimator TensorFlow validation set, six digits Health data Hierarchical indexing arrays DataFrame reordering and sorting levels stack() function statistic levels structure two-dimensional structure I IDEs, see Interactive development environments (IDEs) Image analysis concept of convolutions definition edge detection blackandwhite.jpg image black and white system filters function gradients.jpg image gray gradients Laplacian and Sobel filters results source code face detection gradient theory OpenCV ( see Open Source Computer Vision (OpenCV)) operations representation of Indexing functionalities arithmetic and data alignment dropping reindexing Integration Interactive development environments (IDEs) Eclipse (pyDev) Komodo Liclipse NinjaIDE Spyder Sublime Interactive programming language Interfaced programming language Internet of Things (IoT) Interpreted programming language Interpreter characterization Cython Jython PVM PyPy tokenization IPython and IPython QtConsole Jupyter project logo Notebook DataFrames QtConsole shell tools of Iris flower dataset Anderson Iris Dataset IPython QtConsole Iris setosa features length and width, petal matplotlib library PCA decomposition target attribute types of analysis variables J JavaScript D3 Library bar chart CSS definitions data-driven documents HTML importing library IPython Notebooks Jinja2 library pandas dataframe render() function require.config() method web chart creation Jinja2 library Jython K K-nearest neighbors classification decision boundaries 2D scatterplot, sepals predict() function random.permutation() training and testing set L LaTeX accents fonts fractions, binomials, and stacked numbers with IPython Notebook in Markdown Cell in Python 2 Cell with matplotlib radicals subscripts and superscripts symbols arrow symbols big symbols binary operation and relation symbols Delimiters Hebrew lowercase Greek miscellaneous symbols standard function names uppercase Greek Learning phase Liclipse Linear regression Line chart annotate() arrowprops kwarg Cartesian axes color codes data points different series gca() function Greek characters LaTeX expression line and color styles mathematical expressions mathematical function pandas plot() function set_position() function xticks() and yticks() functions Linux distribution LOD cloud diagram Logistic regression M Machine learning (ML) algorithm development process deep learning diabetes dataset features/attributes Iris flower dataset learning problem linear/least square regression coef_ attribute fit() function linear correlation parameters physiological factors and progression of diabetes single physiological factor schematization of supervised learning SVM ( see Support vector machines (SVMs)) training and testing set unsupervised learning Mapping adding values inplace option rename() function renaming, axes replacing values Mathematical expressions with LaTeX, see LaTeX MATLAB matplotlib matplotlib library architecture artist layer backend layer functions and tools layers pylab and pyplot scripting layer (pyplot) artist layer graphical representation hierarchical structure primitive and composite graphical representation LaTeX NumPy Matrix product Merging operation DataFrame dataframe objects index join() function JOIN operation left_index/right_index options left join, right join and outer join left_on and right_on merge() function Meteorological data Adriatic Sea and Po Valley cities Comacchio image of mountainous areas reference standards TheTimeNow website climate data source JSON file Weather Map site IPython Notebook chart representation CSV files DataFrames humidity function linear regression matplotlib library Milan read_csv() function result shape() function SVR method temperature Jupyter Notebook access internal data command line dataframe extraction procedures Ferrara JSON file json.load() function parameters prepare() function RoseWind ( see RoseWind) wind speed Microsoft excel files dataframe data.xls internal module xlrd read_excel() function MongoDB Multi Layer Perceptron (MLP) artificial networks evaluation of experimental data hidden layers IPython session learning phase model definition test phase and accuracy calculation Musical data N Natural Language Toolkit (NLTK) bigrams and collocations common_contexts() function concordance() function corpora downloader tool fileids() function HTML pages, text len() function library macbeth variable Python library request() function selecting words sentimental analysis sents() function similar() function text, network word frequency macbeth variable most_common() function nltk.download() function nltk.FreqDist() function stopwords string() function word search Ndarray array() function data, types dtype (data-type) intrinsic creation type() function NOSE MODULE “Not a Number” data filling, NaN occurrences filtering out NaN values NaN value NumPy library array manipulation ( see Array manipulation) basic operations aggregate functions arithmetic operators increment and decrement operators matrix product ufunc broadcasting compatibility complex cases operator/function BSD conditions and Boolean arrays copies/views of objects data analysis indexing bidimensional array monodimensional ndarray negative index value installation iterating an array ndarray ( see Ndarray) Numarray python language reading and writing array data shape manipulation slicing structured arrays vectorization O Object-oriented programming language OCR, see Optical Character Recognition (OCR) software Open data Open data sources climatic data demographics IPython Notebook matplotlib pandas dataframes pop2014_by_state dataframe pop2014 dataframe United States Census Bureau financial data health data miscellaneous and public data sets musical data political and government data publications, newspapers, and books social data sports data Open Source Computer Vision (OpenCV) deep learning image processing and analysis add() function blackish image blending destroyWindow() method elementary operations imread() method imshow() method load and display merge() method NumPy matrices saving option waitKey() method working process installation MATLAB packages start programming Open-source programming language Optical Character Recognition (OCR) software order() function P Pandas dataframes Pandas data structures DataFrame assigning values deleting column element selection filtering membership value nested dict transposition evaluating values index objects duplicate labels methods NaN values NumPy arrays and existing series operations operations and mathematical functions series assigning values declaration dictionaries filtering values index internal elements, selection operations Pandas library correlation and covariance data structures ( see Pandas data structures) function application and mapping element row/column statistics getting started hierarchical indexing and leveling indexes ( see Indexing functionalities) installation Anaconda development phases Linux module repository, Windows PyPI source testing “Not a Number” data python data analysis sorting and ranking Permutation new_order array np.random.randint() function numpy.random.permutation() function random sampling DataFrame take() function Pickle—python object serialization cPickle frame.pkl pandas library stream of bytes Political and government data pop2014_by_county dataframe pop2014_by_state dataframe pop2014 dataframe Portable programming language PostgreSQL Principal component analysis (PCA) Public data sets PVM, see Python virtual machine (PVM) pyplot module interactive chart Line2D object plotting window show() function PyPy interpreter Python data analysis library deep learning frameworks module OpenCV Python Package Index (PyPI) Python’s world code implementation distributions Anaconda Enthought Canopy Python(x,y) IDEs ( see Interactive development environments (IDEs)) installation interact interpreter ( see Interpreter) IPython ( see IPython) programming language PyPI Python 2 Python 3 running, entire program code SciPy libraries matplotlib NumPy pandas shell source code data structure dictionaries and lists functional programming Hello World index libraries and functions map() function mathematical operations print() function writing python code, indentation Python virtual machine (PVM) PyTorch Q Qualitative analysis Quantitative analysis R R Radial Basis Function (RBF) Radicals, LaTeX Ranking Reading and writing array binary files tabular data Reading and writing data CSV and textual files header option index_col option myCSV_01.csv myCSV_03.csv names option read_csv() function read_table() function .txt extension databases create_engine() function dataframe pandas.io.sql module pgAdmin III PostgreSQL read_sql() function read_sql_query() function read_sql_table() function sqlalchemy sqlite3 DataFrame objects functionalities HDF5 library data structures HDFStore hierarchical data format mydata.h5 HTML files data structures read_html () web_frames web pages web scraping I/O API Tools JSON data books.json frame.json json_normalize() function JSONViewer normalization read_json() and to_json() read_json() function Microsoft excel files NoSQL database insert() function MongoDB pickle—python object serialization RegExp metacharacters read_table() skiprows TXT files nrows and skiprows options portion by portion writing ( see Writing data) XML ( see XML) Regression models Reindexing RoseWind DataFrame hist array polar chart scatter plot representation showRoseWind() function S Scikit-learn library data analysis k-nearest neighbors classification PCA Python module sklearn.svm.SVC supervised learning svm module SciPy libraries matplotlib NumPy pandas Sentimental analysis document_features() function documents list() function movie_reviews negative/positive opinion opinion mining Shape manipulation reshape() function shape attribute transpose() function Single layer perceptron (SLP) accuracy activation function architecture cost optimization data analysis evaluation phase learning phase model definition explicitly implicitly learning phase placeholders tf.add() function tf.nn.softmax() function modules representation testing set test phase and accuracy calculation training sets Social data sort_index() function Sports data SQLite3 stack() function String manipulation built-in methods count() function error message index() and find() join() function replace() function split() function strip() function regular expressions findall() function match() function re.compile() function regex re.split() function split() function Structured arrays dtype option structs/records Subjective interpretations Subscripts and superscripts, LaTeX Supervised learning machine learning scikit-learn Support vector classification (SVC) decision area effect, decision boundary nonlinear number of points, C parameter predict() function regularization support_vectors array training set, decision space Support vector machines (SVMs) decisional space decision boundary Iris Dataset decision boundaries linear decision boundaries polynomial decision boundaries polynomial kernel RBF kernel training set SVC ( see Support vector classification (SVC)) SVR ( see Support vector regression (SVR)) Support vector regression (SVR) curves diabetes dataset linear predictive model test set, data swaplevel() function T TensorFlow data flow graph Google’s framework installation IPython QtConsole MLP ( see Multi Layer Perceptron (MLP)) model and sessions SLP ( see Single layer perceptron (SLP)) tensors operation parameters print() function representations of tf.convert_to_tensor() function tf.ones() method tf.random_normal() function tf.random_uniform() function tf.zeros() method Text analysis techniques definition NLTK ( see Natural Language Toolkit (NLTK)) techniques Theano trigrams() function U, V United States Census Bureau Universal functions (ufunc) Unsupervised learning W Web Scraping Wind speed polar chart representation RoseWind_Speed() function ShowRoseWind() function ShowRoseWind_Speed() function to_csv () function Writing data HTML files myFrame.html to_html() function na_rep option to_csv() function X, Y, Z XML books.xml getchildren() getroot() function lxml.etree tree structure lxml library objectify parse() function tag attribute text attribute

The Deep Learning Revolution (The MIT Press)
by Terrence J. Sejnowski
Published 27 Sep 2018

In 2016, for example, Intel purchased Nervana, a small start-up company in San Diego that has designed special-purpose VLSI chips for deep learning, for $400 million; former Nervana CEO Naveen Rao is now heading Intel’s new AI Products Group, which reports directly to the CEO of Intel. In 2017, Intel purchased Mobileye, a company that specializes in sensors and computer vision for self-driving cars, for $15.3 billion dollars. Nvidia, which developed special-purpose digital chips optimized for graphics applications and gaming, called “graphics processing units” (GPUs), is now selling more special-purpose chips for deep learning and cloud computing. And Google has designed a far more efficient special-purpose chip, the tensor processing unit (TPU), to power deep learning for its Internet services. But specialized software is equally important for developing deep learning applications.

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(Sejnowski’s wife), 44, 174, 203, 224, 269, 271 on the brain, 174 Ed Posner and, 44, 163 Francis Crick and, 269, 319n3 parallel distributed processing (PDP) and, 203 perceptron and, 44, 44f Sejnowski and, 161, 163, 269–270 Index SEXNET talk, 161 Stephen Wolfram and, 203 writings, 44f, 286n7, 291nn8–9, 313n2, 314n6 Golomb, Solomon “Sol” Wolf (Beatrice’s father), 220–224, 222f, 271, 273 Goodfellow, Ian, 135 Google, 20, 191, 205 deep learning and, ix, 7, 192 Geoffrey Hinton and, ix, 191, 273 PageRank algorithm, 311n4 self-driving cars, ix, 4, 6 TensorFlow and, 205–206 tensor processing unit (TPU), 7, 205 Google Assistant, 192 Google Brain, 191–192, 273 Google Translate, ix, 7, 8, 8f, 117, 191 Google X, 4 Gopnik, Alison, 317n10 Gould, Stephen Jay, 312n14 Gradient descent, 112 Grand Challenges for Science in the 21st Century conference, 195 Grandmother cell hypothesis, 235, 237 Grandmother cells, 235, 236f, 237–238 Graphics processing units (GPUs), 205 Graves, Alex, 259, 318n25 Gray, Michael S., 44f Greenspan, Ralph J., 316n21 Griffin, Donald R., 277 Groh, Jennifer M., 315n12 Gross, Charles G., 56, 64, 293n3 Grossberg, Stephen, 92, 297n5 Gross domestic intangibles (GDI), 193 Gross domestic product (GDP), 193 Guggenheim Museum Bilbao, 72, 72f Gutmann, Amy, 226f Halgren, Eric, 227, 228f, 314n12 Handwritten zip codes, learning to recognize, 104, 105f, 106 Hanson, David, 179f, 308n16 Hardy, Godfrey H., 223, 314n5 Index Harris, Kristen M., 121, 300n18 Hassabis, Demis, 19f, 20, 159, 288n36, 317n15 Hasson, Uri, 78, 295n18 Hawking, Stephen, 24, 125 Haykin, Simon, 154, 291n13, 305nn16–17 He, Kaiming, 129 Hebb, Donald O., 79, 101, 298n16, 313n11 Hebbian synaptic plasticity, 79, 95b, 101–102, 133, 213 Hecht-Nielsen, Robert, 118 Heeger, David J., 295n18 Helmholtz, Hermann von, 63, 225f, 314n10 Helmholtz Club, 63, 293n2 Hemingway, Ernest, 7–8 Herault, Jeanny, 81, 295n1 Hertz, John A., 94f Hidden target distribution, 241, 242f Hidden targets, 241 Hidden units (in neural networks), 103f, 113, 114f, 116, 119, 128, 132–133, 237–238 backprop networks with, 111b, 118, 148 in Boltzmann machine, 98b, 101, 102, 104, 106, 109 layers of, 47, 72, 74, 98b, 104, 106, 111b, 128, 153 perceptron and, 106, 109 simple cells compared with, 72, 74 Hillis, Danny, 229 Hinton, Geoffrey Everest, 91, 92, 96, 113, 117, 127–129, 271, 272 Boltzmann machine and, 49, 79, 104, 105f, 106, 110, 112, 127 Carnegie Mellon and, 60f, 117, 117f characterizations of, ix Charles Smith and, 61 computing with networks and, 273 David Rumelhart and, 109, 110, 112 329 deep learning and, 129f, 141f dropout technique and, 120 education, 50 George Boole and, 54 Google and, ix, 191, 273 neural networks and, 49, 165, 207 overview, 49–51 photographs, 50f, 60f, 117f, 129f positions held by, 51, 52, 61, 99, 127, 141, 191, 310n41 students, 24, 104, 128, 165 and the workings of the brain, 49–51 workshops, 1, 49, 50f, 52, 54, 60f, 109 writings, 1, 79, 97f, 100f, 103f, 105f, 112, 132f, 165, 286n13, 292n6, 297n12, 298n14, 298n20, 298n22, 299n4, 300nn14–15, 302n7, 303n17 Hippocampus (HC), 76f, 94, 101, 121, 236f Hit the opponent pieces, 148 HMAX, 128 Ho, Yu-Chi, 299n2 Hochreiter, Sepp, 134 Hodgkin, Alan, 32 Hoff, Ted, 39 Hofstadter, Douglas R., 224 Holland, John H., 312n15 Hollom, P.

pages: 421 words: 110,406

Platform Revolution: How Networked Markets Are Transforming the Economy--And How to Make Them Work for You
by Sangeet Paul Choudary , Marshall W. van Alstyne and Geoffrey G. Parker
Published 27 Mar 2016

As you can see, Amazon has by far outstripped Walmart in the number and variety of APIs provided. The power of modularity is one of the reasons that the personal computer industry grew so quickly in the 1990s. The key components of PC systems were central processing units (CPUs) that provided the computation, graphical processing units (GPUs) that created rich images on the screen, random access memory (RAM) that provided working storage, and a spinning hard drive (HD) that provided large amounts of long-term storage. Each of these subsystems communicated with the others using well-defined interfaces that allowed for tremendous innovation, as firms such as Intel (CPUs), ATI and Nvidia (GPUs), Kingston (RAM), and Seagate (HD) all worked independently to improve the performance of their products.

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B., 54–55 classified ads, 49, 63, 120, 131, 133–34 click-throughs, 190, 197 cloud-based networking, 30, 56 cloud-based storage, 54, 56, 56, 59, 102, 177–78 cloud computing, 145, 155, 209 Coase, Ronald, 234–35 Coca-Cola, 198–99 code, computer, 53, 79–80, 131–32, 140, 143, 166, 170, 172, 240, 254–55, 267 coffee machines, 143, 157–58, 159 colleges and universities, 8–9, 91, 97–99, 265–68 community-driven curation, 67–68, 78 comparative advantage, 188–89 competition complexities, 210–13 computer programming, 52, 99, 131–32, 267 Confinity, 80, 83 Congress, U.S., 248–49 connectivity platforms, 200–201, 285, 289 consulting firms, 8, 194 consumer relationship management (CRM), 11, 96, 174 consumer-to-consumer marketplace, 2–3, 29 contact information, 163, 190 contracts, 142, 166, 172, 197, 225–26 control systems, 164–65 convex growth, 20, 295, 297 Cook, Tim, 148 co-opetition (co-creation), 194, 212, 227 copyright, 57, 167, 208, 258, 259 core developers, 141–42 corporations, 157–58 governance by, 164, 256–60 image ads for, 229–30 sponsorship by, 137, 139–40 corruption, 160, 236–37 cosmetics industry, 206 Coursera, 8, 265, 266, 267 Craigslist, 47, 49, 91–92, 101, 103, 165, 193, 224 credit, 170, 175, 243–44, 263, 275–76, 277 credit cards, 37, 81, 83, 84, 137, 139–40, 175, 226, 243, 275 credit reports, 243–44 crime, 165, 231, 257 critical assets, 220–21 critical mass, 97, 112, 188, 195, 201–2 Croll, Alistair, 191, 196 cross-side effects, 29, 30, 34, 295 crowd curation, 167–70 crowdfunding, 40, 51, 92, 96, 102, 111 crowdsourcing, 12, 267 Cryptography, 171 currency, 5, 15, 36, 37–38, 46, 159, 173–74 customized ads, 244 Cusumano, Michael A., 58, 178–79 Damodaran, Aswath, 16–18 data: accountability based on, 253–56 aggregators for, 141, 145–46, 244–48, 254, 255, 262–63, 278 big, 11, 247–48, 276 brokers of, 244–45 capture and collection of, 218–19, 264, 296 in communications, 176–78 flow of, 170, 246–48 leveraging of, 217–20 manipulation of, 251–53 in nationalism, 247–48, 260 platforms for, 200 profiles derived from, 48, 119, 127 security of, 230, 243–46, 260 software for, 91–92, 107, 255, 269, 270–71, 275, 276–77, 278, 284, 286 storage of, 54, 56, 56, 59, 102, 171–73, 177–78 strategic, 217–20 tactical, 217–18 tools for, 10–11, 48, 49, 71 databases, 24–25, 42–44, 63, 72–75, 76, 91–92, 107 “Data Brokers: A Call for Transparency and Accountability,” 245 Data.gov, 283 Data Jams, 282 dating services, 26–28, 30, 93, 97–98, 120, 123, 166, 194 De Beers, 208–9 decentralization, 159, 171–73, 272–74 deep design, 179–80 defaults, 170, 263, 276 Delicious, 95–96 demand-side economies of scale, 18–20, 32, 34, 226 democracy, 149–50, 257, 283 department stores, 264, 287 designers, graphic, 66, 72, 106, 118–19, 267 design structure matrices, 57–58 diabetes, 269–70 diamond industry, 208–9 digital currency, 171, 274–78 digital message deliveries, 94–95 digital real estate, 174–75, 216 digital rights management (DRM), 31 Diners Club, 84 direct-to-consumer channels, 264 discounts, 22, 25–26 disintermediation, 68–69, 71–72, 78, 161–62, 170–71, 298 disk defragmentation, 200 dispute resolution, 169–70 Djankov, Simeon, 238, 239 doctors, 263, 268, 269, 271, 279 Dorsey, Jack, 97 dot-com bubble, x, 22, 79, 80, 113, 288 Dribbble, 37, 66, 118–19 driverless cars, 284 driver ratings, 254, 264 driving records, 232–33, 277 Dropbox, 32, 102, 109 Drucker, Peter, 210 drug trafficking, 162 Duhigg, Charles, 146 Duracell, 162 DVDs, 63, 111, 139 e-commerce, 56, 91, 111, 124–25, 145, 204–5 Earth Class Mail, 94–95 eBay, vii, ix, 2, 3, 17, 24, 36, 38, 40, 83–84, 85, 91, 93, 111, 112–13, 125, 135, 161–62, 163, 169–71, 172, 173, 196n, 205, 206, 207, 215, 262 economics, 72, 78, 230, 234–39 economies of scale, 18–20, 206 Edison, Thomas, 19, 284 editors, 7, 10, 68, 72, 93, 129–30, 262 education, 7–8, 77, 96, 111, 122, 124, 212, 233, 261, 263, 265–68, 269, 288, 289 education platforms, 96, 111, 265–68, 289 Eisenmann, Thomas R., ix, 130 electric lighting, 19, 285 electric power, 19, 69, 247, 284–85 Electronic Arts (EA), 94, 124, 240 electronic health records, 270 electronics, 75, 178, 206 email, 81, 85, 101–4, 185 Encyclopaedia Britannica, 10–11 encyclopaedias, 10–11, 129–30, 133, 149–51 Endomondo, 75 end-to-end design principle, 52–54 energy: efficiency of, 254, 284–85, 286 industry for, 261, 272–74, 289 resources of, 69–70, 254, 272–74 enhanced access, 112, 118, 119–21, 126, 127, 296 enhanced curation, 121–22, 126–27 enhanced design, 223–24 enterprise management, 173–75 enterprise resource planning (ERP), 11 entrepreneurs, 79–83, 86, 96, 111, 205, 282 environmental issues, 62, 70–71, 233, 237, 272, 274 Equal Credit Opportunity Act (1974), 243–44 Equity Bank, 277–78 e-readers, 178 eToys, 22 Etsy, 65, 73, 149, 212, 262, 299 European Union (EU), 242, 247–48 events listings, 112–15, 126 Excel, 216 excess inertia, 241, 296 exclusive access, 213–15 expert networks, 30, 36, 68, 93, 96, 99, 117–18 extension developers, 141, 142–45, 147, 148–49, 153–54 external networks, 100–101, 102, 103–4, 105 Facebook, 3, 12, 20, 32, 33, 37, 39, 41–50, 66, 90–91, 98–103, 104, 112, 121, 126, 131–35, 132, 133, 145, 151, 159, 163, 168, 181, 184, 185, 197, 204, 216–18, 221, 226, 245, 251–52, 267, 270–71 Fair Credit Reporting Act (1970), 175 fairness, 179–81, 230 fair use doctrine, 259 farm prices, 42–44, 60 FarmVille, 221 Fasal, 42–44 Federal Reserve, 174 Federal Trade Commission (FTC), 242, 243–44, 245 FedEx, 61, 249–50, 278 feedback loops, 21, 28, 45–46, 68, 71, 72, 100–101, 108, 139, 167–70, 218–19, 223, 296 feet on street (FOS) sales forces, 43–44, 91 files, 63, 166 encryption of, 200 formats for, 29–30 film industry, 9, 66, 138–39, 163, 178, 259, 267 filters, 38–41, 42, 59, 133, 295, 296–97 financial crash of 2008, 178, 230 financial services industry, 11, 16–18, 33, 164, 171–73, 178, 230, 261, 274–78, 289 fitness and sports activities, 74–76, 245, 270–71 five forces model, 207–10, 212, 213, 221 500px, 37, 47 Fiverr, 116, 193 fixed costs, 9–10, 209, 224–25, 278 follow-the-rabbit strategy, 89–91, 105 food industry, 76, 254, 255, 278 Ford, Henry, 19, 32 Ford Motor Co., 19 Fortune 500, 65 Foursquare, 97, 98 fragmented industries, 131, 262, 265, 268–69, 289 fraud, 175, 196–97, 255, 257, 276 Free: The Future of a Radical Price (Anderson), 22 freelancers (independent contractors), 21, 36, 37, 64, 65, 117–18, 193–94, 196, 210, 213, 233–34, 249–51, 279, 280, 287, 297, 299 free trade, 205, 206, 235 Friedersdorf, Conor, 236 Friendster, 98 FuelBand, 74, 75 full-time employees, 249–50 FUSE Labs, 252–53 games, gaming, 94, 103, 124, 132, 159, 163, 178, 211, 212, 217, 221, 240 “Gangnam Style,” 84, 147 gatekeepers, 7–8, 151–52, 171–73, 243, 253, 262, 265, 268, 275–76, 281, 289, 298 Gawer, Annabelle, 58, 178–79 Gebbia, Joe, 1–2 General Electric (GE), 4, 13, 19, 76, 78, 86, 110, 201, 204, 208, 247, 284 Generally Accepted Accounting Principles, 238–39 geographic focus, 98–99, 271 Germany, 96–97, 161, 205 Gillette, King, 109–10 Global 500, 209–10 Go-Jek, 278 Goldberg, Whoopi, 23 Goodwin, Tom, 11–12 Google, vii, 3–7, 21–25, 30–33, 49–50, 55, 58, 64, 72, 111, 112, 120, 121, 125, 134, 137, 140–41, 148, 153–54, 159, 198–99, 214–17, 226, 240, 242, 250, 267, 270–71 Google AdWords, 72, 120, 121, 125 Google Maps, 49–50, 55, 148, 200 Google Play, 154 government platforms, 261, 281–83, 289 graphical processing units (GPUs), 56, 57, 58 graphic design, 67, 226 Great Britain, 160, 205 gross domestic product (GDP), 160, 161 Grossman, Nick, 253, 254, 255, 256 Guardian, 144–45 Gurley, Bill, 16–18, 21 Haber-Bosch process, 19 Hachette Book Group, 251 Haier Group, vii, 76, 125, 198–99, 222 Halo, 94, 240 Halo: Combat Evolved, 94 Hammurabi, Code of, 274 hard drives (HDs), 56, 57, 58 hardware, 56, 57–58, 136, 152–53, 178–79 Harvard University, 98–99, 266 hashtags, 58, 104 Havas Media, 11–12 health care, 32–33, 35, 69, 71, 77, 200, 233, 234, 238, 245, 261, 263, 265, 268–72, 277, 280, 289 health insurance, 234, 263, 271–72, 277, 280 Heiferman, Scott, 113–14, 126 heirlooms, 161–62 Here, 49–50 Hertz, 9 heuristics, 123–24 Hilton Hotels, 8, 64 Hipstamatic, 100 homeowners’ insurance, 175, 232 horizontal integration, 33, 74–76, 208 hospitals, 69, 71, 233, 270, 271–72 hosting sites, 88, 198, 223–24 hotel industry, 1–2, 8–9, 10, 12, 64, 67, 101, 111, 142–43, 198, 224, 229–33, 236, 253, 287 Hotmail, 103, 104 Houghton Mifflin Harcourt, 204, 208, 225 HTTP, 177 Huffington Post, 90 human resources, 14, 39 human rights, 159, 160–61 hypercompetition, 209–10, 213 IBM, x, 137, 152, 179, 284 iCloud, 75 identity theft, 244 InCloudCounsel, 279 income streams, 139–41, 143, 144, 215 India, 73, 91 Indiegogo, 96, 124 Indonesia, 278 Industrial Awakening, 285–86 industrial development, 205–10, 224–25, 268 industrial-era firms, 19, 32, 34, 256, 285, 288 Industrial Revolution, 288 Industry Standard Architecture (ISA), 58 information, 40, 42 agencies for, 243–44 age of, 253, 256, 260 asymmetries of, 161–62, 164, 181, 182, 220, 228, 258–59, 262–63, 265, 269, 281, 289 exchange of, 36, 37, 39, 41, 47–48, 51, 134 intensive need for, 262–63, 265, 268, 281, 289 mis-, 129–30 platforms for, 190, 200, 287 units of, 296–97 initial public offerings (IPOs), ix, 91, 204–5 Instagram, 3, 13, 32, 46, 47, 66, 85, 100, 102–3, 104, 204, 217, 218, 299 instant messaging, 131, 198, 211 insurance industry, 9, 62, 71, 142, 164, 175–76, 232, 268, 277 integrated systems, 33, 74, 131 Intel, vii, x, 57–58, 89, 137, 178–81, 270–71, 284 Intel Architecture Labs (IAL), 179–81 intellectual property (IP), 33, 57, 167, 174–75, 180, 258 interaction failures, 190, 192, 196–97 Interbrand, 198–99 interest rates, 170, 244, 276 Internal Revenue Service (IRS), 93 internal transparency, 176–79 International Financial Reporting Standards, 238–39 Internet, 24–25, 32, 60–63, 76–79, 95–96, 107–13, 121, 167, 201, 204, 205, 209, 244, 249, 250, 263, 264, 283–89, 299 Internet of things, 76, 201, 204, 283–86, 289 inventory, 9, 11–12, 25, 42, 141, 184, 186, 262 investment, ix, 16, 63, 164, 168–69, 184–86, 209, 278 iPads, 95 iPhone, 3, 6–7, 72, 131, 140, 147, 148, 178, 211, 213–14, 222 iStockphoto, 167–68, 173 iTunes, 75, 131, 142, 153, 164, 214, 231 Japan, 66, 205–6 Jassy, Andrew, 177–78 Java programming language, 140 Jawbone, x, 77, 245 job listings, 39, 49, 50, 51, 63, 111, 118–19, 120, 131, 133–34, 137, 184–86, 196, 201, 218 Jobs, Steve, viii, 53, 131, 214 joint venture model, 137, 138 judiciary, 237, 238, 250 JVC, 138–39 Kalanick, Travis, 18, 62 Kelley, Brian P., 157 Kenya, 277–78 Kercher, Meredith, 129–30, 149–50 Keurig Green Mountain, 143, 157–58, 159, 181 Kickstarter, 40, 92, 96, 102, 111 Kindle, 7, 10, 67, 140, 154, 243 Kindle Fire, 140 Knox, Amanda, 129–30, 149–50 Korengold, Barry, 61 Kozmo, 22–23 Kretschmer, Tobias, 257 Kuraitis, Vince, 270, 271 labor: child, 164 division of, 280 market for, 39, 49, 50, 51, 63, 111, 118–19, 120, 131, 133–34, 137, 196, 201, 218, 235 platforms for, 200, 201, 213, 233–34, 248–51, 279–81, 289 regulation of, 230, 249–51, 260, 288 self-employed, 21, 36, 37, 64, 65, 117–18, 193–94, 196, 210, 213, 233–34, 249–51, 279, 280, 287, 297, 299 unions for, 280, 288 Laffont, Jean-Jacques, 235, 237 law firms, 8, 204, 279 laws and legal systems, 88, 164–70, 182, 230, 247–49, 257, 258, 260, 281 lead generation, 113, 117 Lean Analytics (Croll and Yoskovitz), 191, 196 lean startups, 199, 201–2 Lee Kuan Yew, 160–61 LegalZoom, 204, 225 Lending Club, 77, 275, 276 Lessig, Lawrence, 164–65, 166 Levchin, Max, 79–81 Lexis, 204, 225 liability coverage, 175, 232 libertarianism, 79, 80, 236, 238 licensing fees, 61, 131, 258–59 licensing model, 136–37, 138, 139, 214, 235, 296 lightbulbs, 284–85 linear value chains (pipelines), 6, 183–84, 297, 298 LinkedIn, 39, 41–42, 48, 50–51, 103, 111, 119, 170, 173, 184, 197, 218–19, 223, 226, 245 Linux, 137, 138, 154, 200 liquidity, 189–91, 193, 194–95, 201–2, 297 local content regulations, 246–47 logos (icons), 82, 83 “long tail” (software adoption), 216–17, 219 Lyft, 49, 50–51, 67, 213, 227, 250–51, 297 Ma, Jack, 125, 206, 215 MacCormack, Alan, 57 magazines, 72, 151, 197, 244, 264, 275 magnetic resonance imaging (MRI), 69, 71 mail, 63, 94–95, 171 MailChimp, 109 Malaysia, 160 Management Science (MacCormack and Baldwin), 57 mandis (market-makers), 42–44 Manghani, Ravi, 273–74 manufacturing efficiencies, 208, 209, 261 MapMyFitness, 75 mapping services, 49–50, 55, 148, 200 marginal economics, 72, 78 Marini, Rick, 184–85 marketing, 14, 19, 25, 52–53, 72, 73–74, 84–85, 100, 101, 105, 183–84, 209–10, 267 Marketplace Fairness Act (2013), 249 marketplaces, 60, 91, 190, 204, 249 markets: access to, 87–88, 98, 194, 215, 218, 220 aggregation of, 68–69, 72–73, 78, 262, 297 controls for, 164–65 data on, 42–44 emerging, 210–11 entry barriers to, 207–8, 215, 219–20 expansion of, 4, 20, 31–32 failure of, xiii, 161–63, 164, 170–71, 182, 234–35, 256, 257, 258–59, 263, 289 free, 149, 161–65, 173–76, 180, 182, 234–36 frictionless entry into, 25–26, 34, 81, 107–8, 111, 117, 124–25, 130, 168, 206, 297 incumbent advantage in, 86, 218, 261, 263 late-mover problem in, 87–88, 98 liquidity of, 171, 196 local, 70–71, 117–18, 264 manipulation of, 238, 251–53, 260, 287 micro-, 98–99, 105 multi-sided, 159, 164 new entrants to, 207–10, 262, 296 niche, 88, 216, 223–24, 228, 300 one-sided, 157–58, 159 share of, 16–22, 33, 53, 60–62, 65, 81, 87–88, 112–13, 131–33, 132, 133, 137–40, 152–53, 157, 222–26, 260, 287 strategy for, viii, xi, 10, 16–18, 20, 21, 31–32, 33, 42–44, 57–58, 69–73, 77, 78, 89, 111, 124, 173, 210–11, 272–74, 278 supply and demand in, 69–71, 173, 210–11, 272–74, 278 “thickness” of, 164, 171, 173 two-sided, 81, 89, 93, 110, 119, 175, 196, 215, 218, 295, 298 winner-take-all, viii, 224–27, 228, 279–80, 300 marquee strategy, 94–95, 105 Marriott Hotels, 8–9 massive open online courses (MOOCs), 266–67 mass media, 40, 63, 72, 77, 262, 264 MasterCard, 226, 275 matching services, 17, 47–48 Matharu, Taran, 4–5 McCormick Foods, 76 McGraw-Hill, 204, 208 Mechanical Turk, 249, 280 Medicare, 250 Medicast, 269, 279 Medium, 71–72 Meetup, 113–15, 126 Megaupload, 87–88 membership fees, 123, 125 Mercateo, 96–97 mergers and acquisitions, 208, 216, 220–21, 228 Metcalfe, Robert, 20, 297 Metcalfe’s law, 20, 21, 295, 297 metering tools, 272–73 Microsoft, vii, x, 3, 13, 20, 29, 33, 52–53, 94, 103–4, 110, 124, 131, 140, 152–53, 179, 181, 200, 211, 216, 226, 240, 241, 252, 267, 270–71 Microsoft Outlook, 103–4 Microsoft Vista, 52–53 Microsoft Windows, 30, 53, 140, 152–53, 200, 222, 240 Microsoft Windows XP, 53 middlemen, 68–69, 71–72, 78, 161–62, 170–71, 298 Minerva Project, 268 mining, 225, 263 mislabeled bargains, 161–62, 170–71 MIT, ix–x, xi, 214, 266, 267 MIT Initiative on the Digital Economy, ix–x MIT Platform Strategy Summit, xi, 214 moderators, 151–52 modular design, 54–57, 221 monetary policy, 159, 173–74 monetization, 38, 63, 106–27, 188, 215 MonkeyParking, 233, 234 monopolies, 18–19, 162, 163, 172–73, 182, 208–9, 227, 237, 238, 240–41, 242 Monster, 218–19, 223, 226 mortgages, 237, 243, 263 Mount, David, 285–86 MP3 players, 178 multidirectional platforms, 272–74 multihoming, 213–15, 223–28, 250–51, 297, 300 multinational corporations, 246–48 multi-sponsor decision-making, 139–40 multi-user feedback loop, 46, 100–101 music industry, 63, 71, 75, 87, 111, 134–35, 147, 178, 213, 226, 231, 258, 287, 297 MyFitnessPal, 75, 245 Myspace, 87, 92, 98, 125–26, 131–34, 132, 133, 135, 143, 204, 221, 226 Nakamoto, Satoshi, 171–73 Nalebuff, Barry J., 212 NASDAQ, ix, 80 National Transportation Safety Board (NTSB), 237 navigation tools, 191, 297 NBC, 204, 225 negative cross-side effects, 30–32, 34, 295 negative externalities, 163, 229–34, 257, 287 negative feedback, 28, 157–58, 166–67 negative network effects, 17, 26–32, 34, 47, 49, 51, 68, 112–15, 120, 121, 123, 126, 151, 229–34, 287, 298 negative same-side effects, 30, 298 Nest, 204, 225 Netflix, 63, 163, 204, 225 Netscape, 62, 110 network matching, 26–28 network orchestrators, 32 News Corp., 126 news feeds, 121, 168, 251–52 newspapers, 63, 144–45, 264, 287 New York City, 61, 113, 123, 229–30, 231, 258–59 New York State, 69–70, 274 New York Stock Exchange, 55, 171 New York Times, 205 NeXT, 53 Nigeria, 247 Nike, 4, 74–76, 78, 205, 271 9/11 attacks, 113 99designs, 66, 106 Nintendo, 94, 211, 240 noise, 28, 114, 120, 199, 200 Nokia, 49–50, 64, 131, 226 Novel Writing Month, 4–5 NTT, 89 oDesk, 201 oil and gas industry, 225, 235, 259, 263, 272 OkCupid, xi, 26–28, 30, 195–96 oligopolies, 209, 238 on-boarding effect, 90–91, 97 online courses, 96, 111, 265–68, 289 Open Data, 282 “open in” vs.

pages: 387 words: 112,868

Digital Gold: Bitcoin and the Inside Story of the Misfits and Millionaires Trying to Reinvent Money
by Nathaniel Popper
Published 18 May 2015

He understood that everyone on the network was trying to win the computational race with the central processing unit, or CPU, in his or her computer. But the CPU was also running most of the computer’s other basic systems, so it was not particularly efficient at computing hash functions. The graphics processing unit, or GPU, on the other hand, was custom-designed to do the kind of repetitive problem solving necessary to process images and video—similar to what was needed to win the hash race function. Laszlo quickly figured out how to route the mining process through his computer’s GPU. Laszlo’s CPU had been winning, at most, one block of 50 Bitcoins each day, of the approximately 140 blocks that were released daily.

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CHAPTER 19 March 2013 At the same time that Bitcoin’s reputation was getting a makeover in Silicon Valley, the physical infrastructure of the Bitcoin network was also undergoing an extensive transformation. For much of the previous year and a half, the computing power underpinning the network had grown steadily, but slowly. Over the course of 2012 the amount of computing power on the Bitcoin network barely doubled. What’s more, everyone was still relying on basically the same technology—graphic processing units, or GPUs—that had been introduced back in 2010 by Laszlo Hanecz, the buyer of the Bitcoin pizzas. By the end of 2012 there was the equivalent of about 11,000 GPUs working away on the network. But even back in 2010 it had been clear that if Bitcoin became more popular there was a logical next step that would eclipse GPUs.

pages: 501 words: 114,888

The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives
by Peter H. Diamandis and Steven Kotler
Published 28 Jan 2020

Turns out all those cat videos are fantastic for training an AI in image recognition and scene identification. All your Facebook likes and dislikes? Same thing. Put differently, a lot of people think social media is making us dumber, but it’s definitely making AI smarter. At the same time these data sets arrived, exceptionally cheap and incredibly powerful graphics processing units (or GPUs) started to flood the market. GPUs run the endlessly complicated graphics found in video games, but they also power AI. And the result of this relatively minor convergence—big data sets meeting cheap, potent GPUs—sparked one of the fastest invasions in history, with artificial intelligence starting to encroach on every facet of our lives.

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Eric, 63, 231 drones: disaster relief and, 48 increasing demand for, 10 package delivery and, 47–48, 107 reforestation and, 224, 227 drought, 242 drug development: AI and, 165–67 quantum computing and, 30, 167 Duplex (AI assistant), 35 DxtER, 157 dynamic risk, 187–89 Eagleman, David, 134 Easter Island (Rapa Nui), 174 Echo, 35, 101, 132 e-commerce revolution, 98–100 economy: new business models for, 83–87, 111–13 paradigm shifts in, 97–98 technological unemployment and, 227–30 ecosystems, ecosystem services, collapse of, 223–24 Edison, Thomas, 61 educational system: computer-aided self-teaching and, 144–47 customization of, 150 future of, 143–50 impact of exponential technologies on, 23 multisensory learning in, 148 outmoded models for, 143–44 standardized testing in, 144 teacher shortages in, 143 VR and, 51–52, 147–49, 248 e-governance, 234–35 Ekocenters, 214 electric cars, 10, 16–17, 221–23 electroencephalogram (EEG) sensors, 141 Elevian, 90, 178 emotional intelligence: AI and, 135–38 computers and, 103 robots and, 107 empathy, VR and, 148 endorphins, 247 energy: demonetization and, 78 new sources of, and economic paradigm shifts, 98 renewable, 78, 214, 215–18; see also specific technologies storage of, 218–20 Engines of Creation (Drexler), 63–64 Enlightenment, 82 entertainment: affective computing and, 136–38 content in, see content, entertainment future of, 125–42 streaming and, 126–27 entrepreneurship, immigrants and, 239–40 environment, as global and exponential, 12, 22–24 environmental threats, 211, 240 biodiversity crisis and, 48, 207, 212, 223–27 convergence and, 226–27 deforestation and, 48, 206, 207, 223, 224, 226 extreme weather and, 212, 223, 226 pollution and, 212, 226 water scarcity and, 212–15 see also climate change EOS token, 76 epigenetic alterations, 170 Essex, University of, 203 Estonia, e-governance in, 234–35 Ethereum, 187 Etherisc, 187 Ethiopia, Negroponte’s self-teaching experiment in, 144–46 evolution, trajectory of, 258–59 eVTOLs (electric vertical take-off and landing vehicles), 6, 9–10, 11 see also flying cars Exceptional People (Goldin and Cameron), 237–38 existential risks, 230–36, 240 Exo Imaging, 157 experience economy, 86, 111–13 exponential technologies, xi, 31–33, 39, 200, 215 abundance and, 261–63 brains as poorly adapted to, 12 convergence of, see convergence existential risks and, 230–36 healthcare and, 155, 156–67 Moore’s Law and, 7–8 Netflix and, 126 eXp Realty, 196–97 extreme weather, 212, 223, 226, 241–42 FAA, 6 Facebook, 51, 81, 256 advertising revenue of, 117–18 facial recognition, 120 fakes, fakery, digital, 121–23, 131–32 Federal Judicial Center, 49 Feynman, Richard, 63 Filecoin, 76 Final Frontier Medical Devices, 157 finance industry, 181–82 AI and, 194–96 blockchain and, 193, 194 convergence and, 189–96 Fintech, 194 Fitbit, 41–42 5G networks, 39–40, 119, 149 floating cities, 199–200 Floating Island Project, 200 FLOPS (floating operations per second), 28 Florida, Richard, 244 flow, flow states, 81, 257–58 VR and, 247–48 flow batteries, 219–20 Flow Research Collective, xii, 265 flying cars, 3–7, 26 convergence and, 9–12 healthcare and, 154 prime real estate redefined by, 199 ridesharing and, 4, 19 fMRI studies, 21–22 food chain, 202–3 food industry, 181, 201–8 Forbes, 35, 129 Ford, Henry, 12–13 Ford Motor Company, 221 foreign currency exchange, 194 Forest (quantum developer’s kit), 30, 32 Form Energy, 220 fossil fuels, 215–16 Foster, Richard, 23 Fox News, 247 free/data economy, 84–85 Freestyle Foundation Beverage Dispenser, 214 Friis, Janus, 106 Fukushima Daiichi nuclear plant disaster, 45 future, thinking about, 21–22 Gagarin, Yuri, 73 Garcetti, Eric, 20 Gates, Bill, 203, 214, 220 GDF11 (growth differentiation factor 11), 90, 178–79 Gelsinger, Jesse, 65–66 General Motors (GM), 14, 226 generative adversarial networks (GANs), 165–66, 167 gene sequencing, 78 gene therapy, 65–66, 67, 68 genetic diseases, 65–68 genetics, longevity and, 172–73 genius, nurturing of, 79–82 genome, 66–67 editing of, 67–68, 160 epigenetic alterations to, 170 instability in, 170 Genome Project-Write, 159 genomics, personalized, 158–60 Georgia Institute of Technology, 130 Germany, electric cars in, 221 Germany, Nazi, 238–39 germline engineering, 67–68 Giegel, Josh, 17–18 Gigafactory, 219, 222 GitHub, 146 Glaxo, 152 Global Learning XPRIZE, 146 Global Risks Report (World Economic Forum), 212 global warming, see climate change Gmail, Smart Compose feature of, 35 GM Cruise, 14 Go, 36 Goddard, Robert, 17 Goldin, Ian, 237–38 Goldman Sachs, 230 Good Money, 190–91 Google, 8, 36, 51, 71, 89, 100, 128, 146, 156 advertising revenue of, 117–18 Talk to Books program of, 35 see also Alphabet Google Duplex, 101–2 Google Home, 35 Google Lens, 120 Google Now, 100 governance, existential risks and, 234–36 GPS, 43 graphics processing units (GPUs), 34 Great Recession of 2008, 196, 228 greenhouse gases, 206, 207, 215–16, 221, 226 Gross, Neil, 42–43 group flow, 257–58 Groupon, 4 Guardian, 242, 246 Hagler, Brett, 55–56 Haiti, 58 2010 earthquake in, 55 Hanyecz, Laszlo, 57 haptic sensation, 25, 26, 134–35 Hardy, G.

Four Battlegrounds
by Paul Scharre
Published 18 Jan 2023

While AI models are often trained at large data centers, the lower compute requirements mean that inference can increasingly be done on edge devices, such as smartphones, IoT devices, intelligent video cameras, or autonomous cars. Both training and inference are done on computer chips, and advances in computing hardware has been fundamental to the deep learning revolution. Graphics processing units (GPUs) have emerged as a key enabler for deep learning because of their ability to do parallel computation (which is valuable for neural networks) better than traditional central processing units (CPUs). A McKinsey study estimated that 97 percent of deep learning training in data centers in 2017 used GPUs.

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ABBREVIATIONS ABC American Broadcasting Company ACE Air Combat Evolution ACLU American Civil Liberties Union AFWERX Air Force Works AGI artificial general intelligence AI artificial intelligence AIDS acquired immunodeficiency syndrome ALS amyotrophic lateral sclerosis (also known as Lou Gehrig’s disease) ASIC application-specific integrated circuit AU African Union AWACS airborne warning and control system AWCFT Algorithmic Warfare Cross-Functional Team BAAI Beijing Academy of Artificial Intelligence BBC British Broadcasting Corporation BERT Bidirectional Encoder Representations from Transformers BCE before common era C4ISR Command, Control, Communication, Cloud, Intelligence, Surveillance, and Reconnaissance CBC Canadian Broadcasting Corporation CBP Customs and Border Patrol CCP Chinese Communist Party CEIEC China National Electronics Import and Export Corporation CEO chief executive officer CFIUS Committee on Foreign Investment in the United States CIA Central Intelligence Agency CLIP Contrastive Language–Image Pretraining CMU Carnegie Mellon University COBOL common business-oriented language COVID coronavirus disease CPU central processing unit CSAIL Computer Science and Artificial Intelligence Laboratory DARPA Defense Advanced Research Projects Agency DC District of Columbia DDS Defense Digital Service DEA Drug Enforcement Administration DIU Defense Innovation Unit DIUx Defense Innovation Unit—Experimental DNA deoxyribonucleic acid DoD Department of Defense EOD explosive ordnance disposal EPA Environmental Protection Agency ERDCWERX Engineer Research and Development Center Works EU European Union EUV extreme ultraviolet FBI Federal Bureau of Investigation FedRAMP Federal Risk and Authorization Management Program FEMA Federal Emergency Management Agency FOUO For Official Use Only FPGA field-programmable gate arrays GAN generative adversarial network GAO Government Accountability Office GB gigabytes GDP gross domestic product GDPR General Data Protection Regulation GIF graphics interchange format GNP gross national product GPS global positioning system GPU graphics processing unit HA/DR humanitarian assistance / disaster relief HUD head-up display IARPA Intelligence Advanced Research Projects Activity ICE Immigration and Customs Enforcement IEC International Electrotechnical Commission IED improvised explosive device IEEE Institute for Electrical and Electronics Engineers IJOP Integrated Joint Operations Platform IoT Internet of Things IP intellectual property IP internet protocol ISIS Islamic State of Iraq and Syria ISO International Organization for Standardization ISR intelligence, surveillance, and reconnaissance ITU International Telecommunication Union JAIC Joint Artificial Intelligence Center JEDI Joint Enterprise Defense Infrastructure KGB Komitet Gosudarstvennoy Bezopasnosti (Комитет государственной безопасности) MAGA Make America Great Again MAVLab Micro Air Vehicle Lab MIRI Machine Intelligence Research Institute MIT Massachusetts Institute of Technology MPS Ministry of Public Service MRAP mine-resistant ambush protected NASA National Aeronautics and Space Administration NATO North Atlantic Treaty Organization NBC National Broadcasting Company NGA National Geospatial-Intelligence Agency NLG Natural Language Generation nm nanometer NOAA National Oceanic and Atmosphere Administration NREC National Robotics Engineering Center NSIC National Security Innovation Capital NSIN National Security Innovation Network NUDT National University of Defense Technology OTA other transaction authority PhD doctor of philosophy PLA People’s Liberation Army QR code quick response code R&D research and development RFP request for proposals RYaN Raketno Yadernoye Napadenie (Ракетно ядерное нападение) [nuclear missile attack] SEAL sea, air, land SMIC Semiconductor Manufacturing International Corporation SOFWERX Special Operations Forces Works SpaceWERX Space Force Works STEM science, technology, engineering, and mathematics TEVV test and evaluation, verification and validation TPU Tensor Processing Unit TRACE Target Recognition and Adaptation in Contested Environments TSA Transportation Security Administration TSMC Taiwan Semiconductor Manufacturing Company TTC Trade and Technology Council UAV unmanned aerial vehicle UK United Kingdom UN United Nations U.S.

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Able Archer, 287 academic espionage, 163–64 accidents, 255 ACE (Air Combat Evolution), 1–2, 222 ACLU (American Civil Liberties Union), 111, 113 Acosta, Jim, 128 Advanced Research Projects Agency, 72 Advanced Research Projects Agency-Energy, 40 adversarial examples, 239–44, 240f adversarial patches, 241–42, 242f Aether Committee, 159 Afghanistan, 45–46, 54, 255 African Union, 108 AFWERX (Air Force Works), 214 Agence France-Presse, 139 AGI (artificial general intelligence), 284 AI Global Surveillance Index, 109 AI Index, 333–34 airborne warning and control system (AWACS), 196 Air Combat Evolution (ACE), 1–2, 222 aircraft, 191, 255 aircraft availability rates, 197 aircraft carriers, 191–92 AI Research SuperCluster, 296 Air Force 480th ISR Wing, 54 Air Force Works (AFWERX), 214 airlines, 100 AI Task Force, 193–94 AI Technology and Governance conference, 177 AITHOS coalition, 136 alchemy, 232 algorithmic warfare, 53, 56, 58 Algorithmic Warfare Cross-Functional Team (AWCFT), See Project Maven algorithm(s), 288; See also machine learning computer vision, 202–3 efficiency, 51, 297–98 real world situations, vs., 230–36 in social media, 144–51 for surveillance, 82 training, 25 Alibaba, 37, 91, 212 Alibaba Cloud, 160 All-Cloud Smart Video Cloud Solution, 107 Allen, John, 280 Allen-Ebrahimian, Bethany, 82 Alphabet, 26, 296 AlphaDogfight, 1–3, 220–22, 257, 266, 272 AlphaGo, 23, 73, 180, 221, 266, 271, 274, 284, 298, 453, 454 AlphaPilot drone racing, 229–30, 250 AlphaStar, 180, 221, 269, 271, 441 AlphaZero, 267, 269–71, 284 Amazon, 32, 36, 215–16, 224 Deepfake Detection Challenge, 132 and facial recognition, 22–23 and Google-Maven controversy, 62, 66 and government regulation, 111 revenue, 297 AMD (company), 28 American Civil Liberties Union (ACLU), 111, 113 Anandkumar, Anima, 32, 120 Anduril, 66, 218, 224 Angola, 107, 108 Apollo Program, 297 Apple, 92, 95–96 application-specific integrated circuits (ASICs), 180 Applied Intuition, 224 arms race, 254, 257 Army Command College, 279 Army of None (Scharre), 196 artificial general intelligence (AGI), 284 artificial intelligence (AI) agents, 271 community, publication norms, 125 cost of, 296–97 ethics, 159 future of, 294–301 general, 284 as general-purpose enabling technology, 3–4 impact on economic productivity, 72–73 implementation, 31 indices, global, 15–17 narrowness, 233 outcomes, 299–301 regulation of, 111–13 safety, 286, 289, 304 specialized chips for, 28–29, 180, 185 “Artificial intelligence: disruptively changing the ‘rules of the game’” (Chen), 279 Artificial Intelligence Industry Alliance, 172 artificial intelligence (AI) systems future of, 294–301 humans vs., 263–75 limitations of, 229–37 roles in warfare, 273 rule-based, 230, 236 safety and security challenges of, 249–59 arXiv, 163 ASICs (application-specific integrated circuits), 180 ASML (company), 181 Associated Press, 139 Atari, 235 Atlantic, The, 173 atoms, in the universe, number of, 335 AUKUS partnership, 76 Austin, Lloyd, 292 Australia, 76, 108, 158, 182, 187 Australian Strategic Policy Institute, 82, 98, 158 Autodesk, 162 automated surveillance, 103 automatic target recognition, 56–58 automation bias, 263 autonomous cars, 23, 65 autonomous weapons, 61, 64–66, 256 autonomous weapons, lethal, 286 AWACS (airborne warning and control system), 196 AWCFT (Algorithmic Warfare Cross-Functional Team), See Project Maven Azerbaijan, 108 BAAI (Beijing Academy of Artificial Intelligence), 172, 455 backdoor poisoning attacks, 245 badnets, 246 BAE (company), 211 Baidu, 37, 92, 160, 172, 173, 212 Baise Executive Leadership Academy, 109 “Banger” (call sign), 1 Bannon, Steve, 295 Battle of Omdurman, 13 BBC, 138 BeiDou, 80 Beijing, 84, 92, 159 Beijing Academy of Artificial Intelligence (BAAI), 172, 455 Beijing AI Principles, 172, 173 Beijing Institute of Big Data Research, 157 Belt and Road Initiative, 105, 108–10 BERTLARGE, 294 Betaworks, 127–28 Bezos, Jeff, 215 biases, 234, 236 Biddle, Stephen, 219 Biden, Hunter, 131 Biden, Joe, and administration, 33–34, 147, 166–67, 184, 252, 292 big data analysis, 91 Bing, 160 Bin Salman, Mohammed, 141 biometrics, 80, 84; See also facial recognition “Bitter Lesson, The” (Sutton), 299 black box attacks, 240–41 blacklists, 99–100 BlackLivesMatter, 143, 148 “blade runner” laws, 121–22, 170 blind passes, 249 Bloomberg, 118 Bloomberg Government, 257 Boeing, 193, 216 Bolivia, 107 bots, 118, 121–22, 142, 144–49, 221 Bradford, Anu, 112 Bradshaw, Samantha, 141–42 brain drain, 31, 304 “brain scale” models, 300 Brands, Hal, 223 Brazil, 106, 107, 110 Breakfast Club, 53 Brexit referendum, 122 Bridges Supercomputer, 44 brinkmanship, 281 Brokaw, Tom, 143 Brooks, Rodney, 233 “brothers and sisters,” Han Chinese, 81 Brown, Jason, 54–55, 57, 201–3 Brown, Michael, 49, 196–97 Brown, Noam, 44, 48, 50 Bugs Bunny (fictional character), 231 Bureau of Industry and Security, 166 Burundi, 110 Buscemi, Steve, 130 Bush, George W., and administration, 68–70 ByteDance, 143 C3 AI, 196, 224 C4ISR (Command, Control, Communication, Cloud, Intelligence, Surveillance, and Reconnaissance), 107 CalFire, 201–2 California Air National Guard, 201, 203 Caltech, 32, 120 Cambridge Innovation Center, 135 cameras, surveillance, 6, 86–87, 91 Campbell, Kurt, 292 Canada, 40, 76, 158, 187 Capitol insurrection of 2021, 150 car bombs, 54–55 Carnegie Mellon University, 31–32, 45–46, 66, 193, 196, 207 Carnegie Robotics, 193 cars, self-driving, 23 Carter, Ash, 57 casualties, military, 255 CBC/Radio-Canada, 138 CCP, See Chinese Communist Party Ceaușescu, Nicolae, 345 CEIEC (China National Electronics Import and Export Corporation), 106 censorship, 175–76 centaur model, 263 Center for a New American Security, 36, 71, 222 Center for Data Innovation, 15 Center for Security and Emerging Technology, 33, 139, 162, 185, 298, 323 Center on Terrorism, Extremism, and Counterterrorism, 124 Central Military Commission, 292 Central Military Commission Science and Technology Commission, 36 central processing units (CPUs), 25 CFIUS (Committee on Foreign Investment in the United States), 179 C-5 cargo plane, 196 chance, 282 character of warfare, 280 checkers, 47 Chen Hanghui, 279 Chen Weiss, Jessica, 110 Chesney, Robert, 130 chess, 47, 267, 269, 271, 275 Chile, 107 China AI research of, 30 bots, 142 Central Military Commission Science and Technology Commission, 36 commercial tech ecosystem, 223 data privacy regulations of, 21–22 ethics standards, 171–75 High-End Foreign Expert Recruitment Program, 33 human rights abuses, 63 in industrial revolution, 12–13 internet use, 22 nuclear capabilities, 50 ranking in government strategy, 40 semiconductor imports, 29 synthetic media policies of, 140 technology ecosystem, 91–96 Thousand Talents Plan, 32 China Arms Control and Disarmament Association, 290 China Initiative, 164, 167 China National Electronics Import and Export Corporation (CEIEC), 106 China National Intellectual Property Administration (CNIPA), 353 China Security and Protection Industry Association, 91 China Telecom, 169 Chinese Academy of Sciences, 88, 158 Chinese Academy of Sciences Institute of Automation, 172 Chinese Communist Party (CCP) economic history, 85–86 human rights abuses, 79–80, 83 surveillance, 97–104, 174–77 Chinese graduate students in U.S., 31 Chinese military aggression, 76; See also People’s Liberation Army (PLA) AI dogfighting system, 257 and Google, 62–63 investments in weapons, 70 scientists in U.S., 5 and Tiananmen massacre, 68 U.S. links to, 157–58, 161, 166, 303 Chinese Ministry of Education, 162 Chinese People’s Institute of Foreign Affairs, 173 Chinese Talent Program Tracker, 33 chips, See semiconductor industry; semiconductors CHIPS and Science Act, 40, 180 Cisco, 109, 246 Citron, Danielle, 121, 130 Civil Aviation Industry Credit Management Measures, 100 Clarifai, 60–61, 63, 66, 224 Clark, Jack, 31, 117, 119–25 Clinton, Bill, and administration, 69–70, 97 CLIP (multimodal model), 295–96 cloud computing, 91, 215–16 CloudWalk, 105, 156, 389 CNIPA (China National Intellectual Property Administration), 353 COBOL (programming language), 204 cognitive revolution, 4 cognitization of military forces, 265 Colombia, 107 Command, Control, Communication, Cloud, Intelligence, Surveillance, and Reconnaissance (C4ISR), 107 command and control, 268 Commerce Department, 155–57, 166, 171, 184 Committee on Foreign Investment in the United States (CFIUS), 179 computational efficiency, 297–300 computational game theory, 47–50 compute, 25–29 control over, 27 global infrastructure, 178 hardware, 297–99 resources, size of, 294–96 trends in, 325 usage of, 26, 51 computer chips, See semiconductor industry; semiconductors Computer Science and Artificial Intelligence Laboratory (CSAIL), 156 computer vision, 55–57, 64, 224 Computer Vision and Pattern Recognition conference, 57 concentration camps, 81 confidence-building measures, 290–93 confinement, 82 content recommendations, 145 Cook, Matt, 203 cooperation, research, 303–4 Cornell University, 124 cost, of AI, 296–97 Côte d’Ivoire, 107 Cotton, Tom, 164 counter-AI techniques, 248 COVID pandemic, 74–75 CPUs (central processing units), 25 Crootof, Rebecca, 123 CrowdAI, 202, 224 CSAIL (Computer Science and Artificial Intelligence Laboratory), 156 Cukor, Drew, 57, 58–59 Customs and Border Patrol, 110–11 cyberattacks, 246 Cyber Grand Challenge, 195–96 Cybersecurity Law, 95, 174 “cyberspace,” 102 Cyberspace Administration of China, 99 cyber vulnerabilities, 238 adversarial examples, 239–44 data poisoning, 244–47 discovery, 195–96 model inversion attacks, 247 Czech Republic, 108 Dahua, 89, 156, 169, 353, 354–55, 388–89 Dalai Lama, 80 Dalian University of Technology, 212 DALL·E, 295 Darcey, Brett, 220, 249–50 DARPA (Defense Advanced Research Projects Agency), 1, 195, 210–13, 220 DARPA Squad X, 231, 233, 236 data, 18–24 explosion, 18–19 mapping, 204 open-source, 288 poisoning, 238, 244–47 privacy laws, 21–22, 111–12, 170–71, 174–77 storage, 91 usage, 51 Data Security Law, 95, 174 datasets publicly available, 139 reliance on, 323 training, see training datasets DAWNBench, 57 D-Day Invasion of Normandy, 46 dead hand, 289–90 Dead Hand, 447; See also Perimeter deception in warfare, 45 Deep Blue, 275 deepfake detection, 127, 132–33, 137–38 Deepfake Detection Challenge, 132–33 deepfake videos, 121, 130–32 deep learning, 2, 19, 31, 210, 236 Deep Learning Analytics, 209–13, 233 DeepMind, 23, 26, 32, 180, 221, 271–72, 295–96, 298–99, 441, 454 Deeptrace, 121, 130–33 defense acquisition policy, 217 Defense Advanced Research Projects Agency (DARPA), 1, 195, 210–13, 220 Defense Innovation Board, 65–66 Defense Innovation Unit (DIU), 35, 49, 57, 195–99, 214, 252 Defense One, 58 Defense Sciences Office, 231 defense start-ups, 222 Dell, 162 Deloitte, 246 Deng Xiaoping, 75, 85 Denmark, 108 Department of Defense, 35, 51–52, 56, 60–67, 70, 160, 166, 194 AI principles, 65–66 AI strategy, 249 budget, 297 contracts, 214–18 cyberattacks on, 246 innovation organizations, 198f reform, 225 Department of Energy, 246 Department of Energy’s Office of Science, 40 Department of Homeland Security, 246 Department of Justice, 164, 246 destruction, extinction-level, 282 deterrence, 51 DiCaprio, Leonardo, 130 Dick, Philip K., 81 dictator’s dilemma, 69 Didi, 92 digital devices, 18 DigitalGlobe, 204 Digital Silk Road, 110 DiResta, Renée, 139 disaster relief, 201, 204 disinformation, 117–26 AI text generation, 117–21 deepfake videos, 121 GPT-2 release, 123–24 Russian, 122 voice bots, 121–22 distributional shift, 233, 426 DIU, See Defense Innovation Unit (DIU) DNA database, 89–90 dogfighting, 1, 249–50, 272; See also Alpha Dogfight “Donald Trump neuron,” 295 Doom bots, 221 doomsday device, 282 Dota 2 (game), 26, 117, 267–72, 298 Dragonfly, 62 Drenkow, Nathan, 247 drone pilots, 223 drones, 229–30, 257, 286–87 drone video footage, 36, 53–56, 61, 65, 202–3; See also image processing; video processing drugs, 251 Dulles Airport, 110–11 Dunford, Joe, 62 Duplex, 121 Easley, Matt, 193 Eastern Foundry, 209 Economist, The, 18 Ecuador, 106 efficiency, algorithmic, 51 Egypt, 109 XVIII Airborne Corps at Fort Bragg, 194 elections, 122, 128, 129, 131, 134, 150 Elmer Fudd (fictional character), 231 Entity List, 155–57, 161, 163, 166–67, 171, 182, 184, 388–89 Environmental Protection Agency, 40 Erasmus University Medical Center, 158, 393–94 Esper, Mark, 67, 197, 205 espionage, 33, 163–64 Estonia, 108 “Ethical Norms for New Generation Artificial Intelligence,” 172 ethical use of technology, 140 ethics censorship, 175–76 Chinese standards, 171–75 data privacy, 176–77 international standards, 169–71 Ethiopia, 108 E-3 Sentry, 196 Europe AI research of, 30 in industrial revolution, 12–13 internet use, 22 and semiconductor market, 27 European Union, 76, 187 Europe Defender, 194 EUV (extreme ultraviolet lithography), 181 explainable AI, 237 export controls, 166–67, 181–86, 300 extinction-level destruction, 282 extreme ultraviolet lithography (EUV), 181 Eyes in the Sky (Michel), 54 F-35 stealth fighter jet, 254–55 Faber, Isaac, 193–94, 203 Face++, 88 Facebook account removal, 142 algorithms, 144–46 content moderation, 149 Deepfake Detection Challenge, 132 manipulated media policies of, 140 number of users, 22 and Trusted News Initiative, 139 face swapping, 121, 130–31 facial recognition attacks on, 241, 245 challenges in, 426 in China, 5–6, 80, 88–91, 103, 167 Chinese export of technology, 105–7 laws and policies for, 113, 159, 170 poor performance outside training data, 64–65 of Uighurs, 88–89, 158 in U.S., 22–23, 111, 159 fake news, 117–19, 122, 124–25 Falco (call sign), 1–2, 221, 226 Fan Hui, 298 FBI, 95–96, 164 Fedasiuk, Ryan, 162 Federal Emergency Management Agency (FEMA), 204 FedRAMP, 213 FEMA (Federal Emergency Management Agency), 204 Fidelity International, 157 field-programmable gate arrays (FPGAs), 180 “50 cent army,” 125 Fighting to Innovate (Kania), 222 filtering, of harmful content, 144 Financial Times, 157–58 Finland, 40, 187 fire perimeter mapping, 201–4 5G wireless networking, 37, 108, 182–83 Floyd, George, 143, 148 flu, H5N1 avian bird, 123 ForAllSecure, 196 Forbes magazine, 202 Ford, Harrison, 121 480th ISR Wing, 54 FPGAs (field-programmable gate arrays), 180 France, 40, 76, 108, 158, 187 Frazier, Darnella, 143 Frederick, Kara, 105 French Presidential election, 2017, 122 future, uncertainty of, 276 G7 group, 76, 187 Gab, 149 Gabon, 134 Gadot, Gal, 121 Game Changer, 206 games and gaming, 43–51, 266–73; See also specific games game trees, 47–49 GANs (generative adversarial networks), 127, 133 GAO, See Government Accountability Office (GAO) Garcia, Dominic, 203 Gates, Bill, 159 Gato, 295 GDP (gross domestic product), 69f, 85, 85f GDPR, See General Data Protection Regulation (GDPR) General Dynamics, 209, 212–13 generative adversarial networks (GANs), 127, 133 generative models, 125 genomics, 37 geopolitics, 129, 317 Germany, 12, 76, 107, 108, 158, 187 Gibson, John, 61 Gibson, William, 101, 102 Gizmodo, 120 Global AI Index, 15, 40 Global AI Vibrancy Tool, 319 go (game), 23, 47–48, 73, 180, 271, 275, 298 Golden Shield Project, 87 Goodfellow, Ian, 239 Google, 31, 32, 36, 57, 224, 294 and ASICs, 180 and Dragonfly, 339 Duplex, 121 Meena, 125 and Seven Sons of National Defense, 162 social app dominance, 143 and Trusted News Initiative, 139 work with Chinese researchers, 157, 392, 396 Google AI China Center, 62, 159, 167 Google Brain, 32, 294–96, 299 Google-Maven controversy, 22, 60–67 Google Photos, 64 Googleplex, 195 Google Translate, 234 Gorgon Stare, 53–55, 58 “Governance Principles for a New Generation of Artificial Intelligence,” 173 “Governance Principles for a New Generation of Artificial Intelligence: Develop Responsible Artificial Intelligence,” 172 Government Accountability Office (GAO), 195, 215, 217, 248 government contracting, 215–16, 222, 224–25 government-industry relationship, 95–96 government subsidies, 179–80 GPT-2 (language model), 20, 117–20, 122–25, 139, 294 GPT-3 (language model), 139, 294 GPUs (graphics processing units), 25, 28–29, 185, 296 Grace, Katja, 298 Great Britain, 191–92 Great Firewall, 62, 70, 102, 166 Great Gatsby, The (film), 130 Great Leap Forward, 85 Great Wall, 101 Greitens, Sheena, 105 Griffin, Michael, 200, 257 Guardian, The, 120, 148 Gulf War, 1991, 14, 219 HA/DR (humanitarian assistance/disaster relief), 201, 204 Hamad Bin Khalifa University, 142 Han Chinese, 81, 88 Harbin Institute of Technology, 161 hardware, computing, See compute Harvard University, 32 hashtags, 141 Hate Crimes in Cyberspace (Citron), 121 Heinrich, Martin, 37 Heritage Foundation, 105 Heron Systems in AlphaDogfight Trials, 1–2, 266, 272 background, 220–22 as defense start-up, 224 and real-world aircraft, 249–50 heuristics, 274 Hewlett Packard Enterprise, 157, 392 Hicks, Kathleen, 252 High-End Foreign Expert Recruitment Program, 33 Hikvision, 89, 91, 107, 156, 157, 353, 355, 389, 390 Hikvision Europe, 389 Himalayan border conflict, 75 Hindu, The, 139 Hinton, Geoffrey, 210 HiSilicon, 91 Hoffman, Samantha, 82, 98–99, 101, 102, 174 HoloLens, 160, 217 Honeywell, 162 Hong Kong, 75, 148, 175 Hoover Institution, 162 Horner, Chuck, 14 Howard, Philip, 141–42 Howell, Chuck, 250–51 Huawei, 29, 76, 88–89, 91, 92, 106–9, 169, 171, 182–85, 353, 354, 357, 409 Huawei France, 354 Huffman, Carter, 135–37 human cognition, 275 Human Genetics, 158 human intelligence, 284–85 humanitarian assistance/disaster relief (HA/DR), 201, 204 human-machine teaming, 263–64, 273, 276–86 human psychology, 274 human rights abuses, 63, 155, 158, 176–77 Human Rights Watch, 79, 81–82, 95, 170, 174 Hungary, 110 Hurd, Will, 39 Hurricane Dorian, 204 Husain, Amir, 66, 280 Hwang, Tim, 139, 323 hyperwar, 280 IARPA (Intelligence Advanced Research Projects Activity), 91, 246 IBM, 32, 109, 162, 215 IDG Capital, 157 IEC (International Electrotechnical Commission), 169 IEDs (improvised explosive devices), 45–46 IEEE (Institute for Electrical and Electronics Engineers), 171 iFLYTEK, 37, 91, 93–95, 104, 156, 157, 169 IJOP (Integrated Joint Operations Platform), 81–82 image classification systems, 64–65 image misclassification, 296 Imagen, 295 ImageNet, 19, 54, 210 image processing, 53–55, 58, 61 immigration policies, 33–34, 331 improvised explosive devices (IEDs), 45–46 iNaturalist, 211–12, 233 India, 75, 76, 108, 110, 187 bots, 142 in industrial revolution, 12–13 internet use, 22 industrial revolutions, 4–5, 11–13, 264–65 infant mortality, 85, 87f inference, 25, 180, 298 information processing, scale of, 269 information revolution, 14 insecure digital systems, 248 Institute for Electrical and Electronics Engineers (IEEE), 171 institutions, 35–40 Integrated Joint Operations Platform (IJOP), 81–82 Intel, 27, 29, 156, 162, 179, 181–82, 246, 390–91 intellectual property, 33, 71, 92, 163–64, 179 Intellifusion, 88, 156 intelligence, human, 284–85 intelligence, surveillance, and reconnaissance (ISR), 53–54 Intelligence Advanced Research Projects Activity (IARPA), 91, 246 intelligence analysis, 55 intelligentization of military, 37, 53, 222, 265 intelligentization of surveillance systems, 88 Intelligent Systems Center, 238, 247–48 Intelligent Trial System, 95 Intelligent UAV Swarm System Challenge, 36 international cooperation, 76 International Electrotechnical Commission (IEC), 169 International Organization for Standardization (ISO), 169 international stability, 286–93 international standard-setting, 169–71 International Telecommunication Union (ITU), 169 internet in China, 87, 92, 97, 99 data capacity of, 18 usage, 22 IP Commission, 164 iPhone encryption, 174 Iran, 142 Iraq, 45–46, 58, 253, 255–56 ISIS, 58, 63 ISO (International Organization for Standardization), 169 ISR (intelligence, surveillance, and reconnaissance), 53–54 Israel, 187, 278 IS’Vision, 156 Italy, 76, 108, 187 ITU (International Telecommunication Union), 169–70 JAIC (Joint AI Center), 35, 66, 200–208, 214, 289 jamming and anti-jamming strategies, 50 Japan, 27, 76, 108, 158, 181–82, 187 JASON scientific advisory group, 251 Javorsek, Dan “Animal,” 3, 230 jaywalking, 99 JEDI (Joint Enterprise Defense Infrastructure), 61, 214–18, 224 Jennings, Peter, 143 Johansson, Scarlett, 121, 130 Johns Hopkins University, 223 Johns Hopkins University Applied Physics Laboratory, 238, 247 Joint Enterprise Defense Infrastructure (JEDI), 61, 214–18, 224 “Joint Pledge on Artificial Intelligence Industry Self-Discipline,” 172 Jones, Marc Owen, 142 Jordan, 109 Joske, Alex, 158 Kania, Elsa, 36, 96, 222–24 Kasparov, Garry, 275 Katie Jones (fake persona), 131 Kaufhold, John, 209, 213 Kazakhstan, 108, 155–56 Keegan, John, 443 Ke Jie, 73 Kelly, Kevin, 4 Kelly, Robin, 39 Kennedy, Paul, 12, 13 Kenya, 107 Kernan, Joseph, 200 Kessel Run, 214 KFC, 92 KGB, 122 Khan, Saif, 185–86, 298 Khashoggi, Jamal, 141–42 kill chain, 263 Kim Jong-un, 131 King’s College London, 273 Kingsoft, 160 Kocher, Gabriel “Gab707,” 230 Komincz, Grzegorz “MaNa,” 270 Kovrig, Michael, 177 Krizhevsky, Alex, 210 Kuwait, 46 Lamppost-as-a-Platform, 107 language models, 20, 118–20, 124–25, 232, 234, 294; See also GPT-2; GPT-3; OpenAI Laos, 108 Laskai, Lorand, 96 Laszuk, Danika, 128, 140 Latvia, 108 Lawrence, Jennifer, 130 laws and regulations, 111–13 “blade runner,” 121–22, 170 data privacy, 21–22, 111–12, 170–71, 174–77 facial recognition, 113 and Microsoft, 111 for surveillance, 108–9 learning, unintended, 234 learning hacks, 234–35 Lebanon, 109 Lee, Kai-Fu, 22 Lee, Peter, 165, 167 legal reviews, 259 Le Monde, 108 Les, Jason, 46, 48 lethal autonomous weapons, 286 “liar’s dividend,” 130 Li Bin, 291 Libratus, 43–51, 266–67, 271 Libya, 109 Li Chijiang, 290–91 life expectancy, 85, 86f Li, Fei-Fei, 62 Lin Ji, 93–95, 104 Liu Fan, 393–94 LinkedIn, 131 lip-syncing, 130–31 lithography, extreme ultraviolet (EUV), 181 Liu He, 76 Liu Qingfeng, 156 Llorens, Ashley, 248, 249 Lockheed Martin, 1, 57, 211 London, 109 Long Kun, 291 long-term planning, 270 Lord, Ellen, 217 Lucky, Palmer, 66 Luo, Kevin, 161 Machine Intelligence Research Institute (MIRI), 298 machine learning and compute, 25–26, 32, 296–97 failure modes, 64, 232–33, 236–39, 243–44, 246–49 at Heron Systems, 220–21 opacity of algorithms, 145 and synthetic media, 127, 139 training data for, 202–5, 230 and voice synthesis, 137 at West Point, 194–95 MacroPolo, 30 Made in China 2025, 37, 183 Malaysia, 106 Management Action Group, 56 maneuver warfare, 442 Manhattan Project, 297 Mao Zedong, 85, 97 Marines, 231 marriage, coerced, 81 Martin, Rachael, 206 Martin Aspen (fake persona), 131 Massachusetts Institute of Technology (MIT), 31, 156, 157, 165, 233 Mattis, Jim, 53, 61, 197, 209, 215, 280 MAVLab (Micro Air Vehicle Lab), 250–52 Max Planck Society, 158, 393 McAulay, Daniel, 267 McCord, Brendan, 52, 56–57, 200 McKinsey, 25 McKinsey Global Institute, 72–73 McNair, Lesley, 192 McQuade, Michael, 66 media, AI-generated, 118–20 media conferences, 109 Meena, 125 Megatron-Turing NLG, 20, 294 Megvii, 88–89, 156, 160, 212, 353, 354, 357, 388 Memorandum of Understanding Regarding the Rules of Behavior for Safety of Air and Maritime Encounters, 292 Meng Wanzhou, 177 Merrill Lynch, 162 Meta, 22, 143, 296 metrics, 320 Mexico, 107 Michel, Arthur Holland, 54 Micron, 182 Microsoft, 294 China presence, 159 and computer vision, 57 and cyberattacks, 246–47 deepfake detection, 132, 138–39 and Department of Defense, 36, 62, 66, 215–17, 224–25 digital watermarks, 138 and facial recognition, 23, 111 financial backing of AI, 296–97 funding, 296 and Google-Maven controversy, 62, 66 and government regulation, 111 and ImageNet, 54 Megatron-Turing NLG, 20, 294 and OpenAI, 26 revenue, 297 and Seven Sons of National Defense, 162 and Trusted News Initiative, 139 work with Chinese researchers, 157, 393, 396 Microsoft Research, 31, 167 Microsoft Research Asia, 157–63, 165–67 Microsoft’s Asia-Pacific R&D Group, 161 Middlebury Institute, 124 military AI adoption, 35–37, 219–26 applications, 191–94 military capabilities, 47 military-civil fusion, 5, 95, 161–63 military competition, 304 military forces cognitization, 265 military organization, 278–79 military power, potential, 13 military tactics, future, 277 Ministry of Industry and Information Technology, 87 Ministry of Public Security, 87, 89–90, 158 Ministry of Public Security (MPS), 95 Ministry of Science and Technology, 172, 173 Minneapolis police, 143 minority identification technology, 88–89 “Minority Report, The” (Dick), 81 MIRI (Machine Intelligence Research Institute), 298 Missile Defense Agency, 218 MIT, See Massachusetts Institute of Technology (MIT) MITRE, 250 MIT-SenseTime Alliance on Artificial Intelligence, 156 MIT Technology Review, 93, 159 mobile devices, 18 Mock, Justin “Glock,” 2 model inversion attacks, 247 Modulate, 135–36, 138 monitoring and security checkpoints, 80 Moore’s law, 26, 28, 325 Morocco, 109 Mozur, Paul, 101, 102 MPS Key Lab of Intelligent Voice Technology, 95 MQ-9 Reaper, 53 Mulchandani, Nand, 207, 214, 217 multimodal models, 295–96 multiparty game theory, 50 mutism, 128 Mutsvangwa, Christopher, 105 NASA (National Aeronautics and Space Administration), 40, 72, 220 national AI research cloud, 32 National Artificial Intelligence Initiative Act of 2020, 32 National Artificial Intelligence Research Resource, 32 National Defense Education Act, 33 National Defense Strategy, 52 National Development and Reform Commission, 88 National Geospatial-Intelligence Agency (NGA), 56 National Institute of Standards and Technology, 40 National Institutes of Health, 40 National Instruments, 162 National Intelligence Law, 95, 174 National New Generation Artificial Intelligence Governance Expert Committee, 172 National Oceanic and Atmospheric Administration (NOAA), 40, 204 national power, 13, 318 National Robotics Engineering Center (NREC), 193 National Science Advisory Board for Biosecurity, 123 National Science Foundation, 40 National Security Agency, 216 National Security Commission on AI, 33, 39, 73, 186, 250, 252, 258 National Security Law, 95, 174 national security vulnerabilities, 239 National University of Defense Technology (NUDT), 157, 161 NATO, 287 natural language processing, 206 Nature (journal), 123 nature of war, 280–84 Naval Air Station Patuxent River, 220 Naval Research Laboratory, 162 Naval War College, 219 negative G turns, 249 Netherlands, 158, 181, 187 NetPosa, 156, 391 Neural Information Processing Systems, 232 neural networks, 19f, 25 applications, 54 badnets, 246 and Deep Learning Analytics, 210 explainability, 236–37 failure modes, 232–34, 250 and Heron Systems, 220 training, 19 NeurIPS, 30 Neuromancer (Gibson), 101 “New Generation Artificial Intelligence Development Plan,” 71, 169 New H3C Technologies, 157 “new oil,” 11–17 news articles, bot-generated, 118 new technologies, 255–56 new technologies, best use of, 191–92 New York Times, 31, 118, 125, 138, 290 NGA (National Geospatial-Intelligence Agency), 56 Nieman Journalism Lab, 145 1984 (Orwell), 97–98, 103 NIST (National Institute of Standards and Technology), 91 Nixon, Richard, and administration, 68 NOAA (National Oceanic and Atmospheric Administration), 40, 204 Nokia Bell Labs, 157 Normandy, France, 46 North Korea, 50, 117–18 Northrop Grumman, 57, 211, 216 NREC (National Robotics Engineering Center), 193 nuclear war, 288 nuclear weapons, 11, 50 NUDT (National University of Defense Technology), 157, 161 NVIDIA, 20, 28–29, 32, 120, 156, 246, 294, 390–91 Obama, Barack, and administration, 70, 71, 73, 137 object recognition and classification, 55–58 Office of Inspector General (OIG), 216 Office of Naval Research, 157 Office of Responsible AI, 159 Office of Technology Assessment, 162 OIG (Office of Inspector General), 216 oil, 20–21; See also “new oil” 160th Special Operations Aviation Regiment, 207 OpenAI, 26, 117–20, 122–25, 272, 294, 295–97, 299; See also GPT-2 (language model); GPT-3 (language model) OpenAI Five, 268, 270–71 Operation RYaN, 445; See also RYaN; VRYAN Oracle, 215–18, 224 Orwell, George, 97–98, 103 Osprey tiltrotor aircraft, 255 O’Sullivan, Liz, 60–61, 63, 65 OTA (other transaction authority), 217 outcomes of AI, 299–301 of war, 282–83 Owen, Laura Hazard, 145 Oxford Internet Institute, 141 Pakistan, 107, 142 Palantir, 109 PaLM, 294–95 Pan, Tim, 160, 161, 163 Papernot, Nicolas, 239 Pappas, Mike, 135–38, 140 Paredes, Federico, 250 Parler, 149 Partnership on AI, 132 patches, adversarial, 241–42, 242f Patrini, Giorgio, 130, 132–34, 137, 140 Patriot air and missile defense system, 253 Payne, Kenneth, 273–74 Pelosi, Nancy, 76, 128 Pence, Mike, 295 pension funds, 157 People’s Liberation Army (PLA); See also military-civil fusion affiliated companies, 166–67 and drone pilots, 222–23 researchers funded by, 158, 164 Percent Corporation, 107 Percipient.AI, 224 Perimeter, 289; See also Dead Hand Persian Gulf War, 46, 318 Personal Information Protection Law, 174, 176 pharmaceuticals, 251 phenotyping, DNA, 90 Philippines, 109 phones, 89 phone scanners, 89 photoresist, 182 phylogenic tree, 211 physical adversarial attacks, 242f, 243f, 429 Pichai, Sundar, 62 Pittsburgh, Pa., 44, 193 Pittsburgh Supercomputing Center, 44 PLA, See People’s Liberation Army Pluribus, 50, 51 poisonous animal recognition, 211 poker, 43–44, 46–48, 50, 266–67, 269–73, 335 Poland, 108 Police Audio Intelligent Service Platform, 95 Police Cloud, 89–90 policy analysis, automated, 206 Politiwatch, 124 pornography, 121, 130 Portman, Rob, 37 Poseidon, 289; See also Status-6 post-disaster assessment, 204 power metrics, 13 Prabhakar, Arati, 210 prediction systems, 287–88 predictive maintenance, 196–97, 201 Price, Colin “Farva,” 3 Primer (company), 224 Princeton University, 156, 157 Project Maven, 35–36, 52–53, 56–59, 194, 202, 205, 224; See also Google-Maven controversy Project Origin, 138 Project Voltron, 195–99 Putin, Vladimir, 9, 131, 304–5 Q*bert, 235 Quad summit, 76 Qualcomm Ventures, 157 Quantum Integrity, 132 quantum technology, 37 “rabbit hole” effect, 145 race to the bottom on safety, 286, 289, 304 radar, synthetic aperture, 210 Rahimi, Ali, 232 Raj, Devaki, 202, 207, 213, 224 Rambo (fictional character), 130 RAND Corporation, 252 ranking in government strategy, 40 Rao, Delip, 120, 123 Rather, Dan, 143 Raytheon, 211 reaction times, 272–73 real-time computer strategy games, 267–69 real-world battlefield environments, 264 situations, 230–36 Rebellion Defense, 224 Reddit, 140 reeducation, 81 Reface app, 130 reinforcement learning, 221, 232, 243, 250 repression, 81, 175–77 research and development funding, 35–39, 36f, 38f, 39f, 333–34 Research Center for AI Ethics and Safety, 172 Research Center for Brain-Inspired Intelligence, 172 research communities, 327 responsible AI guidelines, 252 Responsible Artificial Intelligence Strategy, 252 résumé-sorting model, 234 Reuters, 95, 139 Rise and Fall of the Great Powers, The (Kennedy), 12 risk, 271, 290–93 robotic nuclear delivery systems, 289 robotic process automation tools, 206 robotic vehicles, 266 robots, 92–94, 265–66, 286 Rockwell Automation, 162 Rockwell Collins, 193 Romania, 108 Root, Phil, 231 Roper, Will, 55–56, 214, 224, 225, 257 Rubik’s cube, 26 rule-based AI systems, 230, 236 Rumsfeld, Donald, 61 Russia, 12, 40, 52, 108, 110 bots, 142 cyberattacks of, 246 disinformation, 122 invasion of Ukraine, 129, 196, 219, 288 nuclear capabilities, 50 submarines, 255 Rutgers University Big Data Laboratory, 156 RYaN (computer program), 287, 445; See also Operation RYaN; VRYAN safe city technology, 107–8 safety of AI, 286, 289, 304 Samsung, 27–29, 179, 181 Sandholm, Tuomas, 43–51 Sasse, Ben, 184 satellite imagery, 56 Saudi Arabia, 40, 107, 109, 141–42 Scale AI, 224 scaling of innovation, 224 Schatz, Brian, 37 schedule pressures, 254–55 Schmidt, Eric, 39, 40, 71–73, 150, 164–65 Schumer, Chuck, 39 Science (journal), 123 Seagate, 156, 390 security applications, 110–11, 315 security dilemma, 50–51, 289 Sedol, Lee, 23, 266, 274–75, 298 self-driving cars, 23, 65 semiconductor industry; See also semiconductors in China, 178–79 chokepoints, 180–81 export controls, 181–86 global chokepoints in, 178–87 globalization of, 27–29 international strategy, 186–87 in Japan, 179 supply chains, 26, 76, 300 in U.S., 179–80 Semiconductor Manufacturing International Corporation (SMIC), 178, 181, 184 semiconductor(s) fabrication of, 32 foundries, 27–28 improvements in, 325 manufacturing equipment, 179 market, 27 as strategic asset, 300 Seminar on Cyberspace Management, 108–9 SenseNets, 91, 156, 357 SenseTime, 37, 88–89, 91, 156, 160, 169, 353–54, 357, 388 SensingTech, 88 Sensity, 130–33 Sentinel, 132 Sequoia, 157 Serbia, 107, 110 Serelay, 138 servicemember deaths, 255 Seven Sons of National Defense, 161–62 “shallow fakes,” 129 Shanahan, Jack on automated nuclear launch, 289 on international information sharing, 258, 291–92 and JAIC, 66, 201, 203, 205–6, 214 and Project Maven, 57–58 on risks, 254, 256 Sharp Eyes, 88, 91 Shenzhen, China, 37 Shield AI, 66, 196, 222, 224 shortcuts, 254–56 Silk Road, 110 SIM cards, 80, 89 Singapore, 106, 107, 158 singularity in warfare, 279–80 Skyeye, 99 Skynet, 87–88, 90, 91 Slashdot, 120 Slate, 120 smartphones, 26, 80 SMIC (Semiconductor Manufacturing International Corporation), 178, 181, 184 Smith, Brad, 159, 163, 166, 167 social app dominance, 149–50 social credit system, 99–100 social governance, 97–104 social media, 126, 141–51 socio-technical problems, 65 soft power, 317 SOFWERX (Special Operations Forces Works), 214 SolarWinds, 246 South Africa, 107 South China Sea militarization, 71, 74 South Korea, 27, 40, 182, 185, 187 Soviet Union, 287, 289, 447 Spain, 40, 107 SparkCognition, 66, 224 Spavor, Michael, 177 Special Operations Command, 218 Special Operations Forces Works (SOFWERX), 214 speech recognition, 91 “Spider-Man neuron,” 295 Springer Nature, 158 Sputnik, 33, 71–72 Stability AI, 125, 295 stability, international, 286–93 Stable Diffusion, 125, 139, 295 Stallone, Sylvester, 130 Stanford Internet Observatory, 139 Stanford University, 31, 32, 57, 162 Starbucks, 92 StarCraft, 180, 298 StarCraft II, 267, 271, 441 Status-6, 289; See also Poseidon Steadman, Kenneth A., 192 STEM talent, 30–34 sterilization and abortion, 81 Strategic Capabilities Office, 56 strategic reasoning, 49 Strategy Robot, 44–45, 49, 51 Strike Hard Campaign, 79–80 Stuxnet, 283 subsidies, government, 179–80 Sullivan, Jake, 186 Sun Tzu, 45 superhuman attentiveness, 269–70 superhuman precision, 270 superhuman reaction time, 277 superhuman speed, 269, 271 supervised learning, 232 supply chain(s), 300 attacks, 246 global, 76, 179, 183 “Surprising Creativity of Digital Evolution, The,” 235 surveillance, 79–90 cameras, 6, 86–87, 91 laws and policies for, 108–9 throughout China, 84–90 in Xinjiang, 79–83 Sutskever, Ilya, 210 Sutton, Rich, 299, 455 swarms and swarming, 277–79 autonomous systems, 50, 220 demonstrations, 257 Sweden, 108, 158, 187 Switch-C, 294 Synopsys, 162 synthetic aperture radar, 210 synthetic media, 127–34, 138–39 criminal use, 128–29 deepfake detectors, 132–33 deepfake videos, 130–32 geopolitical risks, 129–30 watermarks, digital, 138–39 Syria, 58 system integration, 91 tactics and strategies, 270 Taiwan, 27, 71, 76, 100, 175, 178, 185–86 Taiwan Semiconductor Manufacturing Company (TSMC), 27–28, 179, 181, 184 Taiwan Strait, 71, 75–76 talent, 30–34, 304 Tang Kun, 393 tanks, 192 Tanzania, 109 targeting cycle, 263 target recognition, 210 Target Recognition and Adaptation in Contested Environments (TRACE), 210–12 Tay, chatbot, 247 TDP (thermal design power), 454 TechCrunch, 120 technical standards Chinese, 171–75 international, 169–71 techno-authoritarianism, 79–110, 169 China’s tech ecosystem, 91–96 global export of, 105–10, 106f social governance, 97–104 throughout China, 83–90 in Xinjiang, 79–83 technology ecosystem, Chinese, 91–96 platforms, 35 and power, 11 transfer, 33, 163–64 Tektronix, 162 Tencent, 37, 143, 160, 169, 172 Tensor Processing Unit (TPU), 180 Terregator, 193 Tesla, 65, 180 TEVV (test and evaluation, verification and validation), 251–52 Texas Instruments, 162 text generation, 117–21, 123 text-to-image models, 125, 295 Thailand, 107, 109 thermal design power (TDP), 454 Third Offset Strategy, 53, 61 “Thirteenth Five-Year Science and Technology Military-Civil Fusion Special Projects Plan,” 73 Thousand Talents Plan, 32, 164 “Three-Year Action Plan to Promote the Development of New-Generation AI Industry,” 73 Tiananmen Square massacre, 68, 97–98, 103, 148, 160, 341, 359 tic-tac-toe, 47, 336 TikTok, 146–49 Tortoise Market Research, Inc., 15, 40 TPU (Tensor Processing Unit), 180 TRACE (Target Recognition and Adaptation in Contested Environments), 210–12 Trade and Technology Council (TTC), 187 training costs, 296–97 training datasets, 19–23 attacks on, 238–40, 244–45 of drone footage, 203 “radioactive,” 139 real world environments, vs., 58, 64, 233, 264 size of, 294–96 transistor miniaturization, 28 transparency among nations, 258–59, 288 Treasury Department, 246 Trump, Donald, and administration; See also “Donald Trump neuron” budget cuts, 39–40 and COVID pandemic, 74 and Entity List, 166 GPT-2 fictitious texts of, 117–19 graduate student visa revocation, 164 and Huawei, 182–84 and JEDI contract, 215–16 national strategy for AI, 73 relations with China, 71 and TikTok, 147 Twitter account, 150 trust, 249–53 Trusted News Initiative, 138–39 “truth,” 130 Tsinghua University, 31, 93, 173, 291 TSMC, See Taiwan Semiconductor Manufacturing Company (TSMC) TTC (Trade and Technology Council), 187 Turkey, 107, 108, 110 Turkish language, 234 Twitter, 139–40, 142, 144, 149, 247 Uganda, 108, 109 Uighurs; See also Xinjiang, China facial recognition, 88–89, 158, 353–55 genocide, 79, 304 mass detention, 74, 79–81, 102, 175 speech recognition, 94 surveillance, 82, 155–56 Ukraine, 108, 129, 196, 219, 288 United Arab Emirates, 107, 109 United Kingdom, 12, 76, 108, 122, 158, 187, 191–92 United States AI policy, 187 AI research of, 30 Chinese graduate students in, 31 competitive AI strategy, 185 United States Presidential election, 2016, 122 United States Presidential election, 2020, 128, 131, 134, 150 University of Illinois, 157 University of Richmond, 123 Uniview, 89, 355 unsupervised learning, 232 Ürümqi, 80, 84 Ürümqi Cloud Computing Center, 156 U.S.

pages: 144 words: 43,356

Surviving AI: The Promise and Peril of Artificial Intelligence
by Calum Chace
Published 28 Jul 2015

One of the most promising approaches to computer vision at the moment is “convolutional neural nets”, in which a large number of artificial neurons are each assigned to a tiny portion of an image. It is an interesting microcosm of the whole field of machine learning in that it was first invented in 1980, but did not become really useful until the 21st century when graphics processing unit (GPU) computer chips enabled researchers to assemble very large networks. Rather than overtly seeking to build a conscious mind, most practitioners are seeking to emulate particular intellectual skills at which humans have traditionally beaten computers. There are notable exceptions, such as Doug Lenart, whose Cyc project has been trying to emulate common sense since 1984, and Ben Goertzel, whose OpenCog project is attempting to build an open source artificial general intelligence system.

The Icon Handbook
by Jon Hicks
Published 23 Jun 2011

There are currently two solutions to this problem: packaging multiple different-sized bitmaps in one container format such as .ico or .icns; or a single vector file such as PDF or SVG. It would seem that a vector format would be ideal for icons: after all, a single file could be resized without losing quality. However, the more complex a vector image is, the larger its file size and the longer it takes for a graphics processing unit to process it. Bitmap images need much less processing, though, and for complex illustrations the file size can be dramatically smaller too. It’s also not always possible to create one vector image that can be scaled up or down for anything particularly complex. The larger an icon is, the more detail it potentially needs, and the smaller it is, the less it needs.

pages: 472 words: 117,093

Machine, Platform, Crowd: Harnessing Our Digital Future
by Andrew McAfee and Erik Brynjolfsson
Published 26 Jun 2017

Technology entrepreneur Elliot Turner estimates that the computing power required to execute a cutting-edge machine learning project could be rented from a cloud computing provider like Amazon Web Services for approximately $13,000 by the fall of 2016. Oddly enough, the popularity of modern video games has also been a great boost to machine learning. The specialized graphics processing units (GPUs) that drive popular gaming consoles turn out to be extremely well suited to the kinds of calculations required for neural networks, so they’ve been drafted in large numbers for this task. AI researcher Andrew Ng told us that “the teams at the leading edge do crazy complicated things in the GPUs that I could never imagine two or three years ago.”

…

Louis, 163 fees Stripe, 172–73 in two-sided networks, 215 fiat currencies, 280, 286, 305 FICO scores, 46–47 file sharing platforms, 144–45 film photography, 131 financial crisis (2008), 285, 308 financial services automated investing, 266–70 crowdlending platforms, 263 as least-trusted industry, 296 and regulation, 202 TØ.com, 290 virtualization of, 91 find-fix-verify, 260 firms economics of, 309–12 theory of, See TCE (transaction cost economics) FirstBuild, 11–14 Fitbit, 163 5G wireless technology, 96 fixed costs, 137 flat hierarchy, 325 Fleiss, Jennifer, 187 Flexe, 188 focus groups, 189–90 “food computers,” 272 food preparation recipes invented by Watson, 118 robotics in, 93–94 Forbes magazine, 303 forks, operating system, 244 Forsyth, Mark, 70 “foxes,” 60–61 fraud detection, 173 “free, perfect, instant” information goods complements, 160–63 economics of, 135–37 free goods, complements and, 159 freelance workers, 189 free market, See market “freemium” businesses, 162 Friedman, Thomas, 135 Friendster, 170 Fukoku Mutual Life, 83 Gallus, Jana, 249n garments, 186–88 Garvin, David, 62 Gazzaniga, Michael, 45n GE Appliances, 15 Gebbia, Joe, 209–10 geeky leadership, 244–45, 248–49 gene editing, 257–58 General Electric (GE), 10–15, 261 General Growth Properties, 134 General Theory of Employment, Interest, and Money, The (Keynes), 278–79 generative design, 112–13 genome sequencing, 252–55, 260–61 Georgia, Republic of, 291 Gershenfeld, Neil, 308 GFDL, 248 Gill, Vince, 12n Giuliano, Laura, 40 global financial crisis (2008), 285, 308 GNU General Public License (GPL), 243 Go (game), 1–6 Goethe, Johann Wolfgang von, 178 Go-Jek, 191 golden ratio, 118 Goldman Sachs, 134 gold standard, 280n Goodwin, Tom, 6–10, 14 Google, 331; See also Android acquiring innovation by acquiring companies, 265 Android purchased by, 166–67 Android’s share of Google revenue/profits, 204 autonomous car project, 17 DeepMind, 77–78 hiring decisions, 56–58 iPhone-specific search engine, 162 and Linux, 241 origins of, 233–34 and self-driving vehicles, 82 as stack, 295 Google AdSense, 139 Google DeepMind, 4, 77–78 Google News, 139–40 Google search data bias in, 51–52 incorporating into predictive models, 39 Graboyes, Robert, 274–75 Granade, Matthew, 270 Grant, Amy, 12n graphics processing units (GPUs), 75 Great Recession (2008), 285, 308 Greats (shoe designer), 290 Grid, The (website design startup), 118 Grokster, 144 Grossman, Sandy, 314 group drive, 20, 24 group exercise, See ClassPass Grove, William, 41 Grubhub, 186 Guagua Xiche, 191–92 gut instincts, 56 gyro sensor, 98 Haidt, Jonathan, 45 Hammer, Michael, 32, 34–35, 37, 59 hands, artificial, 272–75 Hannover Messe Industrial Trade Fair, 93–94 Hanson, Robin, 239 Hanyecz, Laszlo, 285–86 Hao Chushi, 192 “hard fork,” 304–5, 318 Harper, Caleb, 272 Hart, Oliver, 313–15 Hayek, Friedrich von, 151, 235–39, 279, 332 health care, 123–24 health coaches, 124, 334 health insurance claims, 83 Hearn, Mike, 305–6 heat exchangers, 111–13 “hedgehogs,” 60–61 Hefner, Cooper, 133 Hefner, Hugh, 133 hierarchies flat, 325 production costs vs. coordination costs in, 313–14 Hinton, Geoff, 73, 75–76 HiPPOs (highest-paid person’s opinions), 45, 63, 85 hiring decisions, 56–58 Hispanic students, 40 HIStory (Michael Jackson), 131 hive mind, 97 HMV (record store chain), 131, 134 Holberton School of Software Engineering, 289 “hold-up problem,” 316 Holmström, Bengt, 313, 315 Honor (home health care platform), 186 hotels limits to Airbnb’s effects on, 221–23 Priceline and, 223–24 revenue management’s origins, 182 “hot wallet,” 289n housing sales, 39 Howell, Emily (music composition software), 117 Howells, James, 287 Hughes, Chris, 133 human condition, 121, 122 human genome, 257–58 human judgment, See judgment, human Hyman, Jennifer, 187 hypertext, 33 IBM; See also Watson (IBM supercomputer) and Linux, 241 System/360 computer, 48 ice nugget machine, 11–14 idAb algorithm, 253, 254 incentives, ownership’s effect on, 316 incomplete contracting, 314–17 incremental revenue, 180–81 incumbents advantages in financial services, 202 inability to foresee effects of technological change, 21 limits to disruption by platforms, 221–24 platforms’ effect on, 137–48, 200–204 threats from platform prices, 220–21 Indiegogo, 13–14, 263, 272 industrial trusts, 22–23 information business processes and, 88–89 in economies, 235–37 O2O platforms’ handling of, 192–93 information asymmetries, 206–10 information goods bundling, 146–47 as “free, perfect, instant,” 135–37 and solutionism, 297–98 information transfer protocols, 138 infrared sensors, 99 InnoCentive, 259 innovation crowd and, 264–66 ownership’s effect on, 316 Instagram, 133, 264–66 institutional investors, 263 Intel, 241, 244 Internet as basis for new platforms, 129–49 economics of “free, perfect, instant” information goods, 135–37 evolution into World Wide Web, 33–34 in late 1990s, 129–31 as platform of platforms, 137–38 pricing plans, 136–37 intuition, See System 1/System 2 reasoning inventory, perishing, See perishing/perishable inventory investing, automated, 266–70 investment advising, 91 Iora Health, 124, 334 Iorio, Luana, 105 iOS, 164–67, 203 iPhone apps for, 151–53, 161–63 Blackberry vs., 168 curation of apps for, 165 demand curve for, 156 introduction of, 151–52 and multisided markets, 218 opening of platform to outside app builders, 163–64 user interface, 170 widespread adoption of, 18 iron mining, 100 Irving, Washington, 252 Isaac, Earl, 46 Isaacson, Walter, 152, 165 iteration, 173, 323; See also experimentation iTunes, 217–18 iTunes Store, 145, 165 Jackson, Michael, 131 Java, 204n Jelinek, Frederick, 84 Jeopardy!

pages: 487 words: 124,008

Your Face Belongs to Us: A Secretive Startup's Quest to End Privacy as We Know It
by Kashmir Hill
Published 19 Sep 2023

But there were limits to what Ton-That could do, both because he wasn’t a machine learning expert and because he didn’t have the best equipment for the job. Part of the reason that neural networks had come into their own was the development of new hardware, including powerful computer chips called graphics processing units, or GPUs, that had been developed for video gaming but that turned out to be incredibly useful for training deep-learning neural networks. Ton-That couldn’t afford state-of-the-art hardware, but luckily for him, he met someone who could access it for free: Smartcheckr’s most important early collaborator, a brilliant mathematician named Terence Z.

…

It didn’t need to be the first company to come out with a world-changing people-identifying product. It could afford to bide its time until some other company broke that particular taboo. Skip Notes *1 A key component to neural nets technology was powerful hardware; luckily for Taigman, Facebook had a few graphics processing units, or GPUs, sitting around, given to Facebook for free by Nvidia, the main company that made them. It was enough to get him started. *2 Why DeepFace? Another term for neural networks is “deep learning,” inspired not by a computer’s profound thoughts but by the “layers” of a neural network, which allow the network to compute increasingly complex representations of an image.

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
by Valliappa Lakshmanan , Sara Robinson and Michael Munn
Published 31 Oct 2020

The serving framework is architected to process an individual request synchronously and as quickly as possible, as discussed in “Design Pattern 16: Stateless Serving Function”. The serving infrastructure is usually designed as a microservice that offloads the heavy computation (such as with deep convolutional neural networks) to high-performance hardware such as tensor processing units (TPUs) or graphics processing units (GPUs) and minimizes the inefficiency associated with multiple software layers. However, there are circumstances where predictions need to be carried out asynchronously over large volumes of data. For example, determining whether to reorder a stock-keeping unit (SKU) might be an operation that is carried out hourly, not every time the SKU is bought at the cash register.

…

Gaussian process, Bayesian optimization genetic algorithms, Trade-Offs and Alternatives, Genetic algorithms-Genetic algorithms GitHub Actions, Integrating CI/CD with pipelines GitLab Triggers, Integrating CI/CD with pipelines GKE, Solution, Running the pipeline on Cloud AI Platform GLoVE, Context language models Google App Engine, Create web endpoint Google Bolo, Standalone single-phase model Google Cloud Functions, Create web endpoint Google Cloud Public Datasets, Data and Model Tooling Google Container Registry, Running the pipeline on Cloud AI Platform Google Kubernetes Engine (see GKE) Google Translate, Standalone single-phase model GPU, Problem, Problem-Synchronous training, ASICs for better performance at lower cost, Minimizing I/O waits, Problem, Running the pipeline on Cloud AI Platform, Transformational phase: Fully automated processes Gradient Boosting Machines, Boosting gradient descent (see SGD) graphics processing unit (see GPU) grid search, Grid search and combinatorial explosion-Grid search and combinatorial explosion, Why It Works Grid-SearchCV, Grid search and combinatorial explosion ground truth label, Data and Feature Engineering, Data Quality, Capturing ground truth-Why It Works H hash bucketscollisions, Bucket collision empty, Empty hash buckets heuristic to choose numbers, Out-of-vocabulary input Hashed Feature design pattern, Data Representation Design Patterns, Design Pattern 1: Hashed Feature-Empty hash buckets, Pattern Interactions Helm, Richard, What Are Design Patterns?

pages: 296 words: 66,815

The AI-First Company
by Ash Fontana
Published 4 May 2021

one-off self-learning Data acquisition Manually fetch Automatically fetch through a direct database connection Data preparation None—pick a clean dataset Clean and label multiple datasets Storage Local Cloud Data pipeline One pipeline Many pipelines Feature development Find one feature Try many features Training One calculation Many calculations Computation Local central processing unit (CPU) or graphics processing unit (GPU) Cloud GPUs Modeling One model Network of models Deployment Local Cloud Presentation Print a report Build an interface Answering one question means working with one dataset that is likely to have the answer to that question, not gathering data from multiple sources and then cleaning (and labeling) that data.

pages: 611 words: 188,732

Valley of Genius: The Uncensored History of Silicon Valley (As Told by the Hackers, Founders, and Freaks Who Made It Boom)
by Adam Fisher
Published 9 Jul 2018

David Levitt: Our hardware engineer took apart a Sharper Image TV, a little Sony TV that was relatively uncommon in those days, and reverse-engineered until we found the video signal and said, “Yes, we can make a Silicon Graphics machine drive that.” Jim Clark: Silicon Graphics was a company that primarily made what would now be called GPUs—special purpose graphics processing units—to accelerate graphics, so that people who used our equipment could visualize the models that they made. David Levitt: We used two Silicon Graphics machines, one for each eye, of course. So that became the first EyePhone. Jaron Lanier: EyePhone, E-Y-E, obviously. Mitch Altman: Our resolution was superlow: 480 by 680.

…

He found this guy who had an idea and really made it happen. Jim Clark: At the end of ’93 I was just finishing my twelfth year after founding and starting Silicon Graphics with a group of my graduate students from Stanford. SGI was a company that primarily made what would now be called a graphics processing unit. The company grew to be quite large, $4 billion a year and ten thousand employees. Marc Andreessen: Silicon Graphics at that time was what Google is today—the best technology company in the Valley. It was the one that everyone wanted to work at. They were just phenomenal technologists with phenomenal products.

pages: 477 words: 75,408

The Economic Singularity: Artificial Intelligence and the Death of Capitalism
by Calum Chace
Published 17 Jul 2016

[cxxi] http://www.popularmechanics.com/technology/a18493/stanford-3d-computer-chip-improves-performance/ [cxxii] http://gadgets.ndtv.com/science/news/mit-builds-low-power-artificial-intelligence-chip-for-smartphones-799803 [cxxiii] http://www.engadget.com/2016/03/28/ibm-resistive-processing-deep-learning/ [cxxiv] http://arstechnica.com/gadgets/2016/04/nvidia-tesla-p100-pascal-details/ [cxxv] CPU stands for Central Processing Unit. They are general purpose processors which can carry out many kinds of computation, but are not necessarily optimised for any of them. GPU stands for Graphics Processing Unit, and as the name suggests, they were originally designed for displaying graphics in video games. They are very good at taking huge quantities of data and carrying out the same operation over and over again. It turns out that machine learning benefits from their particular capabilities. CPUs and GPUs are often deployed in tandem.

pages: 1,136 words: 73,489

Working in Public: The Making and Maintenance of Open Source Software
by Nadia Eghbal
Published 3 Aug 2020

Torvalds’s mailing list messages are filled with expletives, sometimes in all caps. The examples are too numerous to include, but here’s one: In 2012, he gave a talk at Aalto University in Espoo, Finland. In response to a question about Nvidia’s lack of support for Linux (Nvidia is a manufacturer of graphics processing units, or GPUs), he turned to the camera, gave it the middle finger, and growled, “Nvidia, fuck you!”21 It’s not just Torvalds’s communication skills but also his governance style that helped him gain notoriety. In one of his essays, Raymond called this style “benevolent dictator,”23 which was later adapted by Guido van Rossum, author of the Python programming language, into the better-known phrase “Benevolent Dictator for Life” (BDFL), to describe authors of open source projects who retain control even as the project grows.

pages: 296 words: 86,610

The Bitcoin Guidebook: How to Obtain, Invest, and Spend the World's First Decentralized Cryptocurrency
by Ian Demartino
Published 2 Feb 2016

The three most popular algorithms are SHA-256, Scrypt, and X11. SHA-256 is Bitcoin’s algorithm and can be seen as the “basic” algorithm of cryptocurrencies—although it is anything but simple. Scrypt was the community’s first attempt at heading off GPU mining rigs (i.e., mining rigs with multiple, powerful graphics processing units) and ASICs, and it is used by Litecoin and dozens of other currencies. Despite its best attempts, Scrypt ASICs have still managed to hit the market. X11, along with its offshoots X5, X6, X12, etc., is a CPU/GPU hybrid that is again designed to slow the growth of ASIC miners; its most popular coin is Dash (formerly Darkcoin).

pages: 321

Finding Alphas: A Quantitative Approach to Building Trading Strategies
by Igor Tulchinsky
Published 30 Sep 2019

Every day, professional investors run huge numbers of simulations on historical data to seek patterns of price moves, using supercomputers, clusters, and now the cloud. The risk of overfitting, or the discovery of spurious relationships, is especially high given the enormous computational power of modern graphics-processing units. When you see especially good simulation results, you need to be careful to evaluate the overfitting risk of the models. Suppose that a researcher is looking to identify at least one two-year- long backtesting period with an annualized Sharpe ratio higher than 1. If he tries enough strategy configurations, he will eventually find one even if the strategies are actually random, with an expected out-of-sample Sharpe ratio of 0.

pages: 288 words: 86,995

Rule of the Robots: How Artificial Intelligence Will Transform Everything
by Martin Ford
Published 13 Sep 2021

The effectiveness of this distributed computing platform as a delivery vehicle for AI is being dramatically improved by the introduction of a range of hardware and software specifically designed to optimize deep neural networks. This evolution began with the discovery that special graphics microprocessors, used primarily to make fast-action video games possible, were a powerful accelerant for deep learning applications. Graphics processing units, or GPUs, were originally designed to turbocharge the computations required to almost instantaneously render high-resolution graphics. Beginning in the 1990s, these specialized computer chips were especially important in high-end video game consoles, such as the Sony PlayStation and Microsoft Xbox.

pages: 2,466 words: 668,761

Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig
Published 14 Jul 2019

We also see new hardware designs based on the idea that in dealing with an uncertain world, we don’t need 64 bits of precision in our numbers; just 16 bits (as in the bfloat16 format) or even 8 bits will be enough, and will enable faster processing. We are just beginning to see hardware tuned for AI applications, such as the graphics processing unit (GPU), tensor processing unit (TPU), and wafer scale engine (WSE). From the 1960s to about 2012, the amount of computing power used to train top machine learning applications followed Moore’s law. Beginning in 2012, things changed: from 2012 to 2018 there was a 300,000-fold increase, which works out to a doubling every 100 days or so (Amodei and Hernandez, 2018).

…

A final reason for describing CNNs in terms of tensor operations is computational efficiency: given a description of a network as a sequence of tensor operations, a deep learning software package can generate compiled code that is highly optimized for the underlying computational substrate. Deep learning workloads are often run on GPUs (graphics processing units) or TPUs (tensor processing units), which make available a high degree of parallelism. For example, one of Google’s third-generation TPU pods has throughput equivalent to about ten million laptops. Taking advantage of these capabilities is essential if one is training a large CNN on a large database of images.

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It is possible that a big jump in model quality will occur when it becomes economical to process all the video on the Web; for example, the YouTube platform alone adds 300 hours of video every minute. Moore’s law has made it more cost effective to process data; a megabyte of storage cost $1 million in 1969 and less then $0.02 in 2019, and supercomputer throughput has increased by a factor of more than 1010 in that time. Specialized hardware components for machine learning such as graphics processing units (GPUs), tensor cores, tensor processing units (TPUs), and field programmable gate arrays (FPGAs) are hundreds of times faster than conventional CPUs for machine learning training (Vasilache et al., 2014; Jouppi et al., 2017). In 2014 it took a full day to train an ImageNet model; in 2018 it takes just 2 minutes (Ying et al., 2018).

pages: 305 words: 93,091

The Art of Invisibility: The World's Most Famous Hacker Teaches You How to Be Safe in the Age of Big Brother and Big Data
by Kevin Mitnick , Mikko Hypponen and Robert Vamosi
Published 14 Feb 2017

It’s no wonder his e-mails were hacked and spread across the Internet since the attackers had administrative access to most everything within the company. Beyond your work-related passwords are those passwords that protect your most personal accounts. Choosing a hard-to-guess password won’t prevent hacking tools such as oclHashcat (a password-cracking tool that leverages graphics processing units—or GPUs—for high-speed cracking) from possibly cracking your password, but it will make the process slow enough to encourage an attacker to move on to an easier target. It’s a fair guess that some of the passwords exposed during the July 2015 Ashley Madison hack are certainly being used elsewhere, including on bank accounts and even work computers.

pages: 340 words: 90,674

The Perfect Police State: An Undercover Odyssey Into China's Terrifying Surveillance Dystopia of the Future
by Geoffrey Cain
Published 28 Jun 2021

The same year, in 2011, a pair of research assistants working for the famed AI researcher Geoffrey Hinton, a computer science professor at the University of Toronto and Google AI researcher, made a hardware breakthrough that made these advances possible. The researchers realized they could repurpose graphics processing units (GPUs), the components installed in devices that allowed for advances in computer game graphics, to improve the processing speeds of a deep neural net.9 With GPUs, AI developers could utilize the same techniques for displaying shapes and images on a computer screen, and use them to train a neural network in finding patterns.

pages: 332 words: 93,672

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy
by George Gilder
Published 16 Jul 2018

Balaban’s work caught the attention of the academic image gurus Zak Stone and Nicolas Pinto at Perceptio Corporation, and they hired him in November to develop mobile face recognition technology for the iPhone. Like all such projects by then, this one would be based on deep neural-network processing. But it was mobile machine learning, Balaban explains, “meaning running face recognition and other neural nets on the phone’s own graphics processing unit, not even uploading to the sky.” He saw that artificial intelligence did not need to take place in giant data warehouses. This was a contrarian insight worthy of a Thiel Fellow (and by mid-2013 he was living with two of them, Austin Russell and Thomas Sohmers), but it took some years before he capitalized on it.

pages: 848 words: 227,015

On the Edge: The Art of Risking Everything
by Nate Silver
Published 12 Aug 2024

*6 Investopedia defines this term well: “Shitcoin refers to a cryptocurrency with little to no value or no immediate, discernible purpose.” *7 Satoshi after Satoshi Nakamoto, the founder of Bitcoin, and Pepe after Rare Pepe, an early NFT project. *8 GPU stands for “graphics processing unit”—a type of computer chip that was originally optimized to display video game graphics, but which is also highly efficient for general mathematical computations. Thus, GPUs are often used for other computationally intensive problems, such as training AI models. *9 Even the Black-Scholes formula for pricing options—though derided by Peter Thiel and others for being too simplistic—is relatively complex as famous formulas go.

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GPT: A series of large language models created by OpenAI; the most recent version is GPT-4. GPT stands for Generative Pretrained Transformer. “Generative” refers to how LLMs generate output (responses to user queries) rather than merely classifying data; see also: training and transformer. GPU: Graphics processing unit, a chip originally designed for rendering graphics but which is highly efficient for general mathematical computations, making it the gold standard in AI research and applications. See also: compute. Grand-world problem: An open-ended, complex, dynamic problem that doesn’t lend itself to tractable answers through the use of algorithms or models.

pages: 360 words: 100,991

Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence
by Richard Yonck
Published 7 Mar 2017

Generally speaking, by increasing the number of hidden layers, the network is able to perform with increased accuracy. (Though there is a point where accuracy actually begins to fall off.) The trade-off in striving for greater accuracy is that the more nodes and layers used, the more computation time is needed. Fortunately, around the same time as the 2006 papers, graphics processing units, known as GPUs, increased in availability and dropped in cost. These processors made it possible to speed up the network training by orders of magnitude, performing the major number crunching required to reduce what once took weeks to a matter of days or hours. Different approaches led to further refining of these deep learning techniques, using methods with names such as restricted Boltzmann machines and recurrent neural networks.

pages: 348 words: 97,277

The Truth Machine: The Blockchain and the Future of Everything
by Paul Vigna and Michael J. Casey
Published 27 Feb 2018

This means that the protocol’s in-built consensus algorithm—the puzzle miners must solve to win coins—compels their computers to carry out various functions that can’t easily be performed by existing versions of the super-fast Application-Specific Integrated Chips now uniformly embedded into the equipment of the biggest bitcoin miners. The idea is to give no special advantage to those who own these expensive, single-purpose raw computation machines. This means that people running computers with relatively inexpensive graphic processing units, or GPUs, can successfully compete for a decent supply of coins and that a wider distribution of those tokens is possible. Eventually, chip designers tend to figure out how to make ASICs that overcome this resistance, as was the case with ASIC mining equipment that was specially designed to handle Litecoin’s s-crypt algorithm.

pages: 349 words: 102,827

The Infinite Machine: How an Army of Crypto-Hackers Is Building the Next Internet With Ethereum
by Camila Russo
Published 13 Jul 2020

The industrial plant in the photo was located there, in the middle of a forest and next to a hydroelectric plant that drew its energy from the Danube River. From his previous job, Paul knew the factory had a lot of spare space. They calculated it was enough to house about 1,500 GPUs, or graphics processing units, which are used to mine cryptocurrencies because of their speed. It was perfect. They would rent out the space and get cheap, green energy from the power station nearby. Less than a week later, in May 2015, they were living in a small inn close to their brand-new factory. Aurel threw in all of his meager savings, while Paul put in most of the investment needed.

pages: 385 words: 111,113

Augmented: Life in the Smart Lane
by Brett King
Published 5 May 2016

If you wear a smartwatch on your wrist, it likely has more processing power than a desktop computer dating back 15 years. The Raspberry Pi Zero computer, which costs just US$5 today, has the equivalent processing capability of the iPad 2 released in 2011. Vehicles like the Tesla Model S carry multiple central processing units (CPUs) and graphics processing units (GPUs), creating a combined computing platform greater than that of a 747 airliner3. Within 30 years, you’ll be carrying around in your pocket or embedded in your clothes, home and even within your body computing technology that will be more powerful than the most powerful supercomputer built today, and probably even more powerful than all of the computers connected to the Internet in the year 1995.4 Networks and Interwebs The early days of the Internet began as a project known as the Advanced Research Projects Agency Network (ARPANET), led by the Advanced Research Projects Agency (ARPA, later Defense Advanced Research Projects Agency, DARPA) and the academic community.

pages: 416 words: 106,532

Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond: The Innovative Investor's Guide to Bitcoin and Beyond
by Chris Burniske and Jack Tatar
Published 19 Oct 2017

Initially, computers on the network crunched through hashes using their central processing unit (CPU), which is the primary chip responsible for the functioning of our computers. Mining with this method hogged the resources of the computer. And although a CPU is a good multitasker, it’s not the most efficient chip for doing the same task over and over, which is exactly what searching for the golden hash involved. Theoretically, a better chip for mining is the graphical processing unit (GPU). As its name implies, GPUs are used to generate the graphics that appear on screens, but they are now also widely used for machine learning applications. GPUs are massively parallel processing units, meaning they can run similar calculations in parallel because they have hundreds or thousands of mini-processing units, as opposed to CPUs that have just a handful of processing units.4 While the little units within a GPU cannot perform the wide range of abstract operations that a CPU can, they are good enough for hashing together data.

pages: 390 words: 108,171

The Space Barons: Elon Musk, Jeff Bezos, and the Quest to Colonize the Cosmos
by Christian Davenport
Published 20 Mar 2018

Space tourism, then, was not just a way for people, albeit wealthy people, to experience space, but it was a way to make space more accessible. “Tourism often leads to new technologies,” Bezos said at the Washington Post forum. “And then those new technologies often circle back around and get used in very important, utilitarian ways.” Graphic Processing Units, or GPUs, for example, were invented for video games. But now they’re being used for machine learning, he said. In addition to the ten-minute jaunts to space, the future for Blue Origin involved a much larger, more ambitious rocket. Internally, it had been called “Very Big Brother,” but now it had a more formal name: New Glenn, after John Glenn, the first American in orbit.

Reset
by Ronald J. Deibert
Published 14 Aug 2020

One of the best examples of the data-sharing bonanza that occurs over social media can be found on the principal tools we use to navigate the web: our browsers. Depending on their settings and how they are coded, browsers can tell a lot about a user, and the user’s software and hardware46: your CPU (central processing unit), GPU (graphics processing unit), and battery; your operating system; the IP address you are provided for each session; your geolocation; a detailed history of the websites you have visited; your mouse movements and clicks; whether your device has a gyroscope and a compass, and which orientation it is currently in; the social media accounts into which you have logged; which fonts and languages are set to default or are installed on your machine; and so on.

pages: 363 words: 109,834

The Crux
by Richard Rumelt
Published 27 Apr 2022

Today, a modern Intel core has 200 to 500 million transistors, and the whole processor has 4 to 12 cores. In the most advanced applications, all cores can be tasked by the software at once, but if all were fully loaded at once the heat would melt the device. So careful power balancing is required. Most desktop and laptop programs get along just fine with 1 core. By contrast, an Nvidia graphics processing unit, or GPU, has much simpler cores. Each has to do only a few simple multiplication, division, and other arithmetic operations. A GPU core has only 10 million transistors, and the most recent consumer GPU from Nvidia has 2,176 cores. It is this ability to run simple simultaneous operations that make Nvidia GPU-based chips so useful in AI pattern training.

pages: 523 words: 112,185

Doing Data Science: Straight Talk From the Frontline
by Cathy O'Neil and Rachel Schutt
Published 8 Oct 2013

From a 2013 talk by Peter Richtarik from the University of Edinburugh: “In the Big Data domain classical approaches that rely on optimization methods with multiple iterations are not applicable as the computational cost of even a single iteration is often too excessive; these methods were developed in the past when problems of huge sizes were rare to find. We thus need new methods which would be simple, gentle with data handling and memory requirements, and scalable. Our ability to solve truly huge scale problems goes hand in hand with our ability to utilize modern parallel computing architectures such as multicore processors, graphical processing units, and computer clusters.” Much of this is outside the scope of the book, but a data scientist needs to be aware of these issues, and some of this is discussed in Chapter 14. Summing It All Up We’ve now introduced you to three algorithms that are the basis for the solutions to many real-world problems.

pages: 444 words: 117,770

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma
by Mustafa Suleyman
Published 4 Sep 2023

And yet it was also LeCun who said AlphaGo was impossible just days before it made its first big breakthrough. That’s no discredit to him; it just shows that no one can ever be sure of anything at the research frontier. Even in hardware the path toward AI was impossible to predict. GPUs—graphics processing units—are a foundational part of modern AI. But they were first developed to deliver ever more realistic graphics in computer games. In an illustration of the omni-use nature of technology, fast parallel processing for flashy graphics turned out to be perfect for training deep neural networks.

pages: 410 words: 119,823

Radical Technologies: The Design of Everyday Life
by Adam Greenfield
Published 29 May 2017

Chief among these are a multi-core central processing unit; a few gigabits of nonvolatile storage (and how soon that “giga-” will sound quaint); and one or more ancillary chips dedicated to specialized functions. Among the latter are the baseband processor, which manages communication via the phone’s multiple antennae; light and proximity sensors; perhaps a graphics processing unit; and, of increasing importance, a dedicated machine-learning coprocessor, to aid in tasks like speech recognition. The choice of a given chipset will determine what operating system the handset can run; how fast it can process input and render output; how many pictures, songs and videos it can store on board; and, in proportion to these capabilities, how much it will cost at retail.

pages: 457 words: 126,996

Hacker, Hoaxer, Whistleblower, Spy: The Story of Anonymous
by Gabriella Coleman
Published 4 Nov 2014

Peter Bright, a reporter from Ars Technica who conducted a thorough accounting of all the technical details behind the hack, wrote that “In fact, it had what can only be described as a pretty gaping bug in it.”22 Once inside, they rummaged around and found encrypted passwords. The encryption was too strong to crack on their own, but by utilizing the brute force of a pool of GPUs (graphics processing unit), they were able to crack the hashes in a number of hours. One of the passwords, “kibafo33,” granted access to Barr’s Gmail-hosted email account. There the Anons saw the jubilant internal HBGary email exchanges. Naturally, the hackers tried the password on all of Barr’s social media accounts and found that he violated the first rule of informational security: never use the same password across platforms.

pages: 580 words: 125,129

Androids: The Team That Built the Android Operating System
by Chet Haase
Published 12 Aug 2021

See also Dream garbage collector 76, 84–85, 242 Gay, Bruce xv, 96–97, 283, 338, 369 General Magic 95, 222, 382 Ghosh, Debajit xiv, 193–194, 197–199, 202, 228, 369 Gibson, Ryan PC xiv, xxi, 210, 259–262, 329, 361, 369 Gingerbread xvii, 146 Git 248 Google Maps 40, 182, 185, 192, 207, 209, 213–217, 228, 246, 266, 280, 302, 320, 343, 357 Google Play Store 64, 152, 220, 246, 269, 314, 354. See also Android Market Google Search Appliance (GSA) 53, 263 governor 69–70 graphics xiii, xiv, xvii, xviii, 8, 36–37, 41–42, 45–46, 58, 62, 65, 81, 97–98, 100, 102–109, 114, 127, 129–130, 136, 145, 156, 170, 174–177, 180, 195, 216, 232, 235–236, 242, 373, 375 Graphics Processing Unit (GPU) 62, 100, 104–108, 156, 238, 242, 270 Guy, Romain xiv, xviii, xxiii, 60, 84, 97, 123, 144–149, 152, 155–157, 166, 189–191, 246, 257, 270, 282, 289, 314–318, 322, 361, 367, 369, 380–381 Hackborn, Dianne xiv, xxiii, 16, 25, 39, 67, 69, 77–78, 96–97, 118–126, 128–129, 131, 133–135, 148–149, 154–156, 163, 188–189, 214, 225–226, 241–242, 246, 260–261, 271, 298, 309, 325, 330, 344, 362, 369, 381 Hamilton, Jeff xiv, 8, 89, 93, 121, 123, 128–135, 194, 214, 268, 369 Heyl, Ed xiv, 94–97, 99, 154, 248, 362, 369 Hiptop 13–14, 18, 22, 29–30, 34, 67, 77, 153, 171, 193, 195, 201.

pages: 533 words: 125,495

Rationality: What It Is, Why It Seems Scarce, Why It Matters
by Steven Pinker
Published 14 Oct 2021

But more often the hidden units don’t stand for anything we have names for. They implement whichever complex formulas get the job done: “a teensy bit of this feature, but not too much of that feature, unless there’s really a lot of this other feature.” In the second decade of the twenty-first century, computer power skyrocketed with the development of graphics processing units, and data got bigger and bigger as millions of users uploaded text and images to the web. Computer scientists could put multilayer networks on megavitamins, giving them two, fifteen, even a thousand hidden layers, and training them on billions or even trillions of examples. The networks are called deep learning systems because of the number of layers between the input and the output (they’re not deep in the sense of understanding anything).

pages: 515 words: 126,820

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World
by Don Tapscott and Alex Tapscott
Published 9 May 2016

For backward compatibility with slower-changing legacy systems, your laptop or PC is likely a type of complex instruction set computer (CISC) that can run a wide range of math apps that the average person will never ever use. When engineers realized that they’d seriously overshot the market, they created the reduced instruction set computer (RISC). Your mobile device is likely an advanced RISC machine (ARM). What miners realized was that they could also harness their graphics processing unit to increase processing speed. Because modern GPUs have thousands of computing cores on each chip, they are ideal for computations that can be done in parallel, such as the hashing done in bitcoin mining, There were some trade-offs, and estimating the machine’s energy consumption got slightly more complicated, but for the most part GPUs could do the work.19 “If I can design a RISC computer to be oh-so-superfast and massively, near insanely parallel to try the billions of kazillions of codes simultaneously with little or no electricity, I will make money out of thin air,”20 said Bob Tapscott, Don’s CIO brother.

pages: 416 words: 129,308

The One Device: The Secret History of the iPhone
by Brian Merchant
Published 19 Jun 2017

In 2002, CCDs traditionally produced much better image quality but were slower and sucked down more power. CMOSs were cheaper, smaller, and allowed for faster video processing, but they were plagued with problems. Still, Bilbrey had a plan. He would send the video from the camera down to the computer’s graphics processing unit (GPU), where its extra muscle could handle color correction and clean up the video. He could offload the work of the camera sensor to the computer, basically. So his team got to work rerouting the iSight for a demo that was now mere days away. “I developed a bunch of video algorithms for enhancement, cleanup, and filtering, and we employed many of those to create the demo,” he says.

pages: 486 words: 132,784

Inventors at Work: The Minds and Motivation Behind Modern Inventions
by Brett Stern
Published 14 Oct 2012

Once a goal’s in my head, it doesn’t take very long for me to sit down and get to wiring pieces together to do what I had envisioned. Stern: Do you have a next project you’re working on that you want to talk about? Wozniak: I don’t have a specific project right at this moment. I would sure love to investigate building full CPUs [central processing units] out of GPUs [graphics processing units] combined with NAND flash memory and some DRAM in the right proportions to get ultra-performance. GPUs take an awful lot of power because you’ve got so much computing all at once, so I would love to develop chips that work on photonics, since electrons are so much lighter and more efficient than atoms.

pages: 1,025 words: 150,187

ZeroMQ
by Pieter Hintjens
Published 12 Mar 2013

Our supercomputing application is a fairly typical parallel processing model (Figure 1-5). We have: A ventilator that produces tasks that can be done in parallel A set of workers that processes tasks A sink that collects results back from the worker processes Figure 1-5. Parallel pipeline In reality, workers run on superfast boxes, perhaps using GPUs (graphic processing units) to do the hard math. Example 1-8 shows the code for the ventilator. It generates 100 tasks, each one a message telling the worker to sleep for some number of milliseconds. Example 1-8. Parallel task ventilator (taskvent.c) // // Task ventilator // Binds PUSH socket to tcp://localhost:5557 // Sends batch of tasks to workers via that socket // #include "zhelpers.h" int main (void) { void *context = zmq_ctx_new (); // Socket to send messages on void *sender = zmq_socket (context, ZMQ_PUSH); zmq_bind (sender, "tcp://*:5557"); // Socket to send start of batch message on void *sink = zmq_socket (context, ZMQ_PUSH); zmq_connect (sink, "tcp://localhost:5558"); printf ("Press Enter when the workers are ready: "); getchar (); printf ("Sending tasks to workers...

pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies
by Nick Bostrom
Published 3 Jun 2014

Algorithmic overhang is perhaps less likely, but one exception would be if exotic hardware such as quantum computing becomes available to run algorithms that were previously infeasible. One might also argue that neural networks and deep machine learning are cases of algorithm overhang: too computationally expensive to work well when first invented, they were shelved for a while, then dusted off when fast graphics processing units made them cheap to run. Now they win contests. 18. And even if progress on the way toward the human baseline were slow. 19. is that part of the world’s optimization power that is applied to improving the system in question. For a project operating in complete isolation, one that receives no significant ongoing support from the external world, we have ≈ 0, even though the project must have started with a resource endowment (computers, scientific concepts, educated personnel, etc.) that is derived from the entire world economy and many centuries of development. 20.

pages: 661 words: 185,701

The Future of Money: How the Digital Revolution Is Transforming Currencies and Finance
by Eswar S. Prasad
Published 27 Sep 2021

Bitcoin mining was initially conducted on regular computers, with the processing power of the devices’ central processing units (CPUs) determining the success rate of the miners that used them. In the early days, when the Bitcoin blockchain was much shorter and the difficulty level of the numerical problems was far lower than is now the case, anyone with a powerful personal computer could be a successful miner. It soon turned out that graphics processing units (GPUs), essentially graphics cards used in higher-end machines, were better suited for the computations needed for cryptocurrency mining. The rising pecuniary benefits of cryptocurrency mining then led to some advances in hardware that could be better optimized for this purpose. Much of the mining of Bitcoin is now carried out by specialized devices called ASICs, or application-specific integrated circuits.

pages: 619 words: 177,548

Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity
by Daron Acemoglu and Simon Johnson
Published 15 May 2023

It then fits a statistical model to large data sets of image features to predict when humans say that there is a cat in the frame. It subsequently applies the estimated statistical model to new pictures to predict whether there is a cat there or not. Progress was made possible by faster computer processor speed, as well as new graphics processing units (GPUs), originally used to generate high-resolution graphics in video games, which proved to be a powerful tool for data crunching. There have also been major advances in data storage, reducing the cost of storing and accessing massive data sets, and improvements in the ability to perform large amounts of computation distributed across many devices, aided by rapid advances in microprocessors and cloud computing.

pages: 1,409 words: 205,237

Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale
by Jan Kunigk , Ian Buss , Paul Wilkinson and Lars George
Published 8 Jan 2019

Although some instance types in this group also feature SSDs, their count is too small to consider them for worker nodes in sticky cluster implementations. This group also features the X1e instances that offer up to nearly 4 TB of memory. Although single instances in this group can have up to 128 vCPUs, their count is small relative to the strong emphasis on RAM. Accelerated-computing instances This group features instances that offer graphics processing units (GPUs) as accelerators. For big data, this addresses the fast-growing realm of use cases around machine learning and deep learning. All the instance classes, such as the M5 series, C5 series, and others in the aforementioned groups, themselves contain multiple instance types of increasing capabilities.

pages: 562 words: 201,502

Elon Musk
by Walter Isaacson
Published 11 Sep 2023

It’s like the way humans learn to speak and drive and play chess and eat spaghetti and do almost everything else; we might be given a set of rules to follow, but mainly we pick up the skills by observing how other people do them. It was the approach to machine learning envisioned by Alan Turing in his 1950 paper, “Computing Machinery and Intelligence.” Tesla had one of the world’s largest supercomputers to train neural networks. It was powered by graphics processing units (GPUs) made by the chipmaker Nvidia. Musk’s goal for 2023 was to transition to using Dojo, the supercomputer that Tesla was building from the ground up, to use video data to train the AI system. With chips and infrastructure designed in-house by Tesla’s AI team, it has nearly eight exaflops (1018 operations per second) of processing power, making it the world’s most powerful computer for that purpose.

Seeking SRE: Conversations About Running Production Systems at Scale
by David N. Blank-Edelman
Published 16 Sep 2018

Since then, AI has been alternating between being the key to our civilization’s future and tossed on to technology’s trash heap. Today it’s absolutely unfair to say “human versus machine” in any of these and other games given that the machines are in a clear position of strength. Over the past few years, we’ve seen AI exploding, mostly due to Graphics Processing Units (GPUs) making parallel processing faster and cheaper as well as a flood of data of every stripe: images, text, transactions, logs — you name it. Why Now? What Changed for Us? In the SRE world, deployments are getting faster and faster, and we (humans) can react only so fast. We need to automate more and get decisions right.