UC Berkeley
Transformers Take Over: Transformers Applied to Vision, Language, Video, and More
In 2021, transformers were harnessed to discover drugs, recognize speech, and paint pictures — and much more.
UC Berkeley
Reinforcement Learning Transformed: Transformers succeed at reinforcemend learning tasks.
Transformers have matched or exceeded earlier architectures in language modeling and image classification. New work shows they can achieve state-of-the-art results in some reinforcement learning tasks as well.
UC Berkeley
Reinforcement Learning Transformed: Transformers succeed at reinforcemend learning tasks.
Transformers have matched or exceeded earlier architectures in language modeling and image classification. New work shows they can achieve state-of-the-art results in some reinforcement learning tasks as well.
UC Berkeley
Transformers Are Smarter Than You Think: Language transformers can do math, vision, and logic.
The transformer architecture has shown an uncanny ability to model not only language but also images and proteins. New research found that it can apply what it learns from the first domain to the others.
UC Berkeley
Transformers Are Smarter Than You Think: Language transformers can do math, vision, and logic.
The transformer architecture has shown an uncanny ability to model not only language but also images and proteins. New research found that it can apply what it learns from the first domain to the others.
UC Berkeley
Listening to the Brain: NLP System Translates a Man's Brain Activity Into Words
Neural networks translated a paralyzed man’s brainwaves into conversational phrases. Researchers trained a system to interpret electrical impulses from the brain of a man who had lost the ability to speak 15 years ago, and displayed them as words on a video screen.
UC Berkeley
Listening to the Brain: NLP System Translates a Man's Brain Activity Into Words
Neural networks translated a paralyzed man’s brainwaves into conversational phrases. Researchers trained a system to interpret electrical impulses from the brain of a man who had lost the ability to speak 15 years ago, and displayed them as words on a video screen.
UC Berkeley
Walking the Dog: Training a robot to walk over unsteady terrain with RL.
A reinforcement learning system enabled a four-legged robot to amble over unfamiliar, rapidly changing terrain.
UC Berkeley
Walking the Dog: Training a robot to walk over unsteady terrain with RL.
A reinforcement learning system enabled a four-legged robot to amble over unfamiliar, rapidly changing terrain.
UC Berkeley
Medical AI Gets a Grip: An AI System controlled DaVinci surgical robots.
Surgical robots perform millions of delicate operations annually under human control. Now they’re getting ready to operate on their own.
UC Berkeley
Medical AI Gets a Grip: An AI System controlled DaVinci surgical robots.
Surgical robots perform millions of delicate operations annually under human control. Now they’re getting ready to operate on their own.
UC Berkeley
Greener Machine Learning: Here's how AI models can shrink their carbon footprints.
A new study suggests tactics for machine learning engineers to cut their carbon emissions. Led by David Patterson, researchers at Google and UC Berkeley found that AI developers can shrink a model’s carbon footprint a thousand-fold by streamlining architecture...
UC Berkeley
Greener Machine Learning: Here's how AI models can shrink their carbon footprints.
A new study suggests tactics for machine learning engineers to cut their carbon emissions. Led by David Patterson, researchers at Google and UC Berkeley found that AI developers can shrink a model’s carbon footprint a thousand-fold by streamlining architecture...
UC Berkeley
Vision Models Get Some Attention: Researchers add self-attention to convolutional neural nets.
Self-attention is a key element in state-of-the-art language models, but it struggles to process images because its memory requirement rises rapidly with the size of the input. New research addresses the issue with a simple twist on a convolutional neural network.
UC Berkeley
Vision Models Get Some Attention: Researchers add self-attention to convolutional neural nets.
Self-attention is a key element in state-of-the-art language models, but it struggles to process images because its memory requirement rises rapidly with the size of the input. New research addresses the issue with a simple twist on a convolutional neural network.
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