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- Brain Inspired
- Publisher |
- Paul Middlebrooks
- Media Type |
- audio
- Podknife tags |
- Artificial Intelligence,
- Data Science,
- Interview,
- Machine Learning,
- Neuroscience,
- Philosophy,
- Psychology,
- Science & Medicine,
- Technology
- Publication Date |
- Sep 30, 2020
- Episode Duration |
- 01:43:41
Ida and I discuss the current landscape of reinforcement learning in both natural and artificial intelligence, and how the old story of two RL systems in brains - model-free and model-based - is giving way to a more nuanced story of these two systems constantly interacting and additional RL strategies between model-free and model-based to drive the vast repertoire of our habits and goal-directed behaviors. We discuss Ida’s work on one of those “in-between” strategies, the successor representation RL strategy, which maps onto brain activity and accounts for behavior. We also discuss her interesting background and how it affects her outlook and research pursuit, and the role philosophy has played and continues to play in her thought processes.
Related links:
- nejad.org/">Ida’s website.
- Twitter: @criticalneuro.
- A nice review of what we discuss:
- c608-4b3b-9515-11f6b86527f2.filesusr.com/ugd/a6d7e4_850af79063784e60949ba8529006dee5.pdf">Learning Structures: Predictive Representations, Replay, and Generalization.
Time stamps:
0:00 - Intro 4:50 - Skip intro 9:58 - Core way of thinking 19:58 - Disillusionment 27:22 - Role of philosophy 34:51 - Optimal individual learning strategy 39:28 - Microsoft job 44:48 - Field of reinforcement learning 51:18 - Learning vs. innate priors 59:47 - Incorporating other cognition into RL 1:08:24 - Evolution 1:12:46 - Model-free and model-based RL 1:19:02 - Successor representation 1:26:48 - Are we running all algorithms all the time? 1:28:38 - Heuristics and intuition 1:33:48 - Levels of analysis 1:37:28 - Consciousness
Ida and I discuss the current landscape of reinforcement learning in both natural and artificial intelligence, and how the old story of two RL systems in brains - model-free and model-based - is giving way to a more nuanced story of these two systems constantly interacting and additional RL strategies between model-free and model-based to drive the vast repertoire of our habits and goal-directed behaviors. We discuss Ida’s work on one of those “in-between” strategies, the successor representation RL strategy, which maps onto brain activity and accounts for behavior. We also discuss her interesting background and how it affects her outlook and research pursuit, and the role philosophy has played and continues to play in her thought processes.
Related links:
- nejad.org/">Ida’s website.
- Twitter: @criticalneuro.
- A nice review of what we discuss:
- c608-4b3b-9515-11f6b86527f2.filesusr.com/ugd/a6d7e4_850af79063784e60949ba8529006dee5.pdf">Learning Structures: Predictive Representations, Replay, and Generalization.
Time stamps:
0:00 - Intro 4:50 - Skip intro 9:58 - Core way of thinking 19:58 - Disillusionment 27:22 - Role of philosophy 34:51 - Optimal individual learning strategy 39:28 - Microsoft job 44:48 - Field of reinforcement learning 51:18 - Learning vs. innate priors 59:47 - Incorporating other cognition into RL 1:08:24 - Evolution 1:12:46 - Model-free and model-based RL 1:19:02 - Successor representation 1:26:48 - Are we running all algorithms all the time? 1:28:38 - Heuristics and intuition 1:33:48 - Levels of analysis 1:37:28 - Consciousness
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