#106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind
Jul 3, 2020
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Matt Botvinick, Director of Neuroscience Research at DeepMind, bridges neuroscience and AI with his expertise in cognitive psychology. He discusses how much of the brain remains a mystery and the environmental factors shaping cognition. The conversation delves into the prefrontal cortex's adaptability, the complexities of neuronal communication, and insights into meta-reinforcement learning. Botvinick also raises intriguing questions about AI's potential for human connection and the ethical implications of creating emotionally aware machines.
The prefrontal cortex functions as a meta reinforcement learning system, showcasing how neural networks evolve to adapt and learn based on experience.
Neurons communicate through a rate code, highlighting the frequency of neural spiking as a key mechanism of information transmission in the brain.
Understanding how learning to learn mechanisms unfold in the brain emphasizes the interplay between slow synaptic changes and dynamic neural activity patterns.
Meta learning extends to multiple layers, offering possibilities for enhanced generalization and complex rule learning, with implications for unlocking advanced learning capacities.
Deep dives
Prefrontal Cortex as a Meta Reinforcement Learning System
The prefrontal cortex functions as a meta reinforcement learning system, where neural networks evolve to adapt and learn based on experience. This concept mirrors human meta learning, where learning to learn becomes easier with exposure to diverse tasks. Recurrent neural networks trained with reinforcement learning algorithms reveal a spontaneous emergence of meta learning capabilities, showcasing that slow synaptic changes shape network dynamics into a learning algorithm.
Information Transmission in the Brain
Neurons communicate through a rate code, signifying the frequency of neural spiking as a key mechanism of information transmission in the brain. While some studies propose nuanced forms of communication like precise spike timing, the prevalent viewpoint aligns with the rate at which neurons spike. Artificial neural networks in AI research present feasible models of how information transmission occurs in the brain.
Synaptic Mechanisms and Activity Dynamics
Understanding how learning to learn mechanisms unfold in the brain brings attention to the interplay between slow synaptic changes and neural activity dynamics. The meta learning processes observed in recurrent neural networks highlight the role of dynamic activity patterns influenced by synaptic plasticity. This dynamic interplay offers insights into how the brain potentially forms learning strategies based on structured synaptic adaptations and recurrent connectivity.
Levels of Abstraction in Meta Learning
The concept of meta learning extends to multiple layers of learning, suggesting the possibility of successive levels of learning to learn. While meta meta learning and beyond pose intriguing possibilities for enhanced generalization and complex rule learning, the practical interpretation in the brain's mechanisms remains open to exploration. Understanding learning processes at various levels of abstraction may offer a roadmap to unlocking advanced learning capacities.
Meta Learning and Reinforcement Learning
Meta learning gained significant interest in the AI community as a means to improve reinforcement learning systems without specialized algorithms. The focus was on systems with memory shaped by reinforcement learning, leading to automatic meta learning without human-engineered algorithms. This form of meta learning allowed for abstractness in learning with no need for specific human intervention.
Distributional Coding for Value in Dopamine-Based Reinforcement Learning
Distributional reinforcement learning, emphasizing value distributions over single values, showed promise in accelerating reinforcement learning. Relations were found between dopamine's reward prediction errors and this distributional coding. Studies suggest dopamine may exhibit distributional coding, respecting variations in future outcomes rather than reducing everything to a single value.
Integrating Behavior in Neuroscience and Enhancing AI Flexibility
The revival of behavior-focused neuroscience research incorporating AI insights may lead to new understandings of behavior substrates and cognitive control mechanisms. In AI, the evolving challenge is to develop systems with human-like flexibility in adapting to various tasks swiftly. The emphasis is shifting towards abstraction and cognitive control to enhance AI's adaptability and versatility.
Matt Botvinick is the Director of Neuroscience Research at DeepMind. He is a brilliant cross-disciplinary mind navigating effortlessly between cognitive psychology, computational neuroscience, and artificial intelligence.
Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.
OUTLINE:
00:00 – Introduction
03:29 – How much of the brain do we understand?
14:26 – Psychology
22:53 – The paradox of the human brain
32:23 – Cognition is a function of the environment
39:34 – Prefrontal cortex
53:27 – Information processing in the brain
1:00:11 – Meta-reinforcement learning
1:15:18 – Dopamine
1:19:01 – Neuroscience and AI research
1:23:37 – Human side of AI
1:39:56 – Dopamine and reinforcement learning
1:53:07 – Can we create an AI that a human can love?
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