Posts in category: Deepmind

More than a century ago, Srinivasa Ramanujan shocked the mathematical world with his extraordinary ability to see remarkable patterns in numbers that no one else could see. The self-taught mathematician from India described his insights as deeply intuitive and spiritual, and patterns often came to him in vivid dreams.Read More

Our main results prove that while reward can express many tasks, there exist instances of each task type that no Markov reward function can capture. We then provide a set of polynomial-time algorithms that construct a reward function which allows an agent to optimize tasks of each of these three types, and correctly determine when no such reward function exists.Read More

Our brain has an amazing ability to process visual information. We can take one glance at a complex scene, and within milliseconds be able to parse it into objects and their attributes, like colour or size, and use this information to describe the scene in simple language. Underlying this seemingly effortless ability is a complex computation performed by our visual cortex, which involves taking millions of neural impulses transmitted from the retina and transforming them into a more meaningful form that can be mapped to the simple language description. In order to fully understand how this process works in the brain, we need to figure out both how the semantically meaningful information is represented in the firing of neurons at the end of the visual processing hierarchy, and how such a representation may be learnt from largely untaught experience.Read More

Our brain has an amazing ability to process visual information. We can take one glance at a complex scene, and within milliseconds be able to parse it into objects and their attributes, like colour or size, and use this information to describe the scene in simple language. Underlying this seemingly effortless ability is a complex computation performed by our visual cortex, which involves taking millions of neural impulses transmitted from the retina and transforming them into a more meaningful form that can be mapped to the simple language description. In order to fully understand how this process works in the brain, we need to figure out both how the semantically meaningful information is represented in the firing of neurons at the end of the visual processing hierarchy, and how such a representation may be learnt from largely untaught experience.Read More

Koray Kavukcuoglu, VP of Research, discusses why addressing real-world challenges now helps advance the development of true AGI.Read More

When people picture a world with artificial general intelligence (AGI), robots are more likely to come to mind than enabling solutions to society’s most intractable problems. But I believe the latter is much closer to the truth. AI is already enabling huge leaps in tackling fundamental challenges: from solving protein folding to predicting accurate weather patterns, scientists are increasingly using AI to deduce the rules and principles that underpin highly complex real-world domains – ones they might never have discovered unaided. Advances in AGI research will supercharge society’s ability to tackle and manage climate change – not least because of its urgency but also due to its complex and multifaceted nature.Read More

As part of DeepMind’s mission of advancing science, we have acquired the MuJoCo physics simulator and are making it freely available for everyone, to support research everywhere.Read More

When you walk, your feet make contact with the ground. When you write, your fingers make contact with the pen. Physical contacts are what makes interaction with the world possible. Yet, for such a common occurrence, contact is a surprisingly complex phenomenon. Taking place at microscopic scales at the interface of two bodies, contacts can be soft or stiff, bouncy or spongy, slippery or sticky. It’s no wonder our fingertips have four different types of touch-sensors. This subtle complexity makes simulating physical contact — a vital component of robotics research — a tricky task.Read More

Introducing RGB-Stacking as a new benchmark for vision-based robotic manipulation.Read More

Picking up a stick and balancing it atop a log or stacking a pebble on a stone may seem like simple — and quite similar — actions for a person. However, most robots struggle with handling more than one such task at a time. Manipulating a stick requires a different set of behaviours than stacking stones, never mind piling various dishes on top of one another or assembling furniture. Before we can teach robots how to perform these kinds of tasks, they first need to learn how to interact with a far greater range of objects. As part of DeepMind’s mission and as a step toward making more generalisable and useful robots, we’re exploring how to enable robots to better understand the interactions of objects with diverse geometries.Read More