Artificial intelligence could be one of humanity's most useful inventions. DeepMind aims to build advanced AI to expand our knowledge and find solutions to thousands of problems.
We’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.
We have a track record of breakthroughs in fundamental AI research, published in journals like Nature, Science, and more. Our programs have learned to diagnose eye diseases as effectively as the world’s top doctors, to save 30% of the energy used to keep data centres cool, and to make state of the art predictions of the complex 3D shapes of proteins - which could one day transform how drugs are invented.
DeepMind was founded in London in 2010, and we joined forces with Google in 2014 to accelerate our work. Since then, our community has expanded to include teams in Alberta, Montreal, Paris, and Mountain View in California.
Are All Good Word Vector Spaces Isomorphic?
Ivan Vulić, Sebastian Ruder, Anders Søgaard
AxCell: Automatic Extraction of Results from Machine Learning Papers
Marcin Kardas, Piotr Czapla, Pontus Stenetorp, Sebastian Ruder, Sebastian Riedel, Ross Taylor, Robert Stojnic
Experience Grounds Language
Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, Sebastian Ruder
Learning Robust and Multilingual Speech Representations
Kazuya Kawakami, Luyu Wang, Chris Dyer, Phil Blunsom, Aaron van den Oord
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation
Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, Pushmeet Kohli
Supervised Seeded Iterated Learning for Interactive Language Learning
Yuchen Lu, Soumye Singhal, Florian Strub, Olivier Pietquin, Aaron Courville
AdapterHub: A Framework for Adapting Transformers
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, Iryna Gurevych