Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas

184 | Gary Marcus on Artificial Intelligence and Common Sense

Feb 14, 2022

01:24:17

Highlights

AI Chapters

Episode notes

Deep Learning hostility to Symbols

00:40

Douglas Lenat's Cyc did symbols applied to Shakespeare for comprehension

01:14

Grammar Syntax and Semantics

00:44

Deep Learning vs. Classic Symbol Manipulation in AI

02:05

Introduction

5min

I Love a I

6min

Is There an Artificial Intelligence Podcast Editor?

2min

Deep Learning and Neural Networks - Is That Fair?

2min

What Is Deep Learning?

2min

How Do You Find These Correlations?

4min

The Symbolic Approach to Computer Programming

4min

Deep Learning Is Not So Great With Regular Verbs, Right?

2min

Deep Learning Is a New Success in the Games

2min

10.

Can You Get Infinite Data?

4min

11.

Using Existing Knowledge in the Algorithms

3min

12.

GPT Three

5min

13.

What Is the Conceptual Leap That Makes It Hard?

3min

14.

Is It Possible to Build a Symbolic Correlation?

2min

15.

Symbols and Deep Learning

4min

16.

Is There a Right Rule in Deep Learning?

3min

17.

Can We Deeply Learn Our Way Into Common Sense?

3min

18.

The Most Impressive Shot on Goal

5min

19.

The Problem With Deep Learning

3min

20.

Do Other as Ma Not Understand Form Ofths?

3min

21.

How Much Knowledge Is Crucial in the Human Brain?

5min

22.

Human Intelligence Is Multi Dimensional, Right?

2min

23.

Is There a Difference Between AI and Human Intelligence?

3min

24.

Are There Any Obstacles to Creativity?

2min

25.

Is It Possible to Be Smarter Than Machines in 500 Years?

2min

26.

The Real Challenge Right Now Is Learning Systems

4min

27.

What if Everyone Listened to You?

2min

Artificial intelligence is everywhere around us. Deep-learning algorithms are used to classify images, suggest songs to us, and even to drive cars. But the quest to build truly “human” artificial intelligence is still coming up short. Gary Marcus argues that this is not an accident: the features that make neural networks so powerful also prevent them from developing a robust common-sense view of the world. He advocates combining these techniques with a more symbolic approach to constructing AI algorithms.

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Gary Marcus received his Ph.D. in cognitive science from MIT. He is founder and CEO of Robust.AI, and was formerly a professor of psychology at NYU as well as founder of Geometric Intelligence. Among his books are Rebooting AI: Building Machines We Can Trust (with Ernest Davis).

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