AlphaFold

The Singularity Is Nearer: When We Merge with AI
by Ray Kurzweil
Published 25 Jun 2024

BACK TO NOTE REFERENCE 15 Ian Sample, “Google’s DeepMind Predicts 3D Shapes of Proteins,” Guardian, December 2, 2018, https://www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins; Matt Reynolds, “DeepMind’s AI Is Getting Closer to Its First Big Real-World Application,” Wired, January 15, 2020, https://www.wired.co.uk/article/deepmind-protein-folding-alphafold. BACK TO NOTE REFERENCE 16 For some more detailed explainers of AlphaFold 2 and the scientific paper describing it, see “AlphaFold: The Making of a Scientific Breakthrough,” DeepMind, YouTube video, November 30, 2020, https://www.youtube.com/watch?v=gg7WjuFs8F4; “DeepMind Solves Protein Folding | AlphaFold 2,” Lex Fridman, YouTube video, December 2, 2020, https://www.youtube.com/watch?

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Senior et al., “Improved Protein Structure Prediction Using Potentials from Deep Learning,” Nature 577, no. 7792 (January 15, 2020), https://doi.org/10.1038/s41586-019-1923-7. BACK TO NOTE REFERENCE 14 For more detail on how the original AlphaFold achieved great progress on protein folding, see Andrew W. Senior et al., “AlphaFold: Using AI for Scientific Discovery,” DeepMind, January 15, 2020, https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery; Andrew Senior, “AlphaFold: Improved Protein Structure Prediction Using Potentials from Deep Learning,” Institute for Protein Design, YouTube video, August 23, 2019, https://www.youtube.com/watch?

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v=W7wJDJ56c88; Ewen Callaway, “ ‘It Will Change Everything’: DeepMind’s AI Makes Gigantic Leap in Solving Protein Structures,” Nature 588, no. 7837 (November 30, 2020): 203–4, https://doi.org/10.1038/d41586-020-03348-4; Demis Hassabis, “Putting the Power of AlphaFold into the World’s Hands,” DeepMind, July 22, 2022, https://deepmind.com/blog/article/putting-the-power-of-alphafold-into-the-worlds-hands; John Jumper et al., “Highly Accurate Protein Structure Prediction with AlphaFold,” Nature 596, no. 7873 (July 15, 2021): 583–89, https://doi.org/10.1038/s41586-021-03819-2. BACK TO NOTE REFERENCE 17 Mohammed AlQuraishi, “Protein-Structure Prediction Revolutionized,” Nature 596, no. 7873 (August 23, 2021): 487–88, https://doi.org/10.1038/d41586-021-02265-4.

The Age of AI: And Our Human Future
by Henry A Kissinger , Eric Schmidt and Daniel Huttenlocher
Published 2 Nov 2021

Since the 1970s, this challenge has been called protein folding. Before 2016, there had not been much progress toward improving the accuracy of protein folding—until a new program, AlphaFold, yielded major progress. As its name implies, AlphaFold was informed by the approach developers took when they taught AlphaZero to play chess. Like AlphaZero, AlphaFold uses reinforcement learning to model proteins without requiring human expertise—in this case, the known protein structures previous approaches relied upon. AlphaFold has more than doubled the accuracy of protein folding from around 40 to around 85 percent, enabling biologists and chemists around the world to revisit old questions they had been unable to answer and to ask new questions about battling pathogens in people, animals, and plants.2 Advances like AlphaFold—impossible without AI—are transcending previous limits in measurement and prediction.

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David Autor, David Mindell, and Elisabeth Reynolds, “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines,” MIT Task Force on the Work of the Future, November 17, 2020, https://workofthefuture.mit.edu/research-post/the-work-of-the-future-building-better-jobs-in-an-age-of-intelligent-machines. 2. “AlphaFold: A Solution to a 50-Year-Old Grand Challenge in Biology,” DeepMind blog, November 30, 2020, https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology. 3. See Walter Lippmann, Public Opinion (New York: Harcourt, Brace and Company, 1922), 11. 4. Robert Post, “Participatory Democracy and Free Speech,” Virginia Law Review 97, no. 3 (May 2011): 477–478. 5.

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Across the biological, chemical, and physical sciences, a hybrid partnership is emerging in which AI is enabling new discoveries that humans are, in response, working to understand and explain. A striking example of AI enabling broad-based discovery in the biological and chemical sciences is the development of AlphaFold, which used reinforcement learning to create powerful new models of proteins. Proteins are large, complex molecules that play a central role in the structure, function, and regulation of tissues, organs, and processes in biological systems. A protein is made up of hundreds (or thousands) of smaller units called amino acids, which are attached together to form long chains.

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking
by Michael Bhaskar
Published 2 Nov 2021

The artificial intelligence company DeepMind, part of the Alphabet group, had been quietly working on software called AlphaFold. DeepMind uses deep learning neural networks, a newly potent technique of machine learning (ML), to predict how proteins fold. These networks aim to mimic the functioning of the human brain, using layers of mathematical functions that can, by changing their weightings, appear to learn. This makes predictions which are scored against what is already known. Based on its results the neural network keeps learning, getting more accurate the more it guesses to build a highly effective model. At CASP13, AlphaFold was better able to guess the distance and angles between pairs of amino acids.

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New forms of knowledge and perception, quite unlike those of humans, beyond our unaided capabilities, are starting to accelerate the production of ideas. AlphaGo and AlphaFold are signposts to an era where those closest to a particular toolset are best positioned to push back the frontiers of knowledge. Proximity to these tools helps accelerate discovery, producing watershed moments like those at Seoul and Cancun, not to mention other moves from DeepMind alone into areas like medical diagnosis and the modelling of physical processes. And as move thirty-seven hints, this isn't just a matter of inching forward or copying humans; it adds a qualitatively different dimension. If that sounds overdone, consider that AlphaFold wasn't finished in Cancun.

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, NBER Working Paper 25756 Alexander, Scott (2018), ‘Is Science Slowing Down?’, SlateStarCodex, accessed 22 October 2020, available at https://slatestarcodex.com/2018/11/26/is-science-slowing-down-2/ AlQuraishi, Mohammed (2018), ‘AlphaFold @ CASP13: “What just happened?”’, accessed 5 February 2020, available at https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ AlQuraishi, Mohammed (2020), ‘AlphaFold2 @ CASP14: “It feels like one's child has left home”’, accessed 23 December 2020, available at https://moalquraishi.wordpress.com/2020/12/08/ alphafold2-casp14-it-feels-like-ones-child-has-left-home/ Andersen, Kurt (2011), ‘You Say You Want a Devolution?’

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

In late 2018, DeepMind entered an earlier version of its AlphaFold system in a biennial global contest known as the Critical Assessment of Structure Prediction, or CASP. Teams from around the world used a variety of techniques based on both computation and human intuition to attempt to predict the way proteins fold. AlphaFold won the 2018 contest by a wide margin, but even while prevailing, it was able to make the best prediction for only twenty-five of the forty-three protein sequences correctly. In other words, this preliminary version of AlphaFold was not yet accurate enough to be a truly useful research tool.80 The fact that DeepMind was able to refine its technology to the point where a number of scientists declared the protein folding problem to be “solved” just two years later is, I think, an especially vivid indication of just how rapidly specific applications of artificial intelligence are likely to continue advancing.

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Ewen Callaway, “‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures,” Nature, November 30, 2020, www.nature.com/articles/d41586-020-03348-4. 2. Andrew Senior, Demis Hassabis, John Jumper and Pushmeet Kohli, “AlphaFold: Using AI for scientific discovery,” DeepMind Research Blog, January 15, 2020, deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery. 3. Ian Sample, “Google’s DeepMind predicts 3D shapes of proteins,” The Guardian, December 2, 2018, www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins. 4. Lyxor Robotics and AI UCITS ETF, stock market ticker ROAI. 5. See, for example: Carl Benedikt Frey and Michael Osborne, “The future of employment: How susceptible are jobs to computerisation?

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DeepMind’s system uses AI techniques that the company pioneered in the AlphaGo and AlphaZero systems that had famously triumphed over the world’s best human competitors at board games like Go and chess. But the era of AI being primarily associated with adeptness at games is clearly drawing to a close. AlphaFold’s ability to predict the shape of protein molecules with an accuracy that rivals expensive and time-consuming laboratory measurement using techniques like X-ray crystallography offers irrefutable evidence that research at the very frontier of artificial intelligence has produced a practical and indispensable scientific tool with the potential to transform the world.

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

GO TO NOTE REFERENCE IN TEXT If you used traditional brute-force computation DeepMind, “AlphaFold: A Solution to a 50-Year-Old Grand Challenge in Biology,” DeepMind Research, Nov. 20, 2020, www.deepmind.com/​blog/​alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology. GO TO NOTE REFERENCE IN TEXT Mohammed AlQuraishi, a well-known researcher Mohammed AlQuraishi, “AlphaFold @ CASP13: ‘What Just Happened?,’ ” Some Thoughts on a Mysterious Universe, Dec. 9, 2018, moalquraishi.wordpress.com/​2018/​12/​09/​alphafold-casp13-what-just-happened. GO TO NOTE REFERENCE IN TEXT One headline said it all Tanya Lewis, “One of the Biggest Problems in Biology Has Finally Been Solved,” Scientific American, Oct. 31, 2022, www.scientificamerican.com/​article/​one-of-the-biggest-problems-in-biology-has-finally-been-solved.

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GO TO NOTE REFERENCE IN TEXT The result has been an explosion Ewen Callaway, “What’s Next for AlphaFold and the AI Protein-Folding Revolution,” Nature, April 13, 2022, www.nature.com/​articles/​d41586-022-00997-5. GO TO NOTE REFERENCE IN TEXT DeepMind uploaded some 200 million Madhumita Murgia, “DeepMind Research Cracks Structure of Almost Every Known Protein,” Financial Times, July 28, 2022, www.ft.com/​content/​6a088953-66d7-48db-b61c-79005a0a351a; DeepMind, “AlphaFold Reveals the Structure of the Protein Universe,” DeepMind Research, July 28, 2022, www.deepmind.com/​blog/​alphafold-reveals-the-structure-of-the-protein-universe.

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CASP soon became the benchmark in a ferociously competitive but tight-knit field. Progress was steady, but with no end in sight. Then, at CASP13 in 2018, held at a palm-fringed resort in Cancún, a rank outsider entrant arrived at the competition, with zero track record, and beat ninety-eight established teams. The winning team was DeepMind’s. Called AlphaFold, the project started during a weeklong experimental hackathon in my group at the company back in 2016. It grew to become a landmark moment in computational biology and provides a perfect example of how both AI and biotech are advancing at speed. While the second-place team, the well-regarded Zhang group, could predict only three protein structures out of forty-three of the most difficult targets, our winning entry predicted twenty-five.

If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All
by Eliezer Yudkowsky and Nate Soares
Published 15 Sep 2025

But the most common response, back then, was for skeptics to agree with one another that superintelligence would surely have to go through a long, drawn-out, incremental process, measured more in months than in hours, to predict… … the sort of protein folds that AlphaFold 3 can easily predict today. Google DeepMind cracked the protein folding problem between the years of 2018 and 2022 (with AIs named AlphaFold 1, AlphaFold 2, and AlphaFold 3). That was the work for which Demis Hassabis, co-founder of DeepMind, won the Nobel Prize in Chemistry. Did I just get lucky with my prediction? That’s always worth worrying about, when you’re hearing about someone in part because of their successful predictions.

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Any intelligence capable of comprehending biochemistry at the deepest level is capable of building its own self-replicating factories to serve its own purposes. Would a superintelligence have to go through a long, drawn-out, incremental process, measured more in months than in hours, to comprehend biochemistry? That’s what people in 2008 predicted about protein folding, a problem that humans found hard and that AlphaFold found easy. And AlphaFold is not a superintelligence; it’s not tens or hundreds of thousands of times faster than humans. An ultrafast mind wouldn’t want to be bogged down in a month of experimentation that seems to it like a millennium. It would use advanced computer simulations. It would squeeze every drop of information it could out of the information already observed, like Einstein squeezing every drop of insight he could out of a few scant observations about the behavior of light, and thereby predicting that clocks would run more slowly on satellites decades before any satellites were even put into space.

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But notice that what I predicted, and what the skeptics doubted, was something much weaker than what actually came to pass. I predicted: Vastly superhuman machine superintelligence could solve a special case of protein folding, in which the superintelligence was allowed to deliberately pick the easiest-to-predict proteins capable of building what it needed to build. What came to pass was: The narrow AlphaFold models were able to predict almost all biological protein folds, including the ones that humans considered quite hard to predict. I predicted that it would be possible for literal superintelligence to do this in carefully chosen cases. Reality said that it was possible for narrow AI to do this in almost all cases.

Supremacy: AI, ChatGPT, and the Race That Will Change the World
by Parmy Olson

DeepMind’s had been more academic, publishing research papers about the AlphaGo gaming system and AlphaFold, a novel approach to predicting how proteins fold in the human body. AlphaFold was born out of a hackathon—or a collaborative programming event—at DeepMind in 2016, before turning into one of the company’s most promising projects. Hassabis had dreamed of using AGI to solve big global problems like cancer, and it seemed like he finally had an AI system that could do something like that. When amino acids in our cells fold up into specific 3D shapes, they become proteins, and badly folded proteins can lead to diseases. AlphaFold was an AI program that predicted what those 3D shapes would look like when they folded up, and DeepMind believed that could help scientists better understand what kinds of chemical reactions might affect those proteins, aiding drug discovery.

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For your reference, the terms that appear in the print index are listed below. 80,000 Hours (group) Abbott, Andy Acadia, Michael Acemoglu, Daron Acton advertising, Loopt and Age of Spiritual Machines, The (Kurzweil) AGI. See artificial general intelligence. AI accelerationists AI Act (Europe) AI Now Institute Airbnb AI Safety Summit Alphabet autonomous units model and China and DeepMind and valuation of See also Google AlphaFold AlphaFold Protein Structure Database AlphaGo Altman, Connie Altman, Jerry Altman, Sam AOL chat rooms and approach to AI of on bias in DALL-E 2 blog of on ChatGPT ChatGPT and concept of death and creation of OpenAI and DeepMind recruits and detachment from people and early life of funding for OpenAI and government policy and Hassabis and Hydrazine Capital and interest in AI at John Burroughs (school) Loopt and Microsoft and Musk and Nadella on Reddit and removal from OpenAI reputation as tech savior and restructuring of OpenAI and “ship it” strategy and Silicon Valley and Stanford University and Stochastic Parrots paper and on threats posed by AI Toner’s paper and transhumanism and Y Combinator and Amazon America Online (AOL), LGBTQ community and Amodei, Daniela Amodei, Dario Anthropic and concerns about AI and departure from OpenAI OpenAI’s Microsoft partnership and Open Philanthropy and Android Anthropic Apple Art of Accomplishment podcast artificial general intelligence (AGI) DALL-E 2 and economic promises and human brain model and OpenAI and philosophical battle over pursuit of artificial intelligence accelerationists and bias/racism and China and depletion of academic experts to tech in distraction and effective altruism and effect of tech companies of research field and ethics vs. safety and fears over speed of development of future impacts of government policy and human interaction with language bias in data sets open-source p(doom) and philosophical battle over political polarization and propaganda and public imagination and reality as simulation and religion and scale and terminology and transparency and Asana Asimov, Isaac attention Baele, Stephane Baidu Bankman-Fried, Sam Bard Battery Club Beckstead, Nick Bender, Emily Bengio, Yoshua BERT “Better Language Models and Their Implications” (OpenAI) Bing Translate Birhane, Abeba BlackBerry Blumenthal, Richard BooksCorpus Boost Mobile Bostrom, Nick Brexit Brin, Sergey British Eugenics Society Brockman, Anna Brockman, Greg on Altman departure from OpenAI funding and marriage of Microsoft partnership and Musk and OpenAI and removal from OpenAI board and restructuring of OpenAI and Brookings Institute Bullfrog Productions Bumble Buolamwini, Joy Buterin, Vitalik Calico Cambridge Analytica Cambridge University Center for AI Safety Center for Effective Altruism Center for Human-Compatible AI Character.ai ChatGPT ChatGPT Plus China Chrome Claude Claude Pro cloud computing Common Crawl COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) “Concrete Problems in AI Safety” (Amodei) Copilot Coppin, Ben coreference resolution Cotra, Ajeya Cruise DALL-E 2 D’Angelo, Adam Dartmouth College Datasheets for Datasets Dayan, Peter Dean, Jeff Deep Blue DeepMind Alphabet and AlphaFold AlphaGo and Applied ChatGPT and culture of secrecy and current state of ethical oversight board and ethics and ethics council and Facebook and formation of funding and Gemini as global interest company Google acquisition of independent review boards and large language models and McDonagh on medical data and merger with Google Brain and military use and Musk offer and OpenAI and racism and bias and recruiting and restructuring efforts and diffusion models digital assistants Dota Dota 2 Dreams of a Final Theory, (Weinberg) DreamWorks Animation Duplex Durbin, Dick eBay Edge magazine Edmonds, David effective altruism Elixir Studios European Digital Rights Initiative European Union Evil Genius Evil Genius 2 Fable Facebook Acton and bias and buying of startups and Cambridge Analytics scandal concerns tainting reputation of corporate bloat and data access and Facebook AI Research as free Loopt vs.

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He often told staff that he wanted DeepMind to win between three and five Nobel Prizes over the next decade, according to people who worked with him. DeepMind won at CASP in both 2019 and 2020 and open-sourced its protein folding code to scientists in 2021. At the time of writing, more than one million researchers across the world had accessed the AlphaFold Protein Structure Database, according to DeepMind. But science is a slow process, and while Hassabis could one day still win a Nobel, a major discovery using his system remains elusive. Some experts are also skeptical that DeepMind’s protein shape predictions are accurate enough to reliably identify how drug compounds will bind to proteins or that it could save that much time in drug discovery.

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

They created an AI that mines enormous datasets to determine the most likely distance between a protein’s base pairs and the angles of their chemical bonds—aka, the basics of protein folding. They called it AlphaFold. On its first foray into the competition, contestant AIs were given forty-three protein-folding problems to solve. AlphaFold got twenty-five right. The second-place team managed a meager three. If we couple AlphaFold’s progress to Insilico’s GANs and add in the anticipated breakthroughs in quantum computing—another technology being aimed at drug discovery—we’re not far from a world where individually customized medicine will move from science fiction to the standard of care.

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(Author note: Peter’s VC firm is an investor.) researchers only managed to find about five new drug targets a year: Reinhard Renneberg, Biotechnology for Beginners (Academic Press, 2016), p. 281. a biannual competition was created: See: http://predictioncenter.org/. AlphaFold: Read the DeepMind blog about AlphaFold here: https://deepmind.com/blog/alphafold/. Chapter Ten: The Future of Longevity The Nine Horsemen of Our Apocalypse Francis Collins: Dr. Francis Collins shared the stage with Peterat at a 2018 event hosted by the Cura Foundation. Watch the conversation on Longevity and the Morality of Extreme Life Extension here: https://www.youtube.com/watch?

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Abe, Shinzo, 47 Ablow, Keith, 247 Abu Dhabi, 217 abundance, exponential technologies and, 261–63 Abundance (Diamandis and Kotler), xi, 7, 78, 82, 99, 145, 163, 204, 213, 261–62 Abundance Digital, 264, 265 Abundance360, xii, 264, 265 Advano, Aman, 108–9 advertising: AI assistants and, 123–24 big data and, 118 Spatial Web and, 118–20 technological change and, 117–24 aerial ridesharing, 4 AeroFarms, 205 aeroponics, 204, 205 Affectiva, 137 affective computing, 136–38 Affective Computing Group, 137 aging, 170–72 as programmed process, 88–89, 169–70 see also longevity agriculture, reinvention of, 225–26 AI assistants, 35, 37, 132, 135–36, 138, 198 advertising and, 123–24 shopping and, 100–102, 113, 123–24 AI personas, 132 Airbnb, 84, 234 Akonia Holographics, 52 Aleph Farms, 208 Alexa, 100 algorithms, 87, 88 Alibaba, 99, 100, 107, 114 Alipay, 192 Alkahest, 178 Allen, Mark, 178 Allen, Paul, 176 All Nippon Airways (ANA), 26 Alphabet, 46, 89, 162, 235 Project Loon of, 39–40 Verily Life Sciences of, 157 see also Google AlphaFold, 167 AlphaGo, 36 AlphaGo Zero, 36, 37 Alzheimer’s disease, 82, 178 Amarasiriwardena, Gihan, 108–9 Amazon, 4, 21, 47, 100, 107, 108, 114, 119, 127 disruptive business model of, 98–99 Echo of, 35, 101, 132 Project Kuiper and, 40 Amazon Go, 105, 196, 229 ANA Avatar XPRIZE, 26 anandamide, 247 Andreesen, Marc, 32 Andrews, T.

AI 2041: Ten Visions for Our Future
by Kai-Fu Lee and Qiufan Chen
Published 13 Sep 2021

AI can greatly accelerate the speed and reduce the cost of drug and vaccine discovery. For determining protein folding (step 2), in 2020, DeepMind developed AlphaFold 2, which is AI’s greatest achievement for science to date. Proteins are the building blocks of life, yet one aspect of proteins that has remained a mystery is how a sequence of amino acids will fold into a 3D structure to carry out life’s tasks. This is a problem with profound scientific and medical implications and appears well-suited for deep learning. DeepMind’s AlphaFold, trained on a large database of previously discovered 3D protein structures, has demonstrated that it is able to simulate the 3D structure of unseen proteins with similar accuracy to traditional techniques (such as cryo-electron microscopy, mentioned on page 156), which are expensive and can take years for each protein.

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DeepMind’s AlphaFold, trained on a large database of previously discovered 3D protein structures, has demonstrated that it is able to simulate the 3D structure of unseen proteins with similar accuracy to traditional techniques (such as cryo-electron microscopy, mentioned on page 156), which are expensive and can take years for each protein. For this reason, traditional methods have solved less than 0.1 percent of all proteins; thus, AlphaFold may offer a way to rapidly grow the number of solved proteins. AlphaFold has been heralded by the biology community as having solved a “fifty-year-old grand challenge in biology.” Once the protein’s 3D structure is known, one expeditious way to discover effective treatment is repurposing, or trying every existing drug that has been proven safe for some other ailment, to see if one of them can fit into this 3D structure.

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Classification: LCC Q335 .L423 2021 (print) | LCC Q335 (ebook) | DDC 006.3—dc23 LC record available at https://lccn.loc.gov/​2021012928 LC ebook record available at https://lccn.loc.gov/​2021012929 International edition ISBN 9780593240717 Ebook ISBN 9780593238301 crownpublishing.com Book design by Edwin Vazquez, adapted for ebook Cover Design: Will Staehle ep_prh_5.7.1_c0_r0 Contents Cover Title Page Copyright Epigraph Introduction by Kai-Fu Lee: The Real Story of AI Introduction by Chen Qiufan: How We Can Learn to Stop Worrying and Embrace the Future with Imagination Chapter One: The Golden Elephant Analysis: Deep Learning, Big Data, Internet/Finance Applications, AI Externalities Chapter Two: Gods Behind the Masks Analysis: Computer Vision, Convolutional Neural Networks, Deepfakes, Generative Adversarial Networks (GANs), Biometrics, AI Security Chapter Three: Twin Sparrows Analysis: Natural Language Processing, Self-Supervised Training, GPT-3, AGI and Consciousness, AI Education Chapter Four: Contactless Love Analysis: AI Healthcare, AlphaFold, Robotic Applications, COVID Automation Acceleration Chapter Five: My Haunting Idol Analysis: Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), Brain-Computer Interface (BCI), Ethical and Societal Issues Chapter Six: The Holy Driver Analysis: Autonomous Vehicles, Full Autonomy and Smart Cities, Ethical and Social Issues Chapter Seven: Quantum Genocide Analysis: Quantum Computers, Bitcoin Security, Autonomous Weapons and Existential Threat Chapter Eight: The Job Savior Analysis: AI Job Displacement, Universal Basic Income (UBI), What AI Cannot Do, 3Rs as a Solution to Displacement Chapter Nine: Isle of Happiness Analysis: AI and Happiness, General Data Protection Regulation (GDPR), Personal Data, Privacy Computing Using Federated Learning and Trusted Execution Environment (TEE) Chapter Ten: Dreaming of Plenitude Analysis: Plenitude, New Economic Models, the Future of Money, Singularity Acknowledgments Other Titles About the Authors What we want is a machine that can learn from experience.

These Strange New Minds: How AI Learned to Talk and What It Means
by Christopher Summerfield
Published 11 Mar 2025

During the last ten years, as AI research has accelerated to warp speed, deep learning systems have offered many amazing new insights that lie well over the horizon of human ken. One astonishing example is AlphaFold, built by the AI research company DeepMind. AlphaFold is a neural network that can accurately predict how the sequence of amino acids in a protein will determine its 3D structure, an important problem for biochemistry and medicine. For decades, legions of top human scientists had inched painstakingly forwards, gradually improving their predictions by conducting wet lab experiments to verify or falsify mathematical models of protein structure. Every year they swapped notes in an annual competition. In 2020, AlphaFold swooped in and blew the experts out of the water, making on average only an 8% error in prediction (the nearest competitor was closer to 50% error).

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Geoffrey Hinton, who is introduced early in the book as the researcher with the best claim to have invented deep learning, was awarded the Nobel Prize in Physics in 2024. Almost everyone agreed that Hinton deserved a Nobel, but the award left many actual physicists scratching their heads about what neural networks have to do with their field. A day later, my friend Demis Hassabis, who founded DeepMind, and John Jumper, first author on the AlphaFold paper, shared the Nobel Prize in Chemistry for their use of AI to predict how proteins fold. Less consternation here – this was one of the most significant scientific advances of our times. There are still five months to go until these pages see the light of day. No doubt much more will transpire before then.

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A B C D E F G H I J K L M N O P Q R S T U V W X Y Z A action space, LLM, 274–5 Adams, Douglas: The Hitchhiker’s Guide to the Galaxy, 1–2, 325 Adept AI, 294 advertising, 188, 220–21, 223, 224, 248, 249, 261–2, 263, 314 affective states, 122, 124 #AIhype, 308–9, 311 AI Safety Institute, 311n, 346 algorithms, 21, 38, 59, 75, 76, 99, 249–50, 263, 278, 279, 326–31 alignment, LLM, 179–238 alignment problem, 322 AlphaCode, 287 AlphaFold, 3–4, 347 AlphaGo, 4, 267 Altman, Sam, 1, 2, 5, 162, 222–3 alt-right, 181–2 American Sign Language (ASL), 58, 60, 61, 63, 65 Anthropic, 5, 51, 192, 209, 215, 235, 251, 342 anthropomorphism, 71–2, 129, 264 Applewhite, Marshall, 217, 219 application programming interface (API), 283–5, 290–91, 292, 295, 300, 301 Aristotle, 13, 16, 32; De Interpretatione (‘On Interpretation’), 73 Artificial General Intelligence (AGI), 1, 136, 140 Artificial Intelligence (AI) Artificial General Intelligence (AGI), 1, 136, 140 assistants, personal, 7, 227–8, 243, 246–7, 251, 263, 272, 292, 295, 297, 300, 301, 332, 334, 335, 342–3 deep learning systems, see deep learning systems empiricist tradition and, see empiricist tradition ethics/safety training, 179–238, see also alignment future of, 239–338 general-purpose, 310 Godfathers of, 6, 305, 310 instrumentality, 246, 247, 267, 330, 332 large language models (or LLMs), see large language models (or LLMs) military/weapons, 292, 309, 314–16, 325 multimodal, 177, 230, 242 natural language processing (NLP), see natural language processing (NLP) neural networks, see neural networks origins of, 14, 9–117 rationalist tradition and, see rationalists/rationalist tradition rights of, 126–7 symbolic, 27, 41, 45, 72, 113, 277–8, 334 thinking and, 119–78 tyranny and, 336–7 Ask Delphi, 244 astroturfing, 224 attention, 104–9, 112–13, 115, 116, 117, 160, 164, 167 Austin, J.

This Is for Everyone: The Captivating Memoir From the Inventor of the World Wide Web
by Tim Berners-Lee
Published 8 Sep 2025

Biochemists looking to speed the development of new medical treatments wanted to model this molecular origami, but computers had struggled with the problem. Demis and his team, building on their insights into chess and Go, produced AlphaFold, a neural net which predicted protein shapes with unprecedented accuracy. In the past, it might take a graduate researcher their entire PhD to figure out a single protein. AlphaFold could do it in minutes. For this, Demis was awarded the 2024 Nobel Prize in Chemistry, and, a little more than ten years after he delivered his Web Foundation anniversary lecture, we were once again invited to a celebratory dinner hosted by Google.

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It is the third leg, the data, that is in many cases the missing piece of the puzzle. For this reason, I see the semantic web – the web of data – as being complementary to the work Demis is doing. Having a world of carefully organized data is very helpful to AI, whatever approach you are using. To solve the protein-folding problem, for example, Demis had trained AlphaFold on vast libraries of known amino-acid sequences and their related protein structures, which acted as the practice questions and answer keys for the biochemical curriculum. There remained a vast ocean of data out there from which this new form of AI might learn. Web data, in particular, represented an open frontier and, a few years after Demis’s 2014 talk at Google, the OpenAI team began to train powerful new language models using almost the entire web as an input.

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The printing, copying, redistribution, or retransmission of this Content without express written permission is prohibited Index Aadhaar ref1 Aaron, Swartz ref1 Abou-Zahra, Shadi ref1, ref2 Abramatic, Jean-François ref1 academic papers, JSTOR ref1 accessibility ref1, ref2, ref3, ref4, ref5, ref6 ActiveX ref1 activism, hostile (Edelman Trust Barometer) ref1 Adam Smith lecture ref1 addiction, social media ref1, ref2, ref3, ref4 Addis, Louise ref1 Adelman, Len ref1 Adobe ref1 advertisements browsers ref1 cookies ref1 first clickable ref1, ref2 microtargeting ref1, ref2 pop-up ref1 privacy ref1 social media ref1, ref2, ref3 third-party distribution networks ref1, ref2 affordability ref1 Africa ref1, ref2, ref3 agents ref1, ref2, ref3, ref4 AJAX platform ref1 Akamai Technologies ref1, ref2 al-Sisi, Abdel Fattah ref1 Alexa ref1, ref2 Alexa Internet ref1 Alexander, Helen ref1 algorithms consistent hashing ref1 PageRank ref1 public key cryptography ref1 social media ref1, ref2, ref3, ref4, ref5, ref6 Alibaba ref1, ref2 Alice in Wonderland (Carroll) ref1 ‘alignment problem’ ref1 AlphaFold ref1, ref2 AlphaGo ref1 AlphaZero ref1 AltaVista ref1, ref2 ‘always on’ ref1 Amazon ref1, ref2, ref3, ref4, ref5, ref6 Andreessen Horowitz venture-capital fund ref1 Andreessen, Marc ref1, ref2, ref3, ref4, ref5, ref6, ref7 Android ref1 Anklesaria, Farhad ref1 Anonymous ref1 AOL ref1, ref2, ref3, ref4 AOL hometown ref1 Apache HTTP servers ref1 Apollo naming system ref1, ref2 Apple anti-trust lawsuits ref1 apps ref1 business model ref1 HyperCard ref1 interoperability ref1 iPhone ref1, ref2, ref3 Jobs leaves ref1 Jobs returns ref1 partnerships ref1 Siri ref1 standards ref1 WHATWG ref1, ref2, ref3 Applied Semantics ref1 apps interoperability ref1, ref2 killer apps ref1 smartphones ref1 web apps ref1 Arab Spring ref1 Archer, Mary ref1 archives ref1, ref2 Arena browser ref1, ref2 ARPANET ref1 Arroyo, James ref1 artichokes ref1 artificial intelligence (AI) AI ‘agents’ ref1, ref2, ref3 ‘AI winter’ ref1, ref2 authors and musician’s concerns ref1 autonomy ref1 Charlie ref1, ref2 copyright infringement ref1 DeepMind ref1 Ditchley Summit ref1, ref2, ref3 early development ref1 future possibilities ref1, ref2 global summits ref1 GOFAI ref1 GPTs (Generative Pre-trained Transformers) ref1, ref2, ref3, ref4, ref5 ‘human in the loop’ ref1 Inflection.AI ref1 intention economy ref1 military applications ref1 need for inclusivity ref1 neural networks ref1, ref2, ref3, ref4 OpenAI ref1, ref2, ref3, ref4, ref5 paradigm shift ref1 RAGs (Retrieval-Augmented Generation systems) ref1 reinforcement learning from human feedback ref1 search engines ref1 semantic web ref1 simplified text ref1 singularity ref1 speed of development ref1 superintelligence ref1 trust ref1 see also ChatGPT Asimov, Isaac ref1, ref2, ref3, ref4 Association for Computing Machinery (ACM) ref1 atheism ref1, ref2 Athumi ref1 Atkinson, Bill ref1 Attenborough, David ref1 attention economy ref1, ref2, ref3, ref4 attention spans ref1 audio descriptions ref1 audiobooks ref1 augmented reality ref1 Australia ref1, ref2, ref3 authentication ref1 authoritarians ref1, ref2, ref3, ref4 Autodesk ref1 Baidu ref1 bar-code scanners ref1 Barabasi, Albert-Laszlo ref1 Barlow, John Perry ref1, ref2, ref3 Barton, Nick ref1, ref2 BBC ref1, ref2, ref3 Beihang University, Beijing ref1, ref2 Beijing ref1 Belgium ref1, ref2 Bell Labs ref1 Bellingcat organization ref1 Bengio, Yoshua ref1 Berkman Klein Center for Internet and Society ref1, ref2 Berners-Lee, Alice (daughter) ref1, ref2, ref3, ref4 Berners-Lee, Ben (son) ref1, ref2, ref3, ref4, ref5 Berners-Lee, Conway (father) ref1, ref2, ref3, ref4, ref5 Berners-Lee, Mary Lee (mother) ref1, ref2, ref3, ref4, ref5, ref6, ref7 Berners-Lee, Rosemary see Leith, Rosemary Berners-Lee, Tim awards ref1, ref2, ref3, ref4, ref5, ref6 character ref1, ref2, ref3 childhood and education ref1, ref2, ref3, ref4 children ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8 cottage in Wales ref1 D.G.

Growth: A Reckoning
by Daniel Susskind
Published 16 Apr 2024

As Hassabis put it, predicting the 3-D shape of proteins from the one-dimensional amino acid sequence became a sort of Fermat’s Last Theorem for biology – a seductively simple claim, captured in the field’s metaphorical marginalia, that turned out to be extremely difficult to solve. So he built AlphaFold to do just that. In 2018, the first version of the program attended (in virtual form) an event called the ‘Critical Assessment of Protein Structure Prediction’, or CASP, a biannual global competition where more than a hundred groups of researchers from around the world compete to predict 3-D protein shapes from those one-dimensional strings. It won the event. Then, in 2020, AlphaFold 2 entered the competition and not only won, but achieved a level of accuracy that astounded the profession.88 Again, this was not simply a fun bit of biological origami: the shape of a protein is key for understanding what it does, how it causes diseases, how it interacts with drugs.

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Far more exciting, though, are the technologies that generate entirely new ideas. The field of AI-enabled drug discovery, in its infancy only a few years ago, is now well-established. Moderna, for instance, was celebrated for using AI to develop their Covid-19 vaccine during the pandemic.81 But perhaps the most exciting development in this space has been AlphaFold, a system developed by DeepMind to tackle the ‘protein folding problem’, a benign name for one of the most profound and difficult questions in biology. DeepMind already had a spread of achievements behind it before turning to biology, from game-playing triumphs (culminating in AlphaZero, a system that taught itself in a few hours to play go, shogi and chess at superhuman levels) to more practical victories (for instance, reducing Google’s data server cooling bill by 40 per cent).

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To find a specific word or phrase from the index, please use the search feature of your ebook reader. Abrahamovitz, Moses, 287n31 Acemoglu, Daron, 45, 47, 211 Akerlof, George, 158 ‘Alignment Problem’, 124–7 Allen, Robert, 5–6, 209–10, 278n5 Alphabet (company), R&D (Research & Development), 188 AlphaFold (AI system), 200–2 Amazon, patents, 182 Anfinsen, Christian, 201 anti-globalization movements, 119 Apollo space mission, 189–90 Arrhenius, Svante, 100 artificial intelligence (AI): applications, 108–9; and medicine, 200; ‘paperclip maximiser’ thought experiment, 124–5; social narratives around, 244–5 Athenaeus, 178 Atkinson, Anthony, 104 Attenborough, David, 150 Attlee, Clement, 263 Australia, patents, 182 automation: threat of, 107–10; tradeoffs, 241–5 Autor, David, 116 Bacon, Francis, 46 Bankman-Fried, Sam, 261 Barnes & Noble, 182 Becker, Gary, 36 Beckerman, Wilfred, 160, 167, 222, 313n41 Bell, Daniel, 145 Bell Labs (company), 189 Bellman, Richard, 159 Berlin, Isaiah, 91, 145, 166, 173, 202, 305n53, 320n50 Bernanke, Ben, 76 Berne Convention, 181 Berners-Lee, Tim, 185 Beveridge, William, 77 Bhagwati, Jagdish, 116 Black Death, 16–18 black markets, and GDP, 134–5 Blair, Tony, 115, 268, 311n15 Blake, William, 209 Bohr, Niels, 191 Boisguillenber, Pierre Le Pesant de, 62 Bostrom, Nick, 125, 259 Boulding, Kenneth, 316n3 Bowman, James, 189 Boyle, James, 184 Bretton Woods Conference, 66 BRIC group, 174–5 Britain: Brexit referendum, 263–4; National Health Service (NHS), 128; Office for National Statistics, 134–5 Broadberry, Stephen, 278n10 capital fundamentalism, 30, 33 capitalism, and economic growth, 218–20 Caplan, Bryan, 143 carbon tax, 241 Carroll, Lewis, Through the Looking-Glass, 22 Chang, Ha-Joon, 246 Chang, Ruth, 315n59 China: industrial robots, 211; joins the WTO, 256–7 Chivers, Tom, 150 Chu, Steven, 151 Churchill, Winston, 95, 263 citizen assemblies, 266 Clark, Colin, 64 Clark, Gregory, 4–5, 10, 12–13, 278n5 climate change, and economic growth, 97–101, 235–41 Climate Change Conference (Egypt, 2022), 237 Club of Rome, The Limits of Growth (1972), 153–4 Cold War, and economic growth, 67–73 Coleridge, Samuel Taylor, 14 Collins, Robert, 333n2 communities, cost of globalization, 118–19 comparative advantage, theory of, 114–15 consensus conferences, 266 copyright, 179, 181 Covid-19 pandemic: and key workers, 138; and modelling, 224; technological consequences of, 212–16; vaccine, 200, 214; and work, 75 Cowen, Tyler, 85–6, 175, 187 Coyle, Diane, 293n3 Creative Commons (organization), 185 Daley, William, 293n6 Daly, Herman, Toward a Steady-State Economy (1973), 154 Darwin, Charles, 280n30 DeepMind (company), 189, 200, 202, 244 Deepwater Horizon oil spill (2010), 136 degrowth movement, 149–51 DeLong, Brad, Slouching Towards Utopia, 176 Dene, William de la, 17 Dennett, Daniel, 281n35 Diamond, Jared, Guns, Germs, and Steel, 45–6 Diderot, Denis, 179 digital technology, and political power, 111–14 diminishing returns, law of, 20–2 Domar, Evsey, 28, 58, 73, 285n20, 294n11, 294n12 see also Harrod-Domar model Dorling, Danny, 327n2 Dorsey, Jack, 113 DuPont (company), 189 Dworkin, Ronald, 112 Easterlin, Richard, 86 Easterly, William, 30 econometrics, 42–3 Economist, The (journal), 33 Edward III of England, 17 ‘effective altruism’, 259 Ehrlich, Paul, The Population Bomb (1968), 154 Einstein, Albert, 191, 196 Eliot, T.

More From Less: The Surprising Story of How We Learned to Prosper Using Fewer Resources – and What Happens Next
by Andrew McAfee
Published 30 Sep 2019

The cells in our bodies are assembly lines for proteins, but we currently understand little about how these assembly lines work—how they fold a two-dimensional string of amino acids into a complicated 3-D protein. But thanks to digital tools, we’re learning quickly. In 2018, as part of a contest, the AlphaFold software developed by Google DeepMind correctly guessed the structure of twenty-five out of forty-three proteins it was shown; the second-place finisher guessed correctly three times. DeepMind cofounder Demis Hassabis says, “We [haven’t] solved the protein-folding problem, this is just a first step… but we have a good system and we have a ton of ideas we haven’t implemented yet.”

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“We never had a technology before that could educate”: Sara Castellanos, “Google Chief Economist Hal Varian Argues Automation Is Essential,” Wall Street Journal, February 8, 2018, https://blogs.wsj.com/cio/2018/02/08/google-chief-economist-hal-varian-argues-automation-is-essential/. “We [haven’t] solved the protein-folding problem, this is just a first step”: Ian Sample, “Google’s DeepMind Predicts 3D Shapes of Proteins,” Guardian, December 2, 2018, https://www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins. increase the energy efficiency of data centers by as much as 30 percent: “Safety-First AI for Autonomous Data Centre Cooling and Industrial Control,” DeepMind, accessed March 25, 2019, https://deepmind.com/blog/safety-first-ai-autonomous-data-centre-cooling-and-industrial-control/.

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Abolition of the Slave Trade Act (1807), 37 abolitionist movement, 37 accountability, 175 Acemoglu, Daron, 159, 160, 161 additionality, 260 affluence, 10–11, 62 Africa, 168 agriculture, 12, 13, 17–18, 92, 100, 252–53 Agriculture Department, US, 82 air pollution, 23, 54–55, 61, 95, 145, 146, 147–48 airlines, 110–11 Alphabet, 235, 236 AlphaFold, 239–40 aluminum, 80, 90, 120 Amazon, 206 ammonia, 30–31 anarchy, 130 Anders, Bill, 53 Android, 235, 236 Annan, Kofi, 150 A.P. Møller-Maersk, 257 Apollo 8 mission, 53–54 apparent consumption, 78–79 Apple, 102, 111, 169, 235, 257 Applebaum, Anne, 218 Arab oil embargo, 161 Ardekani, Siamak, 75 artificial intelligence, 205 Asch, Solomon, 226 Atacama Desert, 17 “Atoms for Peace,” 58–59 Audubon, John James, 43, 258 Austin, Benjamin, 254 Ausubel, Jesse, 4–5, 75, 76, 78, 183 authoritarianism, 174, 217–18, 220 automobiles, 161–63 back to the land movement, 67–68, 91–93 bananas, 24 Baron, Jonathan, 127 BASF, 31 Beach, Brian, 41 beefalo, 182 Bergius, Friedrich, 31 Berzin, Alfred, 164 Bezos, Jeff, 206 Bhattacharjee, Amit, 127 Bishop, Bill, 227 Bismarck, Otto von, 225 bison, 44–46, 96, 152–53, 183 Blake, William, 40–41 Blomqvist, Linus, 270 Bloom, Paul, 210 blue whales, 47 Borlaug, Norman, 31–32, 262 Bosch, Carl, 31 Boulding, Kenneth, 63–65 Boulton, Matthew, 15–16, 20, 121, 206 Bowling Alone (Putnam), 213 Brand, Stewart, 67–68, 182, 183 Brandeis, Louis, 259 Brazil, 173, 174 Brynjolfsson, Erik, 112, 205 Bump, Philip, 224 cap-and-trade programs, 143–44, 145, 187, 188 capitalism, 2–3, 4, 5, 36, 99–123, 113, 125–39, 141, 151, 158–59, 161, 167–68 critiques of, 126–31 defining of, 115–18 negatives of, 142–43 spread of, 170–73 carbon capture systems, 187 carbon dioxide, 185, 188–89 carbon offsets, 259–60 carbon taxes, 187, 249–50, 252, 257, 259 Caro, Robert, 29n Case, Steve, 256 CBS Evening News with Walter Cronkite, 53 central planning, 116, 122, 170 cerium, 107 Chávez, Hugo, 134–35 Chicago and North Western Railway, 105–06 child labor, 35, 38–39, 167 child mortality, 196–97 China, 85, 93–94, 106, 110, 133, 145, 154, 172, 174, 185 chlorofluorocarbons, 149–50, 185, 228, 249 cholera, 22–23, 26 Christensen, Clay, 265 Christmas Carol, A (Dickens), 24 chromium, 72 Church, George, 182 Cichon, Steve, 101–02 circle of sympathy, 176 Civil War, US, 38 Clapham, Phillip, 163 Clark, Gregory, 10–11, 20 Clean Air Act, 66, 95, 122, 143, 147, 161 Clean Water Act (1972), 66, 190, 252–53 climate change, 60, 158, 185, 228, 243, 248, 257, 269, 274 Clinton, Hillary, 201, 224 Closing Circle, The (Commoner), 64 coal, 16, 18, 19, 40, 41, 56 as finite resource, 48–49 Coal Question, The (Jevons), 48, 49 Coase, Ronald, 143 collusion, 129 colonialism, 35, 39–40, 167 Commoner, Barry, 64 commons, 183–84 communism, 133, 172 Communist Manifesto, The (Marx and Engels), 21 comparative advantage, 19n competition, 109–10, 116, 129, 203 computer-aided design, 113 computers, 141 concentration, 199–210, 218, 224 economic, 202–03 industrial, 204 of wealth, 205–07 Condition of the Working Class in England, The (Engels), 21 Congo Free State, 39 conservationists, 95–96 conspicuous consumption, 152 Constitution, US, 38 consumption, 63–64, 88–90 contract enforcement, 116 Cooke, Earl, 60 Coors, 101 copper, 79, 80, 90, 107, 120 coprolite, 18 Cordier, Daniel, 106–07 Corn Laws, 18, 172 Corporate Average Fuel Economy (CAFE) standards, 162 corporatism, 129 corruption, 175 cotton industry, 38 cotton textiles, 19 Cramer, Kathy, 221 CRIB, 62–68, 87–97 cronyism, 129 Crookes, William, 30 crude oil, 58 “Crude Oil” (GAO), 103 Crutzen, Paul, 150 Cuba, 133 Cutler, David, 28 Cutter, Bo, 105 Cuyahoga River, 54 Daimler, Gottlieb, 26–27 Dana, Jason, 127 Davenport, Thomas, 27 de-extinction movement, 182 death penalty, 176 deaths of despair, 214, 216, 219–20, 247 Deaton, Angus, 210, 213–14, 220 DeepMind, 239–40 deforestation, 43, 184–85 degrowth, 63–64, 88 demand, 50–51 dematerialization, 4–5, 71, 72–73, 75–85, 87, 125, 141, 144, 151–52, 160, 167, 168, 235, 247–48, 259 causes of, 99–123 paths to, 110–11 Demick, Barbara, 94 democracy, 174 Democracy in America (Tocqueville), 89–90 democratic socialism, 133–34 Deng Xiaoping, 170 Denmark, 117–18 developing countries, 56 Devezas, Tessaleno, 73 Dickens, Charles, 24 digital tools, 234–35 Dijkstra, Lewis, 199 Dimon, Jamie, 256 Ding Xuedong, 253 disconnection, 211–29, 247, 253–54, 255, 270–71 diversity, 216–17 Dodge, Irving, 45 Donora, Pa., 41, 55, 66, 145 Dragusanu, Raluca, 268 data centers, 240 Duolingo, 236 DuPont, 149 Durkheim, Emile, 215–16, 219 Earth Day, 3, 53, 60–61 Earthrise, 53–54 Ecology as Politics (Gorz), 63–64 Edison, Thomas, 27 education, 177, 195, 256 Ehrlich, Paul, 55, 59, 62, 65, 71–72, 75, 151, 244–45 Eisenhower, Dwight, 58 electrical power, 26–28, 29, 30, 36 Elephant Graph, 221–23 elephants, 153–54 Elop, Stephen, 102 Emancipation Proclamation, 38 emancipative values, 176 energy consumption, 58–60, 59 Energy Information Administration, US, 103 Engels, Friedrich, 21 Engels Pause, 20, 23 England, 18–20, 22, 38 abolitionist movement in, 37 air pollution in, 41 population of, 10–11 population versus wages in, 20 Enlightenment, 122–23 Enlightenment Now (Pinker), 37, 176, 179 environmental movement, 53, 65, 68, 122 Environmental Protection Agency, 66, 95 ephemeralization, 70–71 epidemiology, 22 Essay on the Principle of Population, An, (Malthus), 8–9, 10, 13 Evans, Benedict, 173 externalities, 142 extinctions, 35, 36, 42–43, 61, 96, 151–52, 167, 181–82 Factfulness (Rosling), 179 Factory Act (1833), 38 factory ships, 47 Fair Trade certification, 268 false imprisonment, 175 famine, 12, 13, 61, 62, 69 Famine 1975!

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

This capability, unimaginable at the turn of the century, will be a key component of virtual reality tools in the future: AI is on the way to constructing convincing alternative realities. In late 2018, DeepMind researchers at a conference in Mexico announced AlphaFold, a system to understand a fundamental issue in medicine called protein folding.3 Protein folding involves predicting the shape that certain molecules will take on. Understanding the shapes that they will form is essential for progress in treating conditions such as Alzheimer’s disease. Unfortunately, the problem is fearsomely difficult. AlphaFold used classic machine learning techniques to learn how to predict protein shapes, and represents a promising step on the road to understanding these kinds of devastating conditions.

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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?

What We Owe the Future: A Million-Year View
by William MacAskill
Published 31 Aug 2022

First, a country could grow the size of its economy indefinitely simply by producing more AI workers; the country’s growth rate would then rise to the very fast rate at which we can build more AIs.48 Analysing this scenario, Nordhaus found that, if the AI workers also improve in productivity over time because of continuing technological progress, then growth will accelerate without bound until we run into physical limits.49 The second consideration is that, via AGI, we could automate the process of technological innovation. We have already seen this recently to some extent: DeepMind’s machine-learning system AlphaFold 2 made a huge leap towards solving the “protein folding problem”—that is, how to predict what shape a protein will take—reaching a level of performance that had been regarded as decades away.50 If AGI could quite generally automate the process of innovation, the rate of technological progress we have seen to date would greatly increase.

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“This computational work represents a stunning advance on the protein-folding problem, a 50-year-old grand challenge in biology. It has occurred decades before many people in the field would have predicted. It will be exciting to see the many ways in which it will fundamentally change biological research.” Professor Venki Ramakrishnan, Nobel laureate and president of the Royal Society 2015–2020, quoted in AlphaFold Team (2020). 51. Aghion et al. 2019, Section 9.4.1, examples 2–4. More generally, the arguably empirically most plausible explanation of economic growth—as captured in so-called semiendogenous growth models (for a review, see Jones [2021])—implies accelerating growth once AI systems can substitute for human labour, assuming that the population of AI workers could grow faster than the current population of humans.

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nuclear winter, 129 postcatastrophe recovery of, 132–134 slavery in agricultural civilisations, 47 suffering of farmed animals, 208–211 technological development feedback loop, 153 AI governance, 225 AI safety, 244 air pollution, 25, 25(fig.), 141, 227, 261 alcohol use and abuse, values and, 67, 78 alignment problem of AI, 87 AlphaFold 2, 81 AlphaGo, 80 AlphaZero, 80–81 al-Qaeda: bioweapons programme, 112–113 al-Zawahiri, Ayman, 112 Ambrosia start-up, 85 Animal Rights Militia, 240–241 animal welfare becoming vegetarian, 231–232 political activism, 72–73 the significance of values, 53 suffering of farmed animals, 208–211, 213 wellbeing of animals in the wild, 211–213 animals, evolution of, 56–57 anthrax, 109–110 anti-eutopia, 215–220 Apple iPhone, 198 Arab slavery, 47 “Are Ideas Getting Harder to Find?”

Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
by Karen Hao
Published 19 May 2025

One of the most famous examples: ResNet, among the most widely used neural networks in the world, was published by Chinese researchers in Microsoft’s Beijing office. ResNet not only underpins major computer-vision, speech-recognition, and language systems but also was a core ingredient of the first version of DeepMind’s AlphaFold, an AI system released in 2018 that could predict a protein’s 3D structure from its amino acid sequence, crucial for accelerating drug development and understanding disease. (DeepMind’s subsequent advancements in AlphaFold, using a different neural network, would earn Demis Hassabis and another senior research scientist at DeepMind a 2024 Nobel Prize in Chemistry.) And yet, in October 2023, the ideas championed by the Closed side would gain their greatest endorsement yet when they surfaced in the Biden administration’s AI executive order.

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See also data privacy; existential risks alignment and, 122–23, 124, 145–46, 316–18 effective altruism and, 55–56, 230–34, 321–22 Frontier Model Forum, 305–6, 309 Senate Judiciary Hearing, 301–3, 307–9, 314–15 thresholds, 301–2, 305–8, 310–11 AI Scientist, 183, 318–19, 325, 347, 375 “AI takeoff,” 232 “AI winter,” 97, 435n Alameda Research, 231 Algorithmic Justice League, 161 algorithms, 51–52, 56, 373–74 Algorithms of Oppression (Noble), 162 Alibaba, 15, 159 Alignment Manhattan Project, 315–18 Allen & Company, 67–68 Alphabet, 105 AlphaFold, 309–10 AlphaGo, 59, 93 Altman, Annie, 43–45, 326–40, 352–55, 406, 458–59n appeals to family for financial help, 327, 331–32 death of father, 329–31 early life and education of, 29, 30, 328–29 mental health struggles of, 44–45, 329–30, 331–32, 339–40 New York magazine article, 326–27, 328–29, 332–33, 336–40, 343, 352 physical health struggles of, 329, 332–33 sexual abuse allegations of, 3, 44–45, 327–28, 334–38, 352–53, 406 sex work of, 326, 332–36 Altman, Jack, 29, 30, 35–36, 41, 69, 185, 327–28, 331, 336 Altman, Jerold “Jerry,” 29–31, 44, 329–31, 332 Altman, Max, 29, 30, 36, 326, 327–28, 331 Altman, Sam AI chip company plan, 3, 377–78 background of, 23, 29–30 benefits of AGI, 19, 405 birth and early life of, 29, 30–31 board of directors and, 40, 252–53, 320–25, 375–76 leadership questions, 345–65 business structure of OpenAI, 13–14, 61–64, 66–67, 86, 402–3, 407 ChatGPT, 260, 261, 262, 280, 346 commercialization plan, 66–67, 150–51 compute phases, plan, 278–81 conflicts and rifts at OpenAI, 149, 150–51, 233–34, 313–16, 396 congressional testimony of, 301–3, 314–15 education of, 30–32 effective altruism ideology and, 233–34 equity crisis and, 388–90, 392–96 firing and reinstatement of, 1–12, 14, 364–73 the investigation, 369–70, 375–76, 377, 392 founding of OpenAI, 12–13, 26–28, 46, 47–51, 53–54 fundraising, 61–62, 65–68, 71–72, 132, 141, 156, 262, 320–21, 331, 367, 377, 405 GPT-3, 133–34, 278–79 GPT-4, 246, 248–52, 279, 346, 383–84, 386, 390–91 Graham and, 28, 32, 36–39, 40, 69 “Intelligence Age,” 19, 405 Jobs comparisons with, 2, 34, 35, 37 Johansson crisis, 382, 390–92, 393 leadership of, 64–65, 69–70, 75, 141–44, 243–44, 354–55, 403–4 leadership behavior, 345–60, 361–65, 382–83, 385–86 Loopt and, 32–37, 43, 68 Manhattan Project, 146–47, 315–17 Mayo’s office design and, 74 media relations of, 33, 34, 383 mission of OpenAI, 5, 400–402 MIT Technology Review and, 86–87 on Napoleon, 399–400 net worth of, 35, 44, 188, 389, 390 other investment projects of, 3, 185–88 paranoia of, 147–48 personality of, 31, 34, 42–45, 333, 346 politics of, 41–42, 43, 62 research road map, 59, 175–78 retreat of October 2022, 256–57 Scallion, 379–80, 380, 382 sexuality of, 31, 41 sister Annie and, 43–45, 326–40, 385–86, 406 sexual abuse allegations, 3, 44–45, 327–28, 334–38, 352–53, 356 success formula of, 32–35, 37, 142–44 vision for OpenAI, 9, 83, 142–43, 262 World Tour of, 312, 313, 337 at Y Combinator (YC), 23, 27–28, 32, 34, 36–38, 39, 43, 68–69, 75, 141, 142, 185, 186, 187–88, 321 altruism, 13, 14, 400.

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See child sex abuse material CUDA (Compute Unified Device Architecture), 61 curie, 150 Curie, Marie, 150 Curry, Steph, 231 cybersecurity, 114, 147, 148, 179–80, 380 Cyc, 97 D DAIR (Distributed AI Research Institute), 414–15, 419 Dalí, Salvador, 234 DALL-E, 11, 114, 234–39, 241–42, 258–59, 269 avocado armchair, 235, 237–38 Damon, Matt, 317–18 D’Angelo, Adam, 321 Altman’s firing, 7, 11, 366, 367 Altman’s leadership behavior, 324–25, 352, 357, 359–60, 361–62 Dartmouth Summer Research Project (1856), 89–90, 94 data annotation, 15, 178, 189–90, 192–223, 414–17 Kenya workers, 15, 18, 190–92, 206–13, 415–17 Scale AI, 202–6, 213–14 self-driving cars, 193–95, 202–6, 214–15 Venezuela workers, 195–96, 198–202, 203–4, 218 data centers, 15, 274–78 Altman’s compute phases, 278–81 carbon emissions, 79–80, 159–60, 171–73 in Chile, 285–91, 295–99 energy usage, 77, 80, 274–78, 280–81, 288–90, 294 Google, 274–75, 285–91, 295–96 in Uruguay, 291–96 “data colonialism,” 103–4 data filtering, 137, 155, 177–78 Dataluna, 289–90 data privacy, 19–20, 33, 56, 103, 136, 186, 301, 308, 310, 413, 416 data scraping, 102–3, 114, 134–38, 151–52, 182–84, 384 “data swamps,” 137–38, 212–13 Data Workers’ Inquiry, 415–17 davinci, 150 da Vinci, Leonardo, 150 Dean, Jeff, 25, 158, 161–62, 163–65, 170–72 deepfakes, 79–80, 239, 391 deep learning, 98–101 discriminatory impacts of, 57, 108–9 ImageNet, 47, 100–101, 117–18, 259 limitations and risks of, 106–10 DeepMind, 6, 17, 24–26, 48, 66, 158–59, 261–62, 384–85 AlphaFold, 309–10 AlphaGo, 59, 93 OpenAI and ChatGPT, 114, 119–20, 132, 159, 261–62 scaling, 132, 158–59 Democratic Party, 41, 231 Dempsey, Jessica, 104n dense neural networks, 177–78 Deployment Safety Board (DSB), 248, 323–24, 346, 350, 362, 363 Desmond-Hellmann, Sue, 376 Díaz Bejarano, Nicolás, 297–99 diffusion, 235–36, 375 Stable Diffusion, 114, 137, 236, 242, 284 Digital Realty, 274 disaster capitalism, 189–223 discriminatory impact, 51–52, 57, 108–9, 114, 137, 161–64, 179, 310, 419, 432n dissolving empire, 418–19 distillation, 177, 307 distress passwords, 149 Divorce, The, 156–57, 181, 213, 230, 233, 242 DNNresearch, 47, 50, 98–99, 100 Doctor Strange (movie), 303 Doomers (Doomerism), 233–34, 250, 267–68, 305–6, 308, 310, 311, 314, 315, 317–18, 319, 377, 387, 388–90, 396, 402, 403–4, 419 doomsday scenario, 26–27 Dorador, Cristina, 283 Dota 2, 66–67, 71, 129, 144–45, 244–45 Dowling, Steve, 154, 256, 382–83 doxing, 303 drinking water.

MegaThreats: Ten Dangerous Trends That Imperil Our Future, and How to Survive Them
by Nouriel Roubini
Published 17 Oct 2022

“You can get into semantics about what does reasoning mean, but clearly the AI system was reasoning at that point,” says New York Times journalist Craig Smith, who now hosts the podcast Eye on AI.5 A year later, AlphaGo Zero bested AlphaGo by learning the rules of the game and then generating billions of data points in just three days. Deep learning has progressed with mind-bending speed. In 2020, Deep Mind’s AlphaFold2 revolutionized the field of biology by solving “the protein-folding problem” that had stumped medical researchers for five decades. Besides probing massive volumes of molecular data on protein structures, AlphaFold deployed “transformers,” an innovative neural network that Google Brain scientists unveiled in a 2017 paper. Resolving the protein-folding problem opens the door to significant new bio-medical breakthroughs. AI-generated artistic initiatives have earned applause. “We have taught a computer to write musical scores,” Gustavo Diaz-Jerez, a software consultant and pianist, told the BBC in 2017.

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A revolutionary approach deployed messenger RNA, or mRNA, that teaches cells how to mobilize our bodies’ immune responses. For anything resembling a happy ending to happen, computers poised to displace us must come to our rescue. We must hope that very rapid development of vaccines will defend us against new viruses. I marvel at an accelerating pace of biomedical discoveries. In 2020, DeepMind’s AlphaFold solved the protein-folding problem that perplexed experts for half a century. It augurs well for accelerating progress against other diseases. Success would improve accessibility and lower costs for prevention, diagnosis, and treatment of all sorts of diseases. Breakthroughs on climate change could deliver cascading benefits.

Hope Dies Last: Visionary People Across the World, Fighting to Find Us a Future
by Alan Weisman
Published 21 Apr 2025

New Republic, January 2, 2024. https://newrepublic.com/article/177572/democrats-climate-activists-collision-course-2024. Google. “White Paper | Google’s Carbon Offsets: Collaboration and Due Diligence.” https://static.googleusercontent.com/media/www.google.com/en//green/pdfs/google-carbon-offsets.pdf. Google Deepmind and EMBL-EBI. “AlphaFold Protein Structure Database.” https://alphafold.ebi.ac.uk. Google Research. “Flood Forecasting.” https://sites.research.google/floodforecasting. ———. “John C. Platt.” https://research.google/people/john-c-platt. ———. “Machine Intelligence.” https://research.google/research-areas/machine-intelligence. ———.

A World Without Work: Technology, Automation, and How We Should Respond
by Daniel Susskind
Published 14 Jan 2020

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al., “Human-Level Control Through Deep Reinforcement Learning,” Nature 518 (25 February 2015): 529–33. 28.  David Autor, Frank Levy, and Richard Murnane, “The Skill Content of Recent Technological Change: An Empirical Exploration,” Quarterly Journal of Economics 118, no. 4 (2003): 129–333. Another “non-routine” task listed was “forming/testing hypotheses.” AlphaFold, a system developed by DeepMind to predict the 3-D structure of proteins, is a good example of progress made in this domain as well. 29.  See ibid.; Autor and Dorn, “The Growth of Low-Skill Service Jobs”; and Autor, “Why Are There Still So Many Jobs?” I first developed this argument in Susskind, “Technology and Employment.”

Boom: Bubbles and the End of Stagnation
by Byrne Hobart and Tobias Huber
Published 29 Oct 2024

One could argue that the Great Stagnation is already over. Indeed, several recent developments have resulted in a resurgence of techno-optimism, from the rapid development of novel mRNA vaccines against Covid-19, one of which was developed within two days of researchers posting the virus’s genome online; to the ability of DeepMind’s AlphaFold to predict protein shapes from their amino-acid sequences, one of biology’s most significant challenges; to the successful test launch of a SpaceX rocket intended to ferry humans to Mars; to the staggering advancements in generative AI models such as GPT. It begs the question: Do we really need more progress if this is what stagnation looks like?