Generally Intelligent

Episode 21: Chelsea Finn, Stanford, on the biggest bottlenecks in robotics and reinforcement learning

Nov 3, 2022

40:07

Creator website

Episode guests

Chelsea Finn

Highlights

AI Chapters

Episode notes

Are There Alternatives to Reinforcement Learning?

02:58

Resonance Learning - Sample Efficiency

01:57

PhD Student - How Did You Become So Proficient?

03:23

Introduction

2min

How Did You Get Into Computer Science?

2min

How Did You Get Your PhD?

3min

Is There a Benchmark for Distribution Shifts in Machine Learning?

2min

How Do You Decide What Is in or Out of Distribution?

3min

Robotics

2min

Is There a Benchmark for Machine Learning?

2min

The Main Blocker to Working With Real Data?

2min

Are There Alternatives to Reinforcement Learning?

3min

10.

What Are Your Research Opinions on Language Models?

2min

11.

Resonance Learning - Sample Efficiency

2min

12.

What Makes a Great Researcher?

3min

13.

PhD Student - How Did You Become So Proficient?

3min

14.

Is There a Timeline for Artificial General Intelligence?

2min

15.

Building Artificial General Intelligence or a Robot That Can Make a Bowl of Cereal in an Unseen Kitchen?

6min

16.

Is Natural Language Processing a Good Approach to Learning Robotics?

3min

Chelsea Finn is an Assistant Professor at Stanford and part of the Google Brain team. She's interested in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction at scale. In this episode, we chat about some of the biggest bottlenecks in RL and robotics—including distribution shifts, Sim2Real, and sample efficiency—as well as what makes a great researcher, why she aspires to build a robot that can make cereal, and much more.

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