AlphaFold: The making of a scientific breakthrough

The inside story of the DeepMind team of scientists and engineers who created AlphaFold, an AI system that is recognised as a solution to "protein folding", a grand scientific challenge for more than 50 years. Find out more: deepmind.com/alphafold Protein references: TBP = To be published 1BYI: Sandalova, T., et al. (1999) Structure of...

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Protein folding explained

Join DeepMind Science Engineer Kathryn Tunyasuvunakool to explore the hidden world of proteins. These tiny molecular machines underpin every biological process in every living thing and each one has a unique 3D shape that determines how it works and what it does. But figuring out the exact structure of a protein is an expensive and often time-consuming...

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DeepMind Scholars: Benedetta's story

The DeepMind Scholars programme gives talented students from underrepresented backgrounds the support they need to study at leading universities, and connect with our researchers and engineers. Scholars get their Masters' fees paid in full, as well as guidance and support from personal DeepMind mentors. Find out more on our website: https://deepmind.com/scholarships

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DeepMind x UCL | Deep Learning Lectures | 12/12 | Responsible Innovation

What can we do to build algorithms that are safe, reliable and robust? And what are the responsibilities of technologists who work in this area? In this talk, Chongli Qin and Iason Gabriel explore these questions — connected through the lens of responsible innovation — in two parts. In the first part, Chongli explores the question of why and how we...

Jul 2020

DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models

This lecture, by DeepMind Research Scientist Andriy Mnih, explores latent variable models, a powerful and flexible framework for generative modelling. After introducing this framework along with the concept of inference, which is central to it, Andriy focuses on two types of modern latent variable models: invertible models and intractable models. Special...

Jul 2020

Women at DeepMind | Applying for Technical Roles

It’s no secret that the gender gap still exists within STEM. Despite a slight increase in recent years, studies show that women only make up about a quarter of the overall STEM workforce in the UK, for example. While the reasons vary, many women report feeling held back by a lack of representation, clear opportunities and information on what working...

Jun 2020

DeepMind x UCL | Deep Learning Lectures | 10/12 | Unsupervised Representation Learning

Unsupervised learning is one of the three major branches of machine learning (along with supervised learning and reinforcement learning). It is also arguably the least developed branch. Its goal is to find a parsimonious description of the input data by uncovering and exploiting its hidden structures. This is presumed to be more reminiscent of how the...

Jun 2020

DeepMind x UCL | Deep Learning Lectures | 9/12 | Generative Adversarial Networks

Generative adversarial networks (GANs), first proposed by Ian Goodfellow et al. in 2014, have emerged as one of the most promising approaches to generative modeling, particularly for image synthesis. In their most basic form, they consist of two "competing" networks: a generator which tries to produce data resembling a given data distribution (e.g.,...

Jun 2020

DeepMind x UCL | Deep Learning Lectures | 8/12 | Attention and Memory in Deep Learning

Attention and memory have emerged as two vital new components of deep learning over the last few years. This lecture by DeepMind Research Scientist Alex Graves covers a broad range of contemporary attention mechanisms, including the implicit attention present in any deep network, as well as both discrete and differentiable variants of explicit attention....

Jun 2020

DeepMind x UCL | Deep Learning Lectures | 7/12 | Deep Learning for Natural Language Processing

This lecture, by DeepMind Research Scientist Felix Hill, first discusses the motivation for modelling language with ANNs: language is highly contextual, typically non-compositional and relies on reconciling many competing sources of information. This section also covers Elman's Finding Structure in Time and simple recurrent networks, the importance...

Jun 2020