Hassabis, Demis

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2017) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1,102

A clinically applicable approach to continuous prediction of future acute kidney injury

Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., et al. (2019) · Nature

Machine Learning in HealthcareAcute Kidney Injury Research

Cited by 1,086

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

When fear is near: threat imminence elicits prefrontal periaqueductal gray shifts in humans.

Mobbs, D., Petrovic, P., Marchant, J. L., Hassabis, D., Weiskopf, N., Seymour, B., Dolan, R. J., & Frith, C. D. (2007) · Science

Memory and Neural MechanismsNeural and Behavioral Psychology Studies

Cited by 951

What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated

Kumaran, D., Hassabis, D., & McClelland, J. L. (2016) · Trends in Cognitive Sciences

Memory and Neural MechanismsNeural dynamics and brain function

Cited by 759

Magnetic control of tokamak plasmas through deep reinforcement learning

Degrave, J., Felici, F., Buchli, J., Neunert, M., Tracey, B., Carpanese, F., Ewalds, T., Hafner, R., Abdolmaleki, A., de las Casas, D., et al. (2022) · Nature

Magnetic confinement fusion researchFusion and Plasma Physics Studies

Cited by 751

Using Imagination to Understand the Neural Basis of Episodic Memory

Hassabis, D., Kumaran, D., & Maguire, E. A. (2007) · The Journal of Neuroscience

Memory Processes and InfluencesMemory and Neural Mechanisms

Cited by 744

Reinforcement learning, fast and slow

Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019) · Trends in Cognitive Sciences

Reinforcement Learning in RoboticsNeural and Behavioral Psychology Studies

Cited by 667

Human-level performance in 3D multiplayer games with population-based reinforcement learning

Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castañeda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., et al. (2019) · Science

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 662

Prefrontal cortex as a meta-reinforcement learning system

Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., Hassabis, D., & Botvinick, M. (2018) · Nature Neuroscience

Neurotransmitter Receptor Influence on BehaviorNeural and Behavioral Psychology Studies

Cited by 596

The construction system of the brain

Hassabis, D., & Maguire, E. A. (2009) · Philosophical Transactions of the Royal Society B: Biological Sciences

Memory and Neural MechanismsMemory Processes and Influences

Cited by 483

Discovering faster matrix multiplication algorithms with reinforcement learning

Fawzi, A., Balog, M., Huang, A., Hubert, T., Romera-Paredes, B., Barekatain, M., Novikov, A., R. Ruiz, F. J., Schrittwieser, J., Swirszcz, G., et al. (2022) · Nature

Complexity and Algorithms in GraphsArtificial Intelligence Applications

Cited by 478

From threat to fear: the neural organization of defensive fear systems in humans.

Mobbs, D., Marchant, J. L., Hassabis, D., Seymour, B., Tan, G., Gray, M., Petrovic, P., Dolan, R. J., & Frith, C. D. (2009) · The Journal of Neuroscience

Memory and Neural MechanismsNeural and Behavioral Psychology Studies

Cited by 476

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025) · Nature

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 395

Advancing regulatory variant effect prediction with AlphaGenome

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2026) · Nature

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 373

Protein structure prediction using multiple deep neural networks in the 13th critical assessment of protein structure prediction (CASP13)

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2019) · Proteins: Structure, Function, and Bioinformatics

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 347

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys

Climate Change and Environmental ImpactArtificial Intelligence Applications

Cited by 336

Hippocampal place cells construct reward related sequences through unexplored space

Ólafsdóttir, H. F., Barry, C., Saleem, A. B., Hassabis, D., & Spiers, H. J. (2015) · eLife

Memory and Neural MechanismsNeurogenesis and neuroplasticity mechanisms

Cited by 319

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Predicting conversion to wet age-related macular degeneration using deep learning

Yim, J., Chopra, R., Spitz, T., Winkens, J., Obika, A., Kelly, C., Askham, H., Lukic, M., Huemer, J., Fasler, K., et al. (2020) · Nature Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 297

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 284

Protein structure predictions to atomic accuracy with AlphaFold

Jumper, J., & Hassabis, D. (2022) · Nature Methods

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 265

Tracking the emergence of conceptual knowledge during human decision making

Kumaran, D., Summerfield, J. J., Hassabis, D., & Maguire, E. A. (2009) · Neuron

Memory and Neural MechanismsMemory Processes and Influences

Cited by 260

Imagine all the people: how the brain creates and uses personality models to predict behavior

Hassabis, D., Spreng, R. N., Rusu, A. A., Robbins, C. A., Mar, R. A., & Schacter, D. L. (2014) · Cerebral Cortex

Neural and Behavioral Psychology StudiesFace Recognition and Perception

Cited by 247