Country of birth: US
She used reinforcement learning to better understand problem solving in both the human brain and AI systems
Kimberly Stachenfeld, a researcher at DeepMind, helped to develop a theory of the human brain region called the hippocampus, which is responsible for spatial memory and navigation. Now she’s taking her groundbreaking neuroscience work and using it to better understand artificial intelligence.
Earlier theories of the hippocampus focused on its key role in representing the past and one’s current situation, in particular one’s location in space. But Stachenfeld wanted to explain how it may also link the present to the future, by representing the current situation in terms of what it predicts about upcoming events. Using insights from an area of AI called reinforcement learning, which is based on trial and error, Stachenfeld proposed that the hippocampus uses a similar mechanism to make associations between a person’s present state (like being in one’s garage) and a desirable future state (like getting to work on time).
Stachenfeld and her team’s theory better explains how the hippocampus might play a role as a prediction system to help the brain quickly evaluate choices, like getting into a car and heading to work versus staying at home and watching TV on a weekday morning.
Now Stachenfeld is taking what she knows about the brain and aims to use it to improve AI. For instance, AI systems can efficiently learn how to achieve simple tasks—like locating the sugar in your cabinet.
But such systems are no match for the human brain, which can learn many things at once by grouping tasks together, and remembers incidental details while learning a task, which might be useful to recall while learning some other, related task. For example, we learn stirring and mixing are fundamentally similar concepts, and we can reuse similar behaviors to perform them.
If Stachenfeld can figure out how the brain does this, she believes she can will help train AI systems orders of magnitudes faster without the need for as much data.