Demis Hassabis was born in 1976 to a Greek Cypriot father and a Chinese Singaporean mother and grew up in North London. A child prodigy in chess from the age of four, Hassabis reached master standard at the age of 13 with an Elo rating of 2300. He captained many of the England junior chess teams and represented the University of Cambridge in the Oxford-Cambridge varsity chess matches from 1995 to 1997.
Between 1988 and 1990, Hassabis attended Queen Elizabeth's School, a boys' grammar school in North London. He was then home-schooled by his parents, during which time he bought his first computer, a ZX Spectrum 48K, and taught himself how to program. He later attended Christ's College, a state-funded comprehensive school in East Finchley, North London, completing his A-levels and scholarship-level exams two years early.
At the request of Cambridge University, Hassabis took a gap year due to his young age. During this time, he began his career in computer games at Bullfrog Productions, first as a level designer and then as a co-designer and lead programmer on the 1994 game "Theme Park". The game sold several million copies and inspired a whole genre of simulation sandbox games.
Hassabis then left Bullfrog to study at Queens' College, Cambridge, where he completed a degree in Computer Science, graduating in 1997 with a Double First.
After graduating, Hassabis worked at Lionhead Studios as a lead AI programmer on the 2001 "god" game "Black & White". In 1998, he left Lionhead to found Elixir Studios, a London-based independent games developer. In addition to managing the company, Hassabis served as executive designer of the BAFTA-nominated games "Republic: The Revolution" and "Evil Genius".
Following Elixir Studios, Hassabis returned to academia to obtain his PhD in cognitive neuroscience from University College London (UCL) in 2009. He continued his research as a visiting scientist at the Massachusetts Institute of Technology (MIT) and Harvard University before earning a postdoctoral research fellowship at UCL in 2009.
Hassabis is currently the CEO and co-founder of DeepMind, a machine learning AI startup founded in London in 2010. DeepMind aims to combine insights from systems neuroscience with new developments in machine learning and computing hardware to unlock powerful general-purpose learning algorithms. The company has focused on training algorithms to master games, such as Breakout and AlphaGo, an AI program that beat the world's top Go player.
In 2014, Google purchased DeepMind for £400 million, allowing the company to operate independently. Since the acquisition, DeepMind has achieved several significant milestones, including the creation of AlphaGo, a program that defeated world champion Lee Sedol at the complex game of Go.
More recently, DeepMind turned its attention to protein folding, a long-standing challenge in biology. In 2018, DeepMind's tool AlphaFold won the 13th Critical Assessment of Techniques for Protein Structure Prediction (CASP) by successfully predicting the most accurate structure for 25 out of 43 proteins. In 2020, AlphaFold again achieved world-beating results in the CASP14 edition of the competition.
Hassabis has received numerous awards and recognitions for his work, including the Breakthrough Prize, the Canada Gairdner International Award, and the Albert Lasker Award for Basic Medical Research. In 2017, he was appointed a CBE and listed in the Time 100 most influential people. In 2024, he was knighted for his services to AI.
Demis Hassabis, born on July 27, 1976, is a British computer scientist, artificial intelligence researcher, and entrepreneur. He was born to a Greek Cypriot father and a Chinese Singaporean mother and grew up in North London. Hassabis was a child prodigy in chess, achieving master standard at the age of 13 with an Elo rating of 2300. He represented the University of Cambridge in chess matches from 1995 to 1997.
For his early education, Hassabis attended Queen Elizabeth's School, Barnet, and later Christ's College, Finchley, completing his A-levels and scholarship-level exams two years early. At the age of 15, he bought his first computer, a ZX Spectrum 48K, and taught himself how to program. He then took a gap year before starting his undergraduate studies at Cambridge, during which he began his career in computer games at Bullfrog Productions.
Hassabis studied Computer Science at Queens' College, Cambridge, graduating in 1997 with a Double First. After graduating, he worked at Lionhead Studios as a lead AI programmer on the game Black & White.
In 1998, Hassabis left Lionhead to found Elixir Studios, a London-based independent game developer. He served as executive designer of the BAFTA-nominated games Republic: The Revolution and Evil Genius.
Following his time at Elixir Studios, Hassabis returned to academia and obtained his PhD in cognitive neuroscience from University College London (UCL) in 2009. He then continued his research as a visiting scientist at the Massachusetts Institute of Technology (MIT) and Harvard University before earning a postdoctoral research fellowship at UCL in 2009.
Hassabis has co-authored several influential papers published in prestigious scientific journals such as Nature, Science, Neuron, and PNAS. His research focuses on imagination, memory, and amnesia, and he has made significant contributions to our understanding of the link between imagination and episodic memory recall.
In 2010, Hassabis co-founded DeepMind, a machine learning AI startup, along with Shane Legg and Mustafa Suleyman. DeepMind aims to combine insights from neuroscience with advancements in machine learning and computing hardware to create artificial general intelligence (AGI).
DeepMind has achieved several notable accomplishments, including the creation of AlphaGo, a program that defeated the world champion at the complex game of Go, and AlphaFold, a tool that predicts the 3D structure of proteins, solving a 50-year grand challenge in science.
In 2014, Google acquired DeepMind for £400 million, and Hassabis became the CEO and co-founder of Google DeepMind. He has also been appointed as a UK Government AI Advisor and has received numerous awards for his contributions to AI and science, including the Breakthrough Prize, the Canada Gairdner International Award, and a knighthood in 2024 for services to AI.
In addition to his research and entrepreneurial pursuits, Hassabis is an accomplished player of various games, including:
Demis Hassabis, born on 27 July 1976, is a British computer scientist, artificial intelligence researcher, and entrepreneur. He was born to a Greek Cypriot father and a Chinese Singaporean mother and grew up in North London. Hassabis was a child prodigy in chess, achieving master standard at the age of 13 with an Elo rating of 2300. He represented the University of Cambridge in chess matches from 1995 to 1997.
For his early education, Hassabis attended Queen Elizabeth's School, Barnet, and later Christ's College, Finchley, completing his A-levels and scholarship-level exams two years early. He then took a gap year and began working at Bullfrog Productions, designing video games. During this time, he also co-designed and lead-programmed the 1994 game "Theme Park."
Hassabis later attended Queens' College, Cambridge, where he studied computer science and graduated with a Double First in 1997.
After graduating, Hassabis worked at Lionhead Studios as a lead AI programmer on the game "Black & White." In 1998, he left Lionhead to found Elixir Studios, a London-based independent game developer. He served as executive designer for the BAFTA-nominated games "Republic: The Revolution" and "Evil Genius."
Following his time at Elixir Studios, Hassabis returned to academia and obtained his PhD in cognitive neuroscience from University College London (UCL) in 2009. He continued his research as a visiting scientist at MIT and Harvard University before earning a postdoctoral research fellowship at UCL in 2009.
Hassabis is currently the CEO and co-founder of DeepMind, a machine learning AI startup founded in London in 2010. DeepMind aims to combine insights from neuroscience with new developments in machine learning and computing hardware to create artificial general intelligence (AGI). In 2014, Google purchased DeepMind for £400 million, and Hassabis has since been tasked with turning AI research into profits for the company.
DeepMind has achieved several notable accomplishments, including the creation of AlphaGo, a program that defeated the world champion at the complex game of Go, and AlphaFold, a tool that predicts the structure of proteins, solving a 50-year grand challenge in science.
Hassabis has received numerous awards and recognition for his work, including:
Demis Hassabis is a renowned computer scientist, artificial intelligence researcher, and entrepreneur. He has made significant contributions to the field of AI through his work at DeepMind and has received numerous awards and recognition for his achievements. Hassabis continues to play a key role in advancing AI research and its practical applications within Google.
Demis Hassabis DeepMind http://www.deepmind.com/ Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, … Nature 518 (7540), 529-533, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:NaGl4SEjCO4C Cited by: 30576
Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, … nature 596 (7873), 583-589, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:tKAzc9rXhukC Cited by: 24339
Mastering the game of Go with deep neural networks and tree search D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, … Nature 529 (7587), 484-489, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:lSLTfruPkqcC Cited by: 19089
Mastering the game of go without human knowledge D Silver, J Schrittwieser, K Simonyan, I Antonoglou, A Huang, A Guez, … Nature 550 (7676), 354-359, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:bFI3QPDXJZMC Cited by: 10937
Overcoming catastrophic forgetting in neural networks J Kirkpatrick, R Pascanu, N Rabinowitz, J Veness, G Desjardins, AA Rusu, … Proceedings of the national academy of sciences 114 (13), 3521-3526, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:pqnbT2bcN3wC Cited by: 7103
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models M Varadi, S Anyango, M Deshpande, S Nair, C Natassia, G Yordanova, … Nucleic acids research 50 (D1), D439-D444, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:t7zJ5fGR-2UC Cited by: 4774
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, … Science 362 (6419), 1140-1144, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:geHnlv5EZngC Cited by: 4471
Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, … nature 575 (7782), 350-354, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:bnK-pcrLprsC Cited by: 4387
Improved protein structure prediction using potentials from deep learning AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, … Nature 577 (7792), 706-710, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:tkaPQYYpVKoC Cited by: 3140
Clinically applicable deep learning for diagnosis and referral in retinal disease J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, … Nature medicine 24 (9), 1342-1350, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:l7t_Zn2s7bgC Cited by: 2332
Mastering atari, go, chess and shogi by planning with a learned model J Schrittwieser, I Antonoglou, T Hubert, K Simonyan, L Sifre, S Schmitt, … Nature 588 (7839), 604-609, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:HE397vMXCloC Cited by: 2326
International evaluation of an AI system for breast cancer screening SM McKinney, M Sieniek, V Godbole, J Godwin, N Antropova, H Ashrafian, … Nature 577 (7788), 89-94, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:AXPGKjj_ei8C Cited by: 2310
Mastering chess and shogi by self-play with a general reinforcement learning algorithm D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, … arXiv preprint arXiv:1712.01815, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:xtRiw3GOFMkC Cited by: 2181
Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, … Nature 596 (7873), 590-596, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:_Re3VWB3Y0AC Cited by: 2147
Protein complex prediction with AlphaFold-Multimer R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, … biorxiv, 2021.10. 04.463034, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:-_dYPAW6P2MC Cited by: 1897
Hybrid computing using a neural network with dynamic external memory A Graves, G Wayne, M Reynolds, T Harley, I Danihelka, … Nature 538 (7626), 471-476, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:M05iB0D1s5AC Cited by: 1886
Neuroscience-inspired artificial intelligence D Hassabis, D Kumaran, C Summerfield, M Botvinick Neuron 95 (2), 245-258, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:pyW8ca7W8N0C Cited by: 1733
Patients with hippocampal amnesia cannot imagine new experiences D Hassabis, D Kumaran, SD Vann, EA Maguire Proceedings of the National Academy of Sciences 104 (5), 1726-1731, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:8k81kl-MbHgC Cited by: 1718
The future of memory: remembering, imagining, and the brain DL Schacter, DR Addis, D Hassabis, VC Martin, RN Spreng, KK Szpunar Neuron 76 (4), 677-694, 2012 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:L8Ckcad2t8MC Cited by: 1547
Deconstructing episodic memory with construction D Hassabis, EA Maguire Trends in cognitive sciences 11 (7), 299-306, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:MXK_kJrjxJIC Cited by: 1477
Dharshan Kumaran _jkvGEUAAAAJ
David Silver -8DNE4UAAAAJ
koray kavukcuoglu sGFyDIUAAAAJ
Joel Veness _iYrAxEAAAAJ
Alex Graves DaFHynwAAAAJ
Dean Mobbs PhD oaSo1NAAAAAJ
Nikolaus Weiskopf 1Peu3wYAAAAJ
Martin Chadwick odkRSW4AAAAJ
Georg Ostrovski a7OnyQgAAAAJ
Demis Hassabis DeepMind http://www.deepmind.com/ Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, … Nature 518 (7540), 529-533, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:NaGl4SEjCO4C Cited by: 30576
Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, … nature 596 (7873), 583-589, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:tKAzc9rXhukC Cited by: 24339
Mastering the game of Go with deep neural networks and tree search D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, … Nature 529 (7587), 484-489, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:lSLTfruPkqcC Cited by: 19089
Mastering the game of go without human knowledge D Silver, J Schrittwieser, K Simonyan, I Antonoglou, A Huang, A Guez, … Nature 550 (7676), 354-359, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:bFI3QPDXJZMC Cited by: 10937
Overcoming catastrophic forgetting in neural networks J Kirkpatrick, R Pascanu, N Rabinowitz, J Veness, G Desjardins, AA Rusu, … Proceedings of the national academy of sciences 114 (13), 3521-3526, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:pqnbT2bcN3wC Cited by: 7103
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models M Varadi, S Anyango, M Deshpande, S Nair, C Natassia, G Yordanova, … Nucleic acids research 50 (D1), D439-D444, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:t7zJ5fGR-2UC Cited by: 4774
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, … Science 362 (6419), 1140-1144, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:geHnlv5EZngC Cited by: 4471
Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, … nature 575 (7782), 350-354, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:bnK-pcrLprsC Cited by: 4387
Improved protein structure prediction using potentials from deep learning AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, … Nature 577 (7792), 706-710, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:tkaPQYYpVKoC Cited by: 3140
Clinically applicable deep learning for diagnosis and referral in retinal disease J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, … Nature medicine 24 (9), 1342-1350, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:l7t_Zn2s7bgC Cited by: 2332
Mastering atari, go, chess and shogi by planning with a learned model J Schrittwieser, I Antonoglou, T Hubert, K Simonyan, L Sifre, S Schmitt, … Nature 588 (7839), 604-609, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:HE397vMXCloC Cited by: 2326
International evaluation of an AI system for breast cancer screening SM McKinney, M Sieniek, V Godbole, J Godwin, N Antropova, H Ashrafian, … Nature 577 (7788), 89-94, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:AXPGKjj_ei8C Cited by: 2310
Mastering chess and shogi by self-play with a general reinforcement learning algorithm D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, … arXiv preprint arXiv:1712.01815, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:xtRiw3GOFMkC Cited by: 2181
Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, … Nature 596 (7873), 590-596, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:_Re3VWB3Y0AC Cited by: 2147
Protein complex prediction with AlphaFold-Multimer R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, … biorxiv, 2021.10. 04.463034, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:-_dYPAW6P2MC Cited by: 1897
Hybrid computing using a neural network with dynamic external memory A Graves, G Wayne, M Reynolds, T Harley, I Danihelka, … Nature 538 (7626), 471-476, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:M05iB0D1s5AC Cited by: 1886
Neuroscience-inspired artificial intelligence D Hassabis, D Kumaran, C Summerfield, M Botvinick Neuron 95 (2), 245-258, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:pyW8ca7W8N0C Cited by: 1733
Patients with hippocampal amnesia cannot imagine new experiences D Hassabis, D Kumaran, SD Vann, EA Maguire Proceedings of the National Academy of Sciences 104 (5), 1726-1731, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:8k81kl-MbHgC Cited by: 1718
The future of memory: remembering, imagining, and the brain DL Schacter, DR Addis, D Hassabis, VC Martin, RN Spreng, KK Szpunar Neuron 76 (4), 677-694, 2012 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:L8Ckcad2t8MC Cited by: 1547
Deconstructing episodic memory with construction D Hassabis, EA Maguire Trends in cognitive sciences 11 (7), 299-306, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:MXK_kJrjxJIC Cited by: 1477
Dharshan Kumaran googlescholarauthorid:jkvGEUAAAAJ
David Silver googlescholarauthor_id:-8DNE4UAAAAJ
koray kavukcuoglu googlescholarauthor_id:sGFyDIUAAAAJ
Joel Veness googlescholarauthorid:iYrAxEAAAAJ
Alex Graves googlescholarauthor_id:DaFHynwAAAAJ
Dean Mobbs PhD googlescholarauthor_id:oaSo1NAAAAAJ
Nikolaus Weiskopf googlescholarauthor_id:1Peu3wYAAAAJ
Martin Chadwick googlescholarauthor_id:odkRSW4AAAAJ
Georg Ostrovski googlescholarauthor_id:a7OnyQgAAAAJ
Demis Hassabis DeepMind http://www.deepmind.com/ Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, … Nature 518 (7540), 529-533, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:NaGl4SEjCO4C Cited by: 30576
Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, … nature 596 (7873), 583-589, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:tKAzc9rXhukC Cited by: 24339
Mastering the game of Go with deep neural networks and tree search D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, … Nature 529 (7587), 484-489, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:lSLTfruPkqcC Cited by: 19089
Mastering the game of go without human knowledge D Silver, J Schrittwieser, K Simonyan, I Antonoglou, A Huang, A Guez, … Nature 550 (7676), 354-359, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:bFI3QPDXJZMC Cited by: 10937
Overcoming catastrophic forgetting in neural networks J Kirkpatrick, R Pascanu, N Rabinowitz, J Veness, G Desjardins, AA Rusu, … Proceedings of the national academy of sciences 114 (13), 3521-3526, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:pqnbT2bcN3wC Cited by: 7103
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models M Varadi, S Anyango, M Deshpande, S Nair, C Natassia, G Yordanova, … Nucleic acids research 50 (D1), D439-D444, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:t7zJ5fGR-2UC Cited by: 4774
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, … Science 362 (6419), 1140-1144, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:geHnlv5EZngC Cited by: 4471
Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, … nature 575 (7782), 350-354, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:bnK-pcrLprsC Cited by: 4387
Improved protein structure prediction using potentials from deep learning AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, … Nature 577 (7792), 706-710, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:tkaPQYYpVKoC Cited by: 3140
Clinically applicable deep learning for diagnosis and referral in retinal disease J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, … Nature medicine 24 (9), 1342-1350, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:l7t_Zn2s7bgC Cited by: 2332
Mastering atari, go, chess and shogi by planning with a learned model J Schrittwieser, I Antonoglou, T Hubert, K Simonyan, L Sifre, S Schmitt, … Nature 588 (7839), 604-609, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:HE397vMXCloC Cited by: 2326
International evaluation of an AI system for breast cancer screening SM McKinney, M Sieniek, V Godbole, J Godwin, N Antropova, H Ashrafian, … Nature 577 (7788), 89-94, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:AXPGKjj_ei8C Cited by: 2310
Mastering chess and shogi by self-play with a general reinforcement learning algorithm D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, … arXiv preprint arXiv:1712.01815, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:xtRiw3GOFMkC Cited by: 2181
Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, … Nature 596 (7873), 590-596, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:_Re3VWB3Y0AC Cited by: 2147
Protein complex prediction with AlphaFold-Multimer R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, … biorxiv, 2021.10. 04.463034, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:-_dYPAW6P2MC Cited by: 1897
Hybrid computing using a neural network with dynamic external memory A Graves, G Wayne, M Reynolds, T Harley, I Danihelka, … Nature 538 (7626), 471-476, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:M05iB0D1s5AC Cited by: 1886
Neuroscience-inspired artificial intelligence D Hassabis, D Kumaran, C Summerfield, M Botvinick Neuron 95 (2), 245-258, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:pyW8ca7W8N0C Cited by: 1733
Patients with hippocampal amnesia cannot imagine new experiences D Hassabis, D Kumaran, SD Vann, EA Maguire Proceedings of the National Academy of Sciences 104 (5), 1726-1731, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:8k81kl-MbHgC Cited by: 1718
The future of memory: remembering, imagining, and the brain DL Schacter, DR Addis, D Hassabis, VC Martin, RN Spreng, KK Szpunar Neuron 76 (4), 677-694, 2012 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:L8Ckcad2t8MC Cited by: 1547
Deconstructing episodic memory with construction D Hassabis, EA Maguire Trends in cognitive sciences 11 (7), 299-306, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:MXK_kJrjxJIC Cited by: 1477
Dharshan Kumaran googlescholarauthorid:[dharshankumaran.md][_jkvGEUAAAAJ]
David Silver googlescholarauthorid:[davidsilver.md][-8DNE4UAAAAJ]
koray kavukcuoglu googlescholarauthorid:[koraykavukcuoglu.md][sGFyDIUAAAAJ]
Joel Veness googlescholarauthorid:[joelveness.md][_iYrAxEAAAAJ]
Alex Graves googlescholarauthorid:[alexgraves.md][DaFHynwAAAAJ]
Dean Mobbs PhD googlescholarauthorid:[deanmobbs_phd.md][oaSo1NAAAAAJ]
Nikolaus Weiskopf googlescholarauthorid:[nikolausweiskopf.md][1Peu3wYAAAAJ]
Martin Chadwick googlescholarauthorid:[martinchadwick.md][odkRSW4AAAAJ]
Georg Ostrovski googlescholarauthorid:[georgostrovski.md][a7OnyQgAAAAJ]
Demis Hassabis DeepMind http://www.deepmind.com/ Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, … Nature 518 (7540), 529-533, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:NaGl4SEjCO4C Cited by: 30576
Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, … nature 596 (7873), 583-589, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:tKAzc9rXhukC Cited by: 24339
Mastering the game of Go with deep neural networks and tree search D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, … Nature 529 (7587), 484-489, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:lSLTfruPkqcC Cited by: 19089
Mastering the game of go without human knowledge D Silver, J Schrittwieser, K Simonyan, I Antonoglou, A Huang, A Guez, … Nature 550 (7676), 354-359, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:bFI3QPDXJZMC Cited by: 10937
Overcoming catastrophic forgetting in neural networks J Kirkpatrick, R Pascanu, N Rabinowitz, J Veness, G Desjardins, AA Rusu, … Proceedings of the national academy of sciences 114 (13), 3521-3526, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:pqnbT2bcN3wC Cited by: 7103
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models M Varadi, S Anyango, M Deshpande, S Nair, C Natassia, G Yordanova, … Nucleic acids research 50 (D1), D439-D444, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:t7zJ5fGR-2UC Cited by: 4774
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, … Science 362 (6419), 1140-1144, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:geHnlv5EZngC Cited by: 4471
Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, … nature 575 (7782), 350-354, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:bnK-pcrLprsC Cited by: 4387
Improved protein structure prediction using potentials from deep learning AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, … Nature 577 (7792), 706-710, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:tkaPQYYpVKoC Cited by: 3140
Clinically applicable deep learning for diagnosis and referral in retinal disease J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, … Nature medicine 24 (9), 1342-1350, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:l7t_Zn2s7bgC Cited by: 2332
Mastering atari, go, chess and shogi by planning with a learned model J Schrittwieser, I Antonoglou, T Hubert, K Simonyan, L Sifre, S Schmitt, … Nature 588 (7839), 604-609, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:HE397vMXCloC Cited by: 2326
International evaluation of an AI system for breast cancer screening SM McKinney, M Sieniek, V Godbole, J Godwin, N Antropova, H Ashrafian, … Nature 577 (7788), 89-94, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:AXPGKjj_ei8C Cited by: 2310
Mastering chess and shogi by self-play with a general reinforcement learning algorithm D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, … arXiv preprint arXiv:1712.01815, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:xtRiw3GOFMkC Cited by: 2181
Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, … Nature 596 (7873), 590-596, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:_Re3VWB3Y0AC Cited by: 2147
Protein complex prediction with AlphaFold-Multimer R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, … biorxiv, 2021.10. 04.463034, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:-_dYPAW6P2MC Cited by: 1897
Hybrid computing using a neural network with dynamic external memory A Graves, G Wayne, M Reynolds, T Harley, I Danihelka, … Nature 538 (7626), 471-476, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:M05iB0D1s5AC Cited by: 1886
Neuroscience-inspired artificial intelligence D Hassabis, D Kumaran, C Summerfield, M Botvinick Neuron 95 (2), 245-258, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:pyW8ca7W8N0C Cited by: 1733
Patients with hippocampal amnesia cannot imagine new experiences D Hassabis, D Kumaran, SD Vann, EA Maguire Proceedings of the National Academy of Sciences 104 (5), 1726-1731, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:8k81kl-MbHgC Cited by: 1718
The future of memory: remembering, imagining, and the brain DL Schacter, DR Addis, D Hassabis, VC Martin, RN Spreng, KK Szpunar Neuron 76 (4), 677-694, 2012 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:L8Ckcad2t8MC Cited by: 1547
Deconstructing episodic memory with construction D Hassabis, EA Maguire Trends in cognitive sciences 11 (7), 299-306, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:MXK_kJrjxJIC Cited by: 1477
Dharshan Kumaran googlescholarauthorid dharshankumaran.md:_jkvGEUAAAAJ
David Silver googlescholarauthorid davidsilver.md:-8DNE4UAAAAJ
koray kavukcuoglu googlescholarauthorid koraykavukcuoglu.md:sGFyDIUAAAAJ
Joel Veness googlescholarauthorid joelveness.md:_iYrAxEAAAAJ
Alex Graves googlescholarauthorid alexgraves.md:DaFHynwAAAAJ
Dean Mobbs PhD googlescholarauthorid deanmobbs_phd.md:oaSo1NAAAAAJ
Nikolaus Weiskopf googlescholarauthorid nikolausweiskopf.md:1Peu3wYAAAAJ
Martin Chadwick googlescholarauthorid martinchadwick.md:odkRSW4AAAAJ
Georg Ostrovski googlescholarauthorid georgostrovski.md:a7OnyQgAAAAJ
Demis Hassabis
DeepMind
http://www.deepmind.com/
Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, … Nature 518 (7540), 529-533, 2015 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:NaGl4SEjCO4C
Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, … nature 596 (7873), 583-589, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:tKAzc9rXhukC
Mastering the game of Go with deep neural networks and tree search D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, … Nature 529 (7587), 484-489, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:lSLTfruPkqcC
Mastering the game of go without human knowledge D Silver, J Schrittwieser, K Simonyan, I Antonoglou, A Huang, A Guez, … Nature 550 (7676), 354-359, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:bFI3QPDXJZMC
Overcoming catastrophic forgetting in neural networks J Kirkpatrick, R Pascanu, N Rabinowitz, J Veness, G Desjardins, AA Rusu, … Proceedings of the national academy of sciences 114 (13), 3521-3526, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:pqnbT2bcN3wC
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models M Varadi, S Anyango, M Deshpande, S Nair, C Natassia, G Yordanova, … Nucleic acids research 50 (D1), D439-D444, 2022 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:t7zJ5fGR-2UC
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, … Science 362 (6419), 1140-1144, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:geHnlv5EZngC
Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, … nature 575 (7782), 350-354, 2019 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:bnK-pcrLprsC
Improved protein structure prediction using potentials from deep learning AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, … Nature 577 (7792), 706-710, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:tkaPQYYpVKoC
Clinically applicable deep learning for diagnosis and referral in retinal disease J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, … Nature medicine 24 (9), 1342-1350, 2018 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:l7t_Zn2s7bgC
Mastering atari, go, chess and shogi by planning with a learned model J Schrittwieser, I Antonoglou, T Hubert, K Simonyan, L Sifre, S Schmitt, … Nature 588 (7839), 604-609, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:HE397vMXCloC
International evaluation of an AI system for breast cancer screening SM McKinney, M Sieniek, V Godbole, J Godwin, N Antropova, H Ashrafian, … Nature 577 (7788), 89-94, 2020 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:AXPGKjj_ei8C
Mastering chess and shogi by self-play with a general reinforcement learning algorithm D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, … arXiv preprint arXiv:1712.01815, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:xtRiw3GOFMkC
Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, … Nature 596 (7873), 590-596, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:_Re3VWB3Y0AC
Protein complex prediction with AlphaFold-Multimer R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, … biorxiv, 2021.10. 04.463034, 2021 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:-_dYPAW6P2MC
Hybrid computing using a neural network with dynamic external memory A Graves, G Wayne, M Reynolds, T Harley, I Danihelka, … Nature 538 (7626), 471-476, 2016 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:M05iB0D1s5AC
Neuroscience-inspired artificial intelligence D Hassabis, D Kumaran, C Summerfield, M Botvinick Neuron 95 (2), 245-258, 2017 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:pyW8ca7W8N0C
Patients with hippocampal amnesia cannot imagine new experiences D Hassabis, D Kumaran, SD Vann, EA Maguire Proceedings of the National Academy of Sciences 104 (5), 1726-1731, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:8k81kl-MbHgC
The future of memory: remembering, imagining, and the brain DL Schacter, DR Addis, D Hassabis, VC Martin, RN Spreng, KK Szpunar Neuron 76 (4), 677-694, 2012 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:L8Ckcad2t8MC
Deconstructing episodic memory with construction D Hassabis, EA Maguire Trends in cognitive sciences 11 (7), 299-306, 2007 Link: https://scholar.google.com/citations?viewop=viewcitation&hl=en&user=dYpPMQEAAAAJ&citationforview=dYpPMQEAAAAJ:MXK_kJrjxJIC
Dharshan Kumaran googlescholarauthorid dharshankumaran.md:_jkvGEUAAAAJ
David Silver googlescholarauthorid davidsilver.md:-8DNE4UAAAAJ
koray kavukcuoglu googlescholarauthorid koraykavukcuoglu.md:sGFyDIUAAAAJ
Joel Veness googlescholarauthorid joelveness.md:_iYrAxEAAAAJ
Alex Graves googlescholarauthorid alexgraves.md:DaFHynwAAAAJ
Dean Mobbs PhD googlescholarauthorid deanmobbs_phd.md:oaSo1NAAAAAJ
Nikolaus Weiskopf googlescholarauthorid nikolausweiskopf.md:1Peu3wYAAAAJ
Martin Chadwick googlescholarauthorid martinchadwick.md:odkRSW4AAAAJ
Georg Ostrovski googlescholarauthorid georgostrovski.md:a7OnyQgAAAAJ
Summary: Sir Demis Hassabis (born 27 July 1976) is a British computer scientist, artificial intelligence researcher and entrepreneur. In his early career he was a video game AI programmer and designer, and an expert board games player. He is the chief executive officer and co-founder of DeepMind and Isomorphic Labs, and a UK Government AI Advisor. He is a Fellow of the Royal Society, and has won many prestigious awards for his work on AlphaFold including the Breakthrough Prize, the Canada Gairdner International Award, and the Lasker Award. In 2017 he was appointed a CBE and listed in the Time 100 most influential people list. In 2024 he was knighted for services to AI.
URL: https://en.wikipedia.org/wiki/Demis_Hassabis
Page ID: 3259263
Content: Sir Demis Hassabis (born 27 July 1976) is a British computer scientist, artificial intelligence researcher and entrepreneur. In his early career he was a video game AI programmer and designer, and an expert board games player. He is the chief executive officer and co-founder of DeepMind and Isomorphic Labs, and a UK Government AI Advisor. He is a Fellow of the Royal Society, and has won many prestigious awards for his work on AlphaFold including the Breakthrough Prize, the Canada Gairdner International Award, and the Lasker Award. In 2017 he was appointed a CBE and listed in the Time 100 most influential people list. In 2024 he was knighted for services to AI.
Early life and education Hassabis was born to a Greek Cypriot father and a Chinese Singaporean mother and grew up in North London. A child prodigy in chess from the age of 4, Hassabis reached master standard at the age of 13 with an Elo rating of 2300 and captained many of the England junior chess teams. He represented the University of Cambridge in the Oxford–Cambridge varsity chess matches of 1995, 1996 and 1997, winning a half blue. Between 1988 and 1990, Hassabis was educated at Queen Elizabeth's School, Barnet, a boys' grammar school in North London. He was subsequently home-schooled by his parents, during which time he bought his first computer, a ZX Spectrum 48K funded from chess winnings, and taught himself how to program from books. He went on to be educated at Christ's College, Finchley, a state-funded comprehensive school in East Finchley, North London. He completed his A-levels and scholarship level exams two years early at the ages of 15 and 16 respectively.
Bullfrog Asked by Cambridge University to take a gap year due to his young age, Hassabis began his computer games career at Bullfrog Productions, first level designing on Syndicate, and then at 17 co-designing and lead programming on the 1994 game Theme Park, with the game's designer Peter Molyneux. Theme Park, a simulation video game, sold several million copies and inspired a whole genre of simulation sandbox games. He earned enough from his gap year to pay his own way through university.
University of Cambridge Hassabis then left Bullfrog to study at Queens' College, Cambridge, where he completed the Computer Science Tripos and graduated in 1997 with a Double First.
Career and research Lionhead After graduating from Cambridge, Hassabis worked at Lionhead Studios. Games designer Peter Molyneux, with whom Hassabis had worked at Bullfrog Productions, had recently founded the company. At Lionhead, Hassabis worked as lead AI programmer on the 2001 "god" game Black & White.
Elixir Studios Hassabis left Lionhead in 1998 to found Elixir Studios, a London-based independent games developer, signing publishing deals with Eidos Interactive, Vivendi Universal and Microsoft. In addition to managing the company, Hassabis served as executive designer of the BAFTA-nominated games Republic: The Revolution and Evil Genius. The release of Elixir's first game, Republic: The Revolution, a highly ambitious and unusual political simulation game, was delayed due to its huge scope, which involved an AI simulation of the workings of an entire fictional country. The final game was reduced from its original vision and greeted with lukewarm reviews, receiving a Metacritic score of 62/100. Evil Genius, a tongue-in-cheek Bond villain simulator, fared much better with a score of 75/100. In April 2005 the intellectual property and technology rights were sold to various publishers and the studio was closed.
Neuroscience research at University College London Following Elixir Studios, Hassabis returned to academia to obtain his PhD in cognitive neuroscience from University College London (UCL) in 2009 supervised by Eleanor Maguire. He sought to find inspiration in the human brain for new AI algorithms. He continued his neuroscience and artificial intelligence research as a visiting scientist jointly at Massachusetts Institute of Technology (MIT), in the lab of Tomaso Poggio, and Harvard University, before earning a Henry Wellcome postdoctoral research fellowship to the Gatsby Computational Neuroscience Unit at UCL in 2009 working with Peter Dayan. Working in the field of imagination, memory, and amnesia, he co-authored several influential papers published in Nature, Science, Neuron, and PNAS. His very first academic work, published in PNAS, was a landmark paper that showed systematically for the first time that patients with damage to their hippocampus, known to cause amnesia, were also unable to imagine themselves in new experiences. The finding established a link between the constructive process of imagination and the reconstructive process of episodic memory recall. Based on this work and a follow-up functional magnetic resonance imaging (fMRI) study, Hassabis developed a new theoretical account of the episodic memory system identifying scene construction, the generation and online maintenance of a complex and coherent scene, as a key process underlying both memory recall and imagination. This work received widespread coverage in the mainstream media and was listed in the top 10 scientific breakthroughs of the year by the journal Science. He later generalised these ideas to advance the notion of a 'simulation engine of the mind' whose role it was to imagine events and scenarios to aid with better planning.
DeepMind Hassabis is the CEO and co-founder of DeepMind, a machine learning AI startup, founded in London in 2010 with Shane Legg and Mustafa Suleyman. Hassabis met Legg when both were postdocs at the Gatsby Computational Neuroscience Unit, and he and Suleyman had been friends through family. Hassabis also recruited his university friend and Elixir partner David Silver. DeepMind's mission is to "solve intelligence" and then use intelligence "to solve everything else". More concretely, DeepMind aims to combine insights from systems neuroscience with new developments in machine learning and computing hardware to unlock increasingly powerful general-purpose learning algorithms that will work towards the creation of an artificial general intelligence (AGI). The company has focused on training learning algorithms to master games, and in December 2013 it announced that it had made a pioneering breakthrough by training an algorithm called a Deep Q-Network (DQN) to play Atari games at a superhuman level by only using the raw pixels on the screen as inputs. DeepMind's early investors included several high-profile tech entrepreneurs. In 2014, Google purchased DeepMind for £400 million. Although most of the company has remained an independent entity based in London, DeepMind Health has since been directly incorporated into Google Health. Since the Google acquisition, the company has notched up a number of significant achievements, perhaps the most notable being the creation of AlphaGo, a program that defeated world champion Lee Sedol at the complex game of Go. Go had been considered a holy grail of AI, for its high number of possible board positions and resistance to existing programming techniques. However, AlphaGo beat European champion Fan Hui 5–0 in October 2015 before winning 4–1 against former world champion Lee Sedol in March 2016. Additional DeepMind accomplishments include creating a Neural Turing Machine, reducing the energy used by the cooling systems in Google's data centers by 40%, advancing research on AI safety, and the creation of a partnership with the National Health Service (NHS) of the United Kingdom and Moorfields Eye Hospital to improve medical service and identify the onset of degenerative eye conditions. More recently, DeepMind turned its artificial intelligence to protein folding, a 50-year grand challenge in science, to predict the 3D structure of a protein from its 1D amino acid sequence. This is an important problem in biology, as proteins are essential to life, almost every biological function depends on them, and the function of a protein is thought to be related to its structure. In December 2018, DeepMind's tool AlphaFold won the 13th Critical Assessment of Techniques for Protein Structure Prediction (CASP) by successfully predicting the most accurate structure for 25 out of 43 proteins. "This is a lighthouse project, our first major investment in terms of people and resources into a fundamental, very important, real-world scientific problem", Hassabis said to The Guardian. In November 2020, DeepMind again announced world-beating results in the CASP14 edition of the competition, with a median global distance test (GDT) score of 87.0 across protein targets in the challenging free-modeling category, much higher than the same 2018 results with a median GDT < 60, and an overall error of less than the width of an atom, making it competitive with experimental methods. DeepMind has also been responsible for technical advances in machine learning, having produced a number of award-winning papers. In particular, the company has made significant advances in deep learning and reinforcement learning, and pioneered the field of deep reinforcement learning which combines these two methods. Hassabis has predicted that Artificial Intelligence will be "one of the most beneficial technologies of mankind ever" but that significant ethical issues remain. In 2023, Hassabis signed the statement that "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war". He considers however that a pause on AI progress would be very hard to enforce worldwide, and that the potential benefits (e.g. for health and against climate change) make it worth continuing. He said that there is an urgent need for research on evaluation tests that measure how capable and controllable new AI models are.
Personal life Hassabis resides in North London with his family. He is also a lifelong fan of Liverpool FC.
Awards and honours Entrepreneurial and scientific 2023 - Albert Lasker Award for Basic Medical Research 2023 - Canada Gairdner International Award 2023 - Breakthrough Prize in Life Sciences for developing AlphaFold, which accurately predicts the structure of protein 2022 - Global Swiss AI Award 2022 - BBVA Foundation Frontiers of Knowledge Award in the category "Biology and Biomedicine". 2022 - Princess of Asturias Award (with Yoshua Bengio, Geoffrey Hinton, and Yann LeCun) for Technical and Scientific Research 2022 - Wiley Prize in Biomedical Sciences 2021 - IRI Medal, established by the Industrial Research Institute (IRI) 2021 - International Honorary Member of the American Academy of Arts and Sciences 2020 - Pius XI Medal from the Pontifical Academy of Sciences 2020 - The 50 most influential people in Britain from British GQ magazine 2020 - Dan David Prize - Future Award 2019 - Winner of UKtech50 (the 50 most influential people in UK technology) from Computer Weekly 2018 - Elected a Fellow of the Royal Society (FRS) in May 2018 2018 - Adviser to the UK's Government Office for Artificial Intelligence 2018 - Honorary Doctorate, Imperial College London 2017 - Appointed Commander of the Order of the British Empire (CBE) in the 2018 New Year Honours for "services to Science and Technology". 2017 - Time 100: The 100 Most Influential People 2017 - The Asian Awards: Outstanding Achievement in Science and Technology 2017 - Elected a Fellow of the Royal Academy of Engineering (FREng) 2017 - American Academy of Achievement: Golden Plate Award 2016 - Honorary Fellow, University College London 2016 - London Evening Standard list of influential Londoners, number 6 2016 - Royal Academy of Engineering Silver Medal 2016 - WIRED Leadership in Innovation 2016 - Nature's 10: the 10 most influential (good or bad) scientists of the year 2016 - Financial Times Digital Entrepreneur of the Year 2015 - Financial Times top 50 Entrepreneurs in Europe 2015 - Fellow Benefactor, Queens' College, Cambridge 2014 - Third most influential Londoner according to the London Evening Standard 2014 - Mullard Award of the Royal Society 2013 - Listed on WIRED's 'Smart 50' 2009 - Fellow of the Royal Society of Arts (FRSA)
Research Hassabis's research work has been listed in the Top 10 Scientific Breakthroughs of the Year by Science Magazine on four separate occasions:
2021 Breakthrough of the Year (Winner) - for AlphaFold v2 2020 Breakthrough of the Year (Top 10) - for AlphaFold v1 2016 Breakthrough of the Year (Top 10) - for AlphaGo 2007 Breakthrough of the Year (Top 10) - for neuroscience research on imagination
DeepMind Cambridge Computer Laboratory Company of the Year (2014) Six Nature front cover articles (2015, 2016, 2019, 2020, and two in 2021) and one Science front cover article (2017) Honorary 9-dan Go rank for AlphaGo from Korean Baduk Association (2016) and Chinese Weiqi Association (2017) Cannes Lion Grand Prix for AlphaGo (2016) WIRED Innovation in AI Award (2016) City AM Innovative Company of the Year (2016)
Games Hassabis is a five-times winner of the all-round world board games championship (the Pentamind), and an expert player of many games including:
Chess: achieved Master standard at age 13 with ELO rating of 2300 (at the time the second-highest in the world for his age after Judit Polgár). Diplomacy: World Team Champion in 2004, 4th in 2006 World Championship, 3rd in 2004 European Championship. Poker: cashed at the World Series of Poker six times including in the Main Event. Multi-games events at the London Mind Sports Olympiad: World Pentamind Champion (a record five times: 1998, 1999, 2000, 2001, 2003) and World Decamentathlon Champion (twice: 2003, 2004).
References External links
Demis Hassabis rating card at FIDE