The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence
by Sebastian Mallaby; · 30 Mar 2026 · 607pp · 161,998 words
-up in performance.[4] In 2009, around the time he met Hassabis, Hinton received another boost: the opportunity to turbocharge these networks with special chips—“graphics processing units” originally designed to render video game images.[5] But even though Hinton was riding high, he found to his astonishment that Hassabis was as
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end of 2015, Huang and his colleagues began to run AlphaGo on a new kind of hardware—a special Google chip that supplanted Nvidia’s graphics processing unit. The new “tensor processing units,” or TPUs, could speed through calculations even faster than GPUs; by rounding off numbers to the nearest integer and
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in a paragraph at once, leveraging the parallel processing power of modern AI chips. Since the start of the decade, AI models had worked on graphics processing units, or their Google equivalent, the TPU. But sequential processing could not take advantage of GPUs’ ability to process thousands of tasks simultaneously. Now, thanks
The Icon Handbook
by Jon Hicks · 23 Jun 2011
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
Programming Android
by Zigurd Mednieks, Laird Dornin, G. Blake Meike and Masumi Nakamura · 15 Jul 2011
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
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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
Androids: The Team That Built the Android Operating System
by Chet Haase · 12 Aug 2021 · 580pp · 125,129 words
, 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
The Art of R Programming
by Norman Matloff · 404pp · 43,442 words
“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
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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
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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
Python Data Analytics: With Pandas, NumPy, and Matplotlib
by Fabio Nelli · 27 Sep 2018 · 688pp · 107,867 words
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
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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
The Simulation Hypothesis
by Rizwan Virk · 31 Mar 2019 · 315pp · 89,861 words
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
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. 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
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accurately, re-rendering) photorealistic images as you move around a virtual environment. The higher the resolution, the more powerful CPU (computer processing unit) or GPU (graphics processing unit) is needed. Lack of Kinesthetic Feedback. To create a full immersion experience, other senses besides our vision need to be stimulated, like touch and
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as the plane or perspective shifts. Like an old dot matrix printer, the scene is re-rendered line by line while the user waits. GPUs (graphics processing units) were created, plain and simple, to speed up graphics processing and make games load faster since CPUs (or central processing units) aren’t optimized
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–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
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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
Valley of Genius: The Uncensored History of Silicon Valley (As Told by the Hackers, Founders, and Freaks Who Made It Boom)
by Adam Fisher · 9 Jul 2018 · 611pp · 188,732 words
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
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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
Doing Data Science: Straight Talk From the Frontline
by Cathy O'Neil and Rachel Schutt · 8 Oct 2013 · 523pp · 112,185 words
. 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
When Computers Can Think: The Artificial Intelligence Singularity
by Anthony Berglas, William Black, Samantha Thalind, Max Scratchmann and Michelle Estes · 28 Feb 2015
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
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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
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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
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. Being easy to program was and is generally far more important than being very efficient. Today there are variations of the basic von Neumann architecture. Graphics Processing Units (GPUs) contain hundreds of von Neumann subsystems that can compute at the same time and so render complex scenes in real time. More radical
ZeroMQ
by Pieter Hintjens · 12 Mar 2013 · 1,025pp · 150,187 words
The Thinking Machine: Jensen Huang, Nvidia, and the World's Most Coveted Microchip
by Stephen Witt · 8 Apr 2025 · 260pp · 82,629 words
The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future
by Kevin Kelly · 6 Jun 2016 · 371pp · 108,317 words
The Everything Blueprint: The Microchip Design That Changed the World
by James Ashton · 11 May 2023 · 401pp · 113,586 words
Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig · 14 Jul 2019 · 2,466pp · 668,761 words
The Long History of the Future: Why Tomorrow's Technology Still Isn't Here
by Nicole Kobie · 3 Jul 2024 · 348pp · 119,358 words
RDF Database Systems: Triples Storage and SPARQL Query Processing
by Olivier Cure and Guillaume Blin · 10 Dec 2014
Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy
by George Gilder · 16 Jul 2018 · 332pp · 93,672 words
The Metaverse: And How It Will Revolutionize Everything
by Matthew Ball · 18 Jul 2022 · 412pp · 116,685 words
The Road to Conscious Machines
by Michael Wooldridge · 2 Nov 2018 · 346pp · 97,890 words
The One Device: The Secret History of the iPhone
by Brian Merchant · 19 Jun 2017 · 416pp · 129,308 words
Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum & Smart Contracts
by David Gerard · 23 Jul 2017 · 309pp · 54,839 words
This Is for Everyone: The Captivating Memoir From the Inventor of the World Wide Web
by Tim Berners-Lee · 8 Sep 2025 · 347pp · 100,038 words
Radical Technologies: The Design of Everyday Life
by Adam Greenfield · 29 May 2017 · 410pp · 119,823 words
Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World
by Cade Metz · 15 Mar 2021 · 414pp · 109,622 words
Driverless: Intelligent Cars and the Road Ahead
by Hod Lipson and Melba Kurman · 22 Sep 2016
On the Edge: The Art of Risking Everything
by Nate Silver · 12 Aug 2024 · 848pp · 227,015 words
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 · 27 Mar 2016 · 421pp · 110,406 words
Augmented: Life in the Smart Lane
by Brett King · 5 May 2016 · 385pp · 111,113 words
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 · 25 Sep 2017 · 391pp · 71,600 words
The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future
by Keach Hagey · 19 May 2025 · 439pp · 125,379 words
The Crux
by Richard Rumelt · 27 Apr 2022 · 363pp · 109,834 words
Superintelligence: Paths, Dangers, Strategies
by Nick Bostrom · 3 Jun 2014 · 574pp · 164,509 words
Bitcoin for the Befuddled
by Conrad Barski · 13 Nov 2014 · 273pp · 72,024 words
Why Machines Learn: The Elegant Math Behind Modern AI
by Anil Ananthaswamy · 15 Jul 2024 · 416pp · 118,522 words
The Means of Prediction: How AI Really Works (And Who Benefits)
by Maximilian Kasy · 15 Jan 2025 · 209pp · 63,332 words
The Future of the Brain: Essays by the World's Leading Neuroscientists
by Gary Marcus and Jeremy Freeman · 1 Nov 2014 · 336pp · 93,672 words
Rule of the Robots: How Artificial Intelligence Will Transform Everything
by Martin Ford · 13 Sep 2021 · 288pp · 86,995 words
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
by Karen Hao · 19 May 2025 · 660pp · 179,531 words
The Cryptopians: Idealism, Greed, Lies, and the Making of the First Big Cryptocurrency Craze
by Laura Shin · 22 Feb 2022 · 506pp · 151,753 words
The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives
by Peter H. Diamandis and Steven Kotler · 28 Jan 2020 · 501pp · 114,888 words
Four Battlegrounds
by Paul Scharre · 18 Jan 2023
Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale
by Jan Kunigk, Ian Buss, Paul Wilkinson and Lars George · 8 Jan 2019 · 1,409pp · 205,237 words
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 · 19 Oct 2017 · 416pp · 106,532 words
The Economic Singularity: Artificial Intelligence and the Death of Capitalism
by Calum Chace · 17 Jul 2016 · 477pp · 75,408 words
Surviving AI: The Promise and Peril of Artificial Intelligence
by Calum Chace · 28 Jul 2015 · 144pp · 43,356 words
Mastering Ethereum: Building Smart Contracts and DApps
by Andreas M. Antonopoulos and Gavin Wood Ph. D. · 23 Dec 2018 · 960pp · 125,049 words
The Infinite Machine: How an Army of Crypto-Hackers Is Building the Next Internet With Ethereum
by Camila Russo · 13 Jul 2020 · 349pp · 102,827 words
Reset
by Ronald J. Deibert · 14 Aug 2020
The Alignment Problem: Machine Learning and Human Values
by Brian Christian · 5 Oct 2020 · 625pp · 167,349 words
Pattern Breakers: Why Some Start-Ups Change the Future
by Mike Maples and Peter Ziebelman · 8 Jul 2024 · 207pp · 65,156 words
Elon Musk
by Walter Isaacson · 11 Sep 2023 · 562pp · 201,502 words
Advances in Financial Machine Learning
by Marcos Lopez de Prado · 2 Feb 2018 · 571pp · 105,054 words
Supremacy: AI, ChatGPT, and the Race That Will Change the World
by Parmy Olson · 284pp · 96,087 words
The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma
by Mustafa Suleyman · 4 Sep 2023 · 444pp · 117,770 words
Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity
by Daron Acemoglu and Simon Johnson · 15 May 2023 · 619pp · 177,548 words
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
by Eric Topol · 1 Jan 2019 · 424pp · 114,905 words
Chokepoints: American Power in the Age of Economic Warfare
by Edward Fishman · 25 Feb 2025 · 884pp · 221,861 words
Inventors at Work: The Minds and Motivation Behind Modern Inventions
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The Future of Money: How the Digital Revolution Is Transforming Currencies and Finance
by Eswar S. Prasad · 27 Sep 2021 · 661pp · 185,701 words
Seeking SRE: Conversations About Running Production Systems at Scale
by David N. Blank-Edelman · 16 Sep 2018
Your Face Belongs to Us: A Secretive Startup's Quest to End Privacy as We Know It
by Kashmir Hill · 19 Sep 2023 · 487pp · 124,008 words
Machine, Platform, Crowd: Harnessing Our Digital Future
by Andrew McAfee and Erik Brynjolfsson · 26 Jun 2017 · 472pp · 117,093 words
Artificial Intelligence: A Guide for Thinking Humans
by Melanie Mitchell · 14 Oct 2019 · 350pp · 98,077 words
Bitcoin Internals: A Technical Guide to Bitcoin
by Chris Clark · 16 Jun 2013 · 52pp · 13,257 words
Finding Alphas: A Quantitative Approach to Building Trading Strategies
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Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence
by Richard Yonck · 7 Mar 2017 · 360pp · 100,991 words
The Perfect Police State: An Undercover Odyssey Into China's Terrifying Surveillance Dystopia of the Future
by Geoffrey Cain · 28 Jun 2021 · 340pp · 90,674 words
Digital Gold: Bitcoin and the Inside Story of the Misfits and Millionaires Trying to Reinvent Money
by Nathaniel Popper · 18 May 2015 · 387pp · 112,868 words
The Bitcoin Guidebook: How to Obtain, Invest, and Spend the World's First Decentralized Cryptocurrency
by Ian Demartino · 2 Feb 2016 · 296pp · 86,610 words
The Deep Learning Revolution (The MIT Press)
by Terrence J. Sejnowski · 27 Sep 2018
Rationality: What It Is, Why It Seems Scarce, Why It Matters
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I, Warbot: The Dawn of Artificially Intelligent Conflict
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The AI-First Company
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Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World
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Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
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The Truth Machine: The Blockchain and the Future of Everything
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Applied Artificial Intelligence: A Handbook for Business Leaders
by Mariya Yao, Adelyn Zhou and Marlene Jia · 1 Jun 2018 · 161pp · 39,526 words
Mastering Blockchain: Unlocking the Power of Cryptocurrencies and Smart Contracts
by Lorne Lantz and Daniel Cawrey · 8 Dec 2020 · 434pp · 77,974 words
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 · 14 Feb 2017 · 305pp · 93,091 words
The Age of Extraction: How Tech Platforms Conquered the Economy and Threaten Our Future Prosperity
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Personal Finance with Python
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Working in Public: The Making and Maintenance of Open Source Software
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Hacker, Hoaxer, Whistleblower, Spy: The Story of Anonymous
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Take the Money and Run: Sovereign Wealth Funds and the Demise of American Prosperity
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The Space Barons: Elon Musk, Jeff Bezos, and the Quest to Colonize the Cosmos
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The Quiet Damage: QAnon and the Destruction of the American Family
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How the World Ran Out of Everything
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