UC Berkeley Events | The 2024 Oppenheimer Lecture featuring Andrea Liu @UCBerkeleyEvents | Uploaded 4 months ago | Updated 2 hours ago
Physical systems that can learn by themselves
Brains learn and perform an enormous variety of tasks on their own, using relatively little energy. Brains are able to accomplish this without an external computer because their analog constituent parts (neurons) update their connections without knowing what all the other neurons are doing using local rules. We have developed an approach to learning that shares the property that analog constituent parts update their properties via a local rule, but does not otherwise emulate the brain. Instead, we exploit physics to learn in a far simpler way. Our collaborators have implemented this approach in the lab, developing physical systems that learn and perform machine learning tasks on their own with little energy cost. These systems should open up the opportunity to study how many more is different within a new paradigm for scalable learning.
Physical systems that can learn by themselves
Brains learn and perform an enormous variety of tasks on their own, using relatively little energy. Brains are able to accomplish this without an external computer because their analog constituent parts (neurons) update their connections without knowing what all the other neurons are doing using local rules. We have developed an approach to learning that shares the property that analog constituent parts update their properties via a local rule, but does not otherwise emulate the brain. Instead, we exploit physics to learn in a far simpler way. Our collaborators have implemented this approach in the lab, developing physical systems that learn and perform machine learning tasks on their own with little energy cost. These systems should open up the opportunity to study how many more is different within a new paradigm for scalable learning.
