Posts in category: Deepmind

Our new Enformer architecture advances genetic research by improving the ability to predict how DNA sequence influences gene expression.Read More

When the Human Genome Project succeeded in mapping the DNA sequence of the human genome, the international research community were excited by the opportunity to better understand the genetic instructions that influence human health and development. DNA carries the genetic information that determines everything from eye colour to susceptibility to certain diseases and disorders. The roughly 20,000 sections of DNA in the human body known as genes contain instructions about the amino acid sequence of proteins, which perform numerous essential functions in our cells. Yet these genes make up less than 2% of the genome. The remaining base pairs — which account for 98% of the 3 billion “letters” in the genome — are called “non-coding” and contain less well-understood instructions about when and where genes should be produced or expressed in the human body. At DeepMind, we believe that AI can unlock a deeper understanding of such complex domains, accelerating scientific progress and offering potential benefits to human health.Read More

Our latest research and state-of-the-art model advances the science of Precipitation Nowcasting.Read More

Our lives are dependent on the weather. At any moment in the UK, according to one study, one third of the country has talked about the weather in the past hour, reflecting the importance of weather in daily life. Amongst weather phenomena, rain is especially important because of its influence on our everyday decisions. Should I take an umbrella? How should we route vehicles experiencing heavy rain? What safety measures do we take for outdoor events? Will there be a flood? Our latest research and state-of-the-art model advances the science of Precipitation Nowcasting, which is the prediction of rain (and other precipitation phenomena) within the next 1-2 hours. In a paper written in collaboration with the Met Office and published in Nature, we directly tackle this important grand challenge in weather prediction. This collaboration between environmental science and AI focuses on value for decision-makers, opening up new avenues for the nowcasting of rain, and points to the opportunities for AI in supporting our response to the challenges of decision-making in an environment under constant change.Read More

We argue that merely using curiosity for fast environment exploration or as a bonus reward for a specific task does not harness the full potential of this technique and misses useful skills. Instead, we propose to shift the focus towards retaining the behaviours which emerge during curiosity-based learning. We posit that these self-discovered behaviours serve as valuable skills in an agent’s repertoire to solve related tasks.Read More

We argue that merely using curiosity for fast environment exploration or as a bonus reward for a specific task does not harness the full potential of this technique and misses useful skills. Instead, we propose to shift the focus towards retaining the behaviours which emerge during curiosity-based learning. We posit that these self-discovered behaviours serve as valuable skills in an agent’s repertoire to solve related tasks.Read More

In our paper, we focus on LMs and their propensity to generate toxic language. We study the effectiveness of different methods to mitigate LM toxicity, and their side-effects, and we investigate the reliability and limits of classifier-based automatic toxicity evaluation.Read More

In our paper, we focus on LMs and their propensity to generate toxic language. We study the effectiveness of different methods to mitigate LM toxicity, and their side-effects, and we investigate the reliability and limits of classifier-based automatic toxicity evaluation.Read More

Most architectures used by AI systems today are specialists. A 2D residual network may be a good choice for processing images, but at best it’s a loose fit for other kinds of data — such as the Lidar signals used in self-driving cars or the torques used in robotics. What’s more, standard architectures are often designed with only one task in mind, often leading engineers to bend over backwards to reshape, distort, or otherwise modify their inputs and outputs in hopes that a standard architecture can learn to handle their problem correctly. Dealing with more than one kind of data, like the sounds and images that make up videos, is even more complicated and usually involves complex, hand-tuned systems built from many different parts, even for simple tasks. As part of DeepMind’s mission of solving intelligence to advance science and humanity, we want to build systems that can solve problems that use many types of inputs and outputs, so we began to explore a more general and versatile architecture that can handle all types of data.Read More

Perceiver IO, a more general version of the Perceiver architecture, can produce a wide variety of outputs from many different inputs.Read More