When a waiter hands me the bill, how do I know whether to pay it myself or let my date pay? On this episode, I get a progress update from Dileep on his company, Vicarious, since Dileep’s last episode. We also talk broadly about his experience running Vicarious to develop AGI and robotics. Then we turn to his latest brain-inspired AI efforts using cloned structured probabilistic graph models to develop an account of how the hippocampus makes a model of the world represents our cognitive maps in different contexts, so we can simulate possible outcomes to choose how to act.
Ken and I discuss open-endedness, the pursuit of ambitious goals by seeking novelty and interesting products instead of advancing directly toward defined objectives. We talk about evolution as a prime example of an open-ended system that has produced astounding organisms, Ken relates how open-endedness could help advance artificial intelligence and neuroscience, and we discuss a range of topics related to the general concept of open-endedness, and Ken takes a couple questions from Stefan Leijnen and Melanie Mitchell.
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.
David, Gyuri, and I discuss the issues they argue for in their back and forth commentaries about the importance of neuroscience and psychology, or implementation-level and computational-level, to advance our understanding of brains and minds – and the names we give to the things we study. Gyuri believes it’s time we use what we know and discover about brain mechanisms to better describe the psychological concepts we refer to as explanations for minds; David believes the psychological concepts are constantly being refined and are just as valid as objects of study to understand minds. They both agree these are important and enjoyable topics to debate.
Jane and I discuss the relationship between AI and neuroscience (cognitive science, etc), from her perspective at Deepmind after a career researching natural intelligence. We also talk about her meta-reinforcement learning work that connects deep reinforcement learning with known brain circuitry and processes, and finally we talk about her recent work using evolutionary strategies to develop altruism and cooperation among the agents in a multi-agent reinforcement learning environment.