Hassabis, Demis

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 235

Decoding Neuronal Ensembles in the Human Hippocampus

Hassabis, D., Chu, C., Rees, G., Weiskopf, N., Molyneux, P. D., & Maguire, E. A. (2009) · Current Biology

Memory and Neural MechanismsSpatial Cognition and Navigation

Cited by 231

Big-loop recurrence within the hippocampal system supports integration of information across episodes

Koster, R., Chadwick, M. J., Chen, Y., Berron, D., Banino, A., Düzel, E., Hassabis, D., & Kumaran, D. (2018) · Neuron

Memory and Neural MechanismsMemory Processes and Influences

Cited by 191

Faster sorting algorithms discovered using deep reinforcement learning

Mankowitz, D. J., Michi, A., Zhernov, A., Gelmi, M., Selvi, M., Paduraru, C., Leurent, E., Iqbal, S., Lespiau, J.-B., Ahern, A., et al. (2023) · Nature

Evolutionary Algorithms and ApplicationsArtificial Intelligence in Games

Cited by 177

Acquisition of chess knowledge in AlphaZero

McGrath, T., Kapishnikov, A., Tomašev, N., Pearce, A., Wattenberg, M., Hassabis, D., Kim, B., Paquet, U., & Kramnik, V. (2022) · Proceedings of the National Academy of Sciences

Artificial Intelligence in GamesArtificial Intelligence Applications

Cited by 166

Neural Mechanisms of Hierarchical Planning in a Virtual Subway Network

Balaguer, J., Spiers, H., Hassabis, D., & Summerfield, C. (2016) · Neuron

Neural and Behavioral Psychology StudiesCognitive Functions and Memory

Cited by 163

Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons

Higgins, I., Chang, L., Langston, V., Hassabis, D., Summerfield, C., Tsao, D., & Botvinick, M. (2021) · Nature Communications

Face Recognition and PerceptionFace recognition and analysis

Cited by 162

Model-Free Episodic Control

Blundell, C., Uria, B., Pritzel, A., Li, Y., Ruderman, A., Leibo, J. Z., Rae, J., Wierstra, D., & Hassabis, D. (2016) · arXiv

Reinforcement Learning in RoboticsMemory and Neural Mechanisms

Cited by 162

Accelerating scientific discovery with Co-Scientist

Gottweis, J., Weng, W.-H., Daryin, A., Tu, T., Sirkovic, P., Myaskovsky, A., Glowaty, G., Weissenberger, F., Orlandi, A., Popovici, D., et al. (2025) · arXiv

Computational Drug Discovery MethodsArtificial Intelligence Applications

Cited by 158

Gemma 2: Improving Open Language Models at a Practical Size

Gemma Team, Riviere, M., Pathak, S., Sessa, P. G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ramé, A., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 149

Improving alignment of dialogue agents via targeted human judgements

Glaese, A., McAleese, N., Trębacz, M., Aslanides, J., Firoiu, V., Ewalds, T., Rauh, M., Weidinger, L., Chadwick, M., Thacker, P., et al. (2022) · arXiv

Speech and dialogue systemsIntelligent Tutoring Systems and Adaptive Learning

Cited by 134

Scalable watermarking for identifying large language model outputs

Dathathri, S., See, A., Ghaisas, S., Huang, P.-S., McAdam, R., Welbl, J., Bachani, V., Kaskasoli, A., Stanforth, R., Matejovicova, T., et al. (2024) · Nature

Advanced Steganography and Watermarking TechniquesAdversarial Robustness in Machine Learning

Cited by 132

Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 117

Gemma 4 Technical Report

Gemma Team, Abd, S. E., Aggarwal, V., Algayres, R., Andreev, A., Bachem, O., Ballantyne, I., Brick, C., Cărbune, V., Casbon, M., et al. (2026) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 112

Learning high-accuracy error decoding for quantum processors

Bausch, J., Senior, A. W., Heras, F. J. H., Edlich, T., Davies, A., Newman, M., Jones, C., Satzinger, K., Niu, M. Y., Blackwell, S., et al. (2024) · Nature

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 106

Choking on the money: reward-based performance decrements are associated with midbrain activity

Mobbs, D., Hassabis, D., Seymour, B., Marchant, J. L., Weiskopf, N., Dolan, R. J., & Frith, C. D. (2009) · Psychological Science

Neural and Behavioral Psychology StudiesNeurotransmitter Receptor Influence on Behavior

Cited by 103

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

DeepMind Lab

Beattie, C., Leibo, J. Z., Teplyashin, D., Ward, T., Wainwright, M., Küttler, H., Lefrancq, A., Green, S., Valdés, V., Sadik, A., et al. (2016) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 68

Retrieval-based model accounts for striking profile of episodic memory and generalization

Banino, A., Koster, R., Hassabis, D., & Kumaran, D. (2016) · Scientific Reports

Memory and Neural MechanismsMemory Processes and Influences

Cited by 57

A model of egocentric to allocentric understanding in mammalian brains

Uria, B., Ibarz, B., Banino, A., Zambaldi, V., Kumaran, D., Hassabis, D., Barry, C., & Blundell, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory)

Memory and Neural MechanismsSpatial Cognition and Navigation

Cited by 48

3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

Varadi, M., Nair, S., Sillitoe, I., Tauriello, G., Anyango, S., Bienert, S., Borges, C., Deshpande, M., Green, T., Hassabis, D., et al. (2022) · GigaScience

Protein Structure and DynamicsEnzyme Structure and Function

Cited by 32

Olympiad-level formal mathematical reasoning with reinforcement learning

Hubert, T., Mehta, R., Sartran, L., Horváth, M. Z., Žužić, G., Wieser, E., Huang, A., Schrittwieser, J., Schroecker, Y., Masoom, H., et al. (2026) · Nature

Artificial Intelligence ApplicationsMathematics, Computing, and Information Processing

Cited by 19

Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents

Wang, J. X., King, M., Porcel, N., Kurth-Nelson, Z., Zhu, T., Deck, C., Choy, P., Cassin, M., Reynolds, M., Song, F., et al. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 10

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Botev, A., De, S., Smith, S. L., Fernando, A., Muraru, G.-C., Haroun, R., Berrada, L., Pascanu, R., Sessa, P. G., Dadashi, R., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 5