Differentiable Neural Computers paper published in Nature
Committed to AI that benefits everyone.
AlphaGo defeats Lee Sedol in the game of Go
Neural scene representation and rendering
Taking DeepMind to the next level: welcoming Lila Ibrahim as our first Chief Operating Officer
Differentiable Neural Computers paper published in Nature
Solve intelligence. Use it to make the world a better place.
DeepMind Health Response to Independent Reviewers Report 2018
Neural scene representation and rendering
Navigating with grid-like representations in artificial agents
Enabling Continual Learning in Neural Networks
Our world class team consists of many renowned experts in their fields.
AlphaGo defeats Lee Sedol in the game of Go
Open research of the highest quality. Over 100 publications, including five Nature papers.
DeepMind opens new AI research office in Edmonton
Neural scene representation and rendering
DeepMind Ethics & Society research unit launches
Raia is a Senior Research Scientist working on Deep Learning at DeepMind, with a particular focus on solving robotics and navigation using deep neural networks. She joined DeepMind following positions at Carnegie Mellon and SRI International as she saw the combination of research into games, neuroscience, deep learning and reinforcement learning as a unique proposition that could lead to fundamental breakthroughs in AI. She says that one of her favourite moments at DeepMind was watching the livestream of Lee Sedol playing AlphaGo at 4am surrounded by the rest of the team, despite the difference in timezone!
We introduce the Generative Query Network (GQN), a framework within which machines learn to perceive their surroundings by training only on data obtained by themselves as they move around scenes. Much like infants and animals, the GQN learns by trying to make sense of its observations of the world around it. In doing so, the GQN learns about plausible scenes and their geometrical properties, without any human labelling of the contents of scenes.
© 2018 DeepMind Technologies Limited
Applying our research for real-world impact in health, science, energy and more.
Progress for DeepMind Health and Streams
Introducing WaveNets: a generative model for raw audio
Shakir grew up in Johannesburg, South Africa, and initially pursued a degree in electrical and information engineering before becoming intrigued by the principles of learning systems and moving onto graduate study at Cambridge University and the Canadian Institute for Advanced Research (CIFAR) exploring Neural Computation and Adaptive Perception. He then joined DeepMind as a Research Scientist, exploring the fundamentals of imagination, reasoning, and future thinking without the need for external signals. He loves working at DeepMind because of its unique environment that embraces and encourages different approaches to machine learning, and relishes the opportunity to regularly think about the ways in which machine learning and AI can be used to truly overcome the challenges facing humanity.
Irina joined DeepMinds neuroscience team in 2015 following a PhD at Oxford University. Her work takes inspiration from the way that babies learn from the world around them, using unsupervised interactions with the environment, copying others, and testing new hypotheses. By developing AI methods that can do this she hopes to create far more capable, resilient algorithms that can adapt to new challenges and perform a larger number of tasks. She joined DeepMind as it gave her a huge amount of freedom to work on the ideas she is passionate about in an inspirational, incredible environment a heaven for AI geeks.
DeepMind reduces energy for Google data centres
S. M. Ali Eslami, Danilo Jimenez Rezende
Navigating with grid-like representations in artificial agents
Enabling Continual Learning in Neural Networks
DeepMind opens new AI research office in Edmonton
Avishkar spent six years as a software engineer in his native country of South Africa before moving into the field of machine learning and completing a masters at University College London. As a Research Engineer at DeepMind he focuses on the technical implementation of machine learning projects, helping Research Scientists turn their ideas into reality. He says the role provides the ideal balance between technical coding and research. He has worked in a variety of research areas and loves the atmosphere at DeepMind as well as being able to interact with so many leaders in the field.
Announcing the Partnership on AI to Benefit People & Society
Deep science meets start-up energy and focus.
Taking DeepMind to the next level: welcoming Lila Ibrahim as our first Chief Operating Officer
Announcing the Partnership on AI to Benefit People & Society
Progress for DeepMind Health and Streams
DeepMind reduces energy for Google data centres
Dominik is a Software Engineer in the Research Engineering team. He joined DeepMind from Google in August 2014, where he worked on Chrome for Android. Dominiks role focuses on the performance of our algorithms. This involves writing tools and libraries for our researchers to scale up their experiments and help them to run their algorithms as fast as possible. Dominik joined DeepMind because he wanted to be a part of an incredibly exciting mission which could have an immediate and clear impact. One of his most memorable DeepMind experiences was seeing WaveNet go from research project to reality.
Mihaela is a Research Engineer at DeepMind. She joined us in 2016 after becoming fascinated with machine learning during her degree at Imperial College London. Before joining DeepMind to research generative models, she worked on text understanding and tackling natural language problems at Google. Mihaela says that for anyone with broad research interests, DeepMind is an oasis of inspiration and knowledge, with plenty of world experts to learn from and flexibility to dive into a variety of topics.
DeepMind Ethics & Society research unit launches
Introducing WaveNets: a generative model for raw audio