AlphaFold Concepts

The Singularity Is Nearer: When We Merge with AI

by Ray Kurzweil · 25 Jun 2024

pharmaceutical breakthroughs.[14] This is where the pattern recognition capabilities of AI offer a profound advantage. In 2018 Alphabet’s DeepMind created a program called AlphaFold, which competed against the leading protein-folding predictors, including both human scientists and earlier software-driven approaches.[15] DeepMind did not use the usual method

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of drawing on a catalog of protein shapes to be used as models. Like AlphaGo Zero, it dispensed with established human knowledge. AlphaFold placed a prominent first out of ninety-eight competing programs, having accurately predicted twenty-five out of forty-three proteins, whereas the second-place competitor

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experiments, so DeepMind went back to the drawing board and incorporated transformers—the deep-learning technique that powers GPT-3. In 2021 DeepMind publicly released AlphaFold 2, which achieved a truly stunning breakthrough.[17] The AI is now able to achieve nearly experimental-level accuracy for almost any protein it is

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(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

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/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

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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

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://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

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.com/watch?v=gg7WjuFs8F4; “DeepMind Solves Protein Folding | AlphaFold 2,” Lex Fridman, YouTube video, December 2, 2020, https://www.youtube.com/watch?v=W7wJDJ56c88; Ewen Callaway, “ ‘It Will Change Everything’: DeepMind’s AI Makes

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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

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-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

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://doi.org/10.1038/d41586-021-02265-4. BACK TO NOTE REFERENCE 18 Hassabis, “Putting the Power of AlphaFold into the World’s Hands”; Jumper et al., “Highly Accurate Protein Structure Prediction with AlphaFold.” BACK TO NOTE REFERENCE 19 For relatively simple explainers of these methods, see National Cancer Institute, “CAR T

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social media, 114–15 alienation, 230 Alphabet. See also Google DeepMind, 41, 42, 47, 50, 196, 238–39 Waymo, 43, 195–96 AlphaCode, 50 AlphaFold, 238–39 AlphaFold 2, 239 AlphaGo, 41–42, 369n AlphaGo Master, 41 AlphaGo Zero, 41–42, 238, 369n AlphaZero, 42 Alquist 3D, 188 Altair 8800, 131, 165

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking

by Michael Bhaskar · 2 Nov 2021

problems of our time. What did just happen? 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

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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. It was a simple, unexpected but powerful approach, supported by world

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years, humans hadn't thought of. A machine did. Thanks to the program, previously unthinkable moves are now part of the tactical lexicon. AlphaGo, like AlphaFold, jolted the game out of a local maximum. DeepMind is at the forefront of a well-publicised renaissance in AI. (AI itself is a big

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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

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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. Two years later, as I was writing this book, at CASP14 a new program, AlphaFold2, emerged. It performed even better

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our time. Demis Hassabis himself makes the link explicit, calling AI a sort of general-purpose Hubble space telescope for science.20 Big ideas like AlphaFold and AlphaGo, instances of the big idea of deep learning neural networks, are steadily making a difference at the coalface. To see how AI reshapes

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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

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getting closer to its first big real-world application’, Wired, accessed 5 February 2020, available at https://www.wired.co.uk/article/deepmind-protein-folding-alphafold Ricón, José Luis (2015), ‘Is there R&D spending myopia?’, Nintil, accessed 6 January 2021, available at https://nintil.com/is-there-rd-spending-myopia

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an Uncertain World, Chichester: John Wiley Senior, Andrew, Jumper, John, Hassabis, Demis, and Kohli, Pushmeet (2020), ‘AlphaFold: Using AI for scientific discovery’, DeepMind, accessed 5 February 2020, available at https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery Shambaugh, Jay, Nunn, Ryan, Breitwieser, Audrey, and Liu, Patrick (2018), The State

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, 295, 304 algorithms 175, 185, 196, 224, 235, 245 aliens 240–1, 306, 308–9, 337 Allison, Jim 58 Alphabet 193, 225, 265, 294, 295 AlphaFold software 225–6, 227, 228–9, 233 AlphaGo software 226–7, 228, 233 AlQuraishi, Mohammed 225, 226, 229 Amazon 84–5, 214, 272 Amazon Prime

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma

by Mustafa Suleyman · 4 Sep 2023 · 444pp · 117,770 words

, 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

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: “One of the Biggest Problems in Biology Has Finally Been Solved,” wrote Scientific American. A previously hidden universe of proteins was revealed at staggering speed. AlphaFold was so good that CASP was, like ImageNet, retired. For half a century protein folding had been one of science’s grand challenges, and then

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space. Parts are too complex to build using conventional tooling and have to be 3-D printed. In chapter 5, we saw what tools like AlphaFold are doing to catalyze biotech. Until recently biotech relied on endless manual lab work: measuring, pipetting, carefully preparing samples. Now simulations speed up the process

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-insights/the-bio-revolution-innovations-transforming-economies-societies-and-our-lives. 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

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/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

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, 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

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-problems-in-biology-has-finally-been-solved. 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

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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. GO TO NOTE REFERENCE IN TEXT In 2019, electrodes surgically

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Incidents Database, 246 air gaps, 241 airlines, 267–68 AlexNet, 58–59 algorithms, 63, 114, 247 Alibaba, 66 Allison, Graham, 123 Alphabet, 128, 256, 257 AlphaFold, 89–90, 109 AlphaGo, 53–54, 113, 117–19, 120 AlphaZero, 54 alternatives, 234 Altos Labs, 85 Amazon, 94–95, 189 Anduril, 166 anthrax, 174

The Age of AI: And Our Human Future

by Henry A Kissinger, Eric Schmidt and Daniel Huttenlocher · 2 Nov 2021 · 194pp · 57,434 words

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

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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

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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

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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. The result is changes in how scientists approach what they can learn in order

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by machines. Advances in AI for scientific discovery have also continued to accelerate. In the summer of 2021, Deep Mind released AlphaFold2, the successor to AlphaFold, which predicts the 3D structure of proteins from their amino acid sequence (see chapter 6). AlphaFold2 and work done at the Baker Lab at the

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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

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/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.

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

by Parmy Olson · 284pp · 96,087 words

as much as possible. 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

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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

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-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

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be precisely designed and fully observed. That was how it built AlphaGo, by programming it to play millions of games against itself in simulation, and AlphaFold, which used simulations of protein folding. Training on real-world data—as OpenAI had done by scraping billions of words from the internet—was messy

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, and Robert West. “Do Llamas Work in English? On the Latent Language of Multilingual Transformers.” www.arxiv.org, February 16, 2024. Chapter 13: Hello, ChatGPT “AlphaFold: The Making of a Scientific Breakthrough.” Google DeepMind’s YouTube channel, November 30, 2020. Andersen, Ross. “Does Sam Altman Know What He’s Creating?” The

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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

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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

Rule of the Robots: How Artificial Intelligence Will Transform Everything

by Martin Ford · 13 Sep 2021 · 288pp · 86,995 words

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

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drug, DeepMind has instead deployed its technology to gain understanding at a more fundamental level. 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

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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

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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

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.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

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, 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

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, “DeepMind’s AI is getting closer to its first big real-world application,” Wired, January 15, 2020, www.wired.co.uk/article/deepmind-protein-folding-alphafold. 81. Semantic Scholar website, accessed May 25, 2020, pages.semanticscholar.org/about-us. 82. Ibid. 83. Khari Johnson, “Microsoft, White House, and Allen Institute release

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

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

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. 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

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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

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, 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

If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All

by Eliezer Yudkowsky and Nate Soares · 15 Sep 2025 · 215pp · 64,699 words

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

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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

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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

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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

AI 2041: Ten Visions for Our Future

by Kai-Fu Lee and Qiufan Chen · 13 Sep 2021

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

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alerting her of approaching danger. She opened her eyes and reached out for Garcia, ready for a long-overdue plastic-textured embrace. ANALYSIS AI HEALTHCARE, ALPHAFOLD, ROBOTIC APPLICATIONS, COVID AUTOMATION ACCELERATION “Contactless Love” transpires in a society transformed by an ongoing pandemic—in this case, the prospect of the COVID-19

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. 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

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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

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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

These Strange New Minds: How AI Learned to Talk and What It Means

by Christopher Summerfield · 11 Mar 2025 · 412pp · 122,298 words

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

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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

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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

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, 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