Posts in category: Deepmind

What can the human brain teach us about AI? And what can AI teach us about our own intelligence? These questions underpin a lot of AI research.Read More

What can the human brain teach us about AI? And what can AI teach us about our own intelligence?Read More

This eight part series hosted by mathematician and broadcaster Hannah Fry aims to give listeners an inside look at the fascinating world of AI research and explores some of the questions and challenges the whole field is wrestling with today.Read More

The Serengeti is one of the last remaining sites in the world that hosts an intact community of large mammals. These animals roam over vast swaths of land, some migrating thousands of miles across multiple countries following seasonal rainfall. As human encroachment around the park becomes more intense, these species are forced to alter their behaviours in order to survive. Increasing agriculture, poaching, and climate abnormalities contribute to changes in animal behaviours and population dynamics, but these changes have occurred at spatial and temporal scales which are difficult to monitor using traditional research methods. There is a great urgency to understand how these animal communities function as human pressures grow, both in order to understand the dynamics of these last pristine ecosystems, and to formulate effective management plans to conserve and protect the integrity of this unique biodiversity hotspot.Read More

Artificial intelligence can now predict one of the leading causes of avoidable patient harm up to two days before it happens, as demonstrated byour latest research published in Nature.Read More

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One in a series of posts explaining the theories underpinning our research. Over the last decade, machine learning has made unprecedented progress in areas as diverse as image recognition, self-driving cars and playing complex games like Go. These successes have been largely realised by training deep neural networks with one of two learning paradigmssupervised learning and reinforcement learning. Both paradigms require training signals to be designed by a human and passed to the computer. In the case of supervised learning, these are the targets (such as the correct label for an image); in the case of reinforcement learning, they are the rewards for successful behaviour (such as getting a high score in an Atari game). The limits of learning are therefore defined by the human trainers. While some scientists contend that a sufficiently inclusive training regimefor example, the ability to complete a very wide variety of tasksshould be enough to give rise to general intelligence, others believe that true intelligence will require more independent learning strategies. Consider how a toddler learns, for instance. Her grandmother might sit with her and patiently point out examples of ducks (acting as the instructive signal in supervised learning), or reward her with applause for solving a woodblock puzzle (as in reinforcement learning).Read More

Mastering the strategy, tactical understanding, and team play involved in multiplayer video games represents a critical challenge for AI research. Now, through new developments in reinforcement learning, our agents have achieved human-level performance in Quake III Arena Capture the Flag, a complex multi-agent environment and one of the canonical 3D first-person multiplayer games. These agents demonstrate the ability to team up with both artificial agents and human players.Read More

One in a series of posts explaining the theories underpinning our research. Bugs and software have gone hand in hand since the beginning of computer programming. Over time, software developers have established a set of best practices for testing and debugging before deployment, but these practices are not suited for modern deep learning systems. Today, the prevailing practice in machine learning is to train a system on a training data set, and then test it on another set. While this reveals the average-case performance of models, it is also crucial to ensure robustness, or acceptably high performance even in the worst case. In this article, we describe three approaches for rigorously identifying and eliminating bugs in learned predictive models: adversarial testing, robust learning, and formal verification.Machine learning systems are not robust by default. Even systems that outperform humans in a particular domain can fail at solving simple problems if subtle differences are introduced. For example, consider the problem of image perturbations: a neural network that can classify images better than a human can be easily fooled into believing that sloth is a race car if a small amount of carefully calculated noise is added to the input image.Read More

At DeepMind, the Research Platform Team builds infrastructure to empower and accelerate our AI research. Today, we are excited to share how we developed TF-Replicator, a software library that helps researchers deploy their TensorFlow models on GPUs and Cloud TPUs with minimal effort and no previous experience with distributed systems. TF-Replicators programming model has now been open sourced as part of TensorFlows tf.distribute.Strategy. This blog post gives an overview of the ideas and technical challenges underlying TF-Replicator. For a more comprehensive description, please read our arXiv paper.A recurring theme in recent AI breakthroughs – from AlphaFold to BigGAN to AlphaStar – is the need for effortless and reliable scalability. Increasing amounts of computational capacity allow researchers to train ever-larger neural networks with new capabilities. To address this, the Research Platform Team developed TF-Replicator, which allows researchers to target different hardware accelerators for Machine Learning, scale up workloads to many devices, and seamlessly switch between different types of accelerators.Read More