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

230 | Raphaël Millière on How Artificial Intelligence Thinks

Mar 20, 2023

01:57:52

Episode guests

Raphaël Millière

Highlights

AI Chapters

Episode notes

Rethink Your Intuition on Model Interpolation

01:14

Memorization enables creativity through generalization

01:59

Semantic Competence in Language Models: A Controversial Perspective

00:56

Beware the Power-Seeking AI

01:22

Introduction

5min

Artificial Intelligence, Machine Learning, Large Language Models, Et Cetera - What's in the News?

4min

Artificial Intelligence Research - Deep Learning

3min

Modern Language Models and Chatbots Are Based on the Transformer Architecture

4min

Bombus - The Better Basics

4min

Is There a Blank Slate in GPT-3?

4min

Is Next Token Prediction a Good Idea?

3min

Is Chat GPT Intelligent, Are They Conscious?

3min

Next Word Prediction Is More Complex Than You Think

4min

10.

Chatbots and the Intentional Halo

4min

11.

The Beta Leap of Artificial Intelligence Research

4min

12.

Using Artificial Neural Networks to Learn Language

4min

13.

The Progress of Connectionism Gives Us a Lesson in Humility

5min

14.

The Architecture of Neural Networks

1min

15.

Compositionality in a Language

2min

16.

The Principle of Compositionality in Natural Language

3min

17.

The Concept of Compositionality

4min

18.

Can We Decode the Internal Encodings of a Language Model?

4min

19.

Is There a Form of Compositional Representation in These Models?

4min

20.

Is There a Jennifer Aniston Neuron in Every Large Language Model?

3min

21.

Is a Large Language Model Intelligence?

2min

22.

Can We Define Semantic Competence to Language Models?

3min

23.

Semantic Competence Is Not a Monolithic Concept

3min

24.

Using Eucalyptus in Definitions

2min

25.

Can a Large Language Model Have an Imagination?

3min

26.

Is Hap's Image Generation Model Merely Teaching Together Pits and Pieces?

4min

27.

Interpolation in Pixel Space

2min

28.

The Connection Between Machine Learning and Compression

5min

29.

Can Language Models Be Agents?

4min

30.

Is There a Threat to Artificial Intelligence?

3min

31.

Scaling Language Models Past Certain Sizes Can Improve Performance

3min

32.

SeiCan - Relating Language to Action

4min

33.

Is There a Time Not Too Far Away to Give Rights to AI Models?

1min

34.

The Right Question and the Legal Question

6min

35.

Can a Computer Think or if You Want to Be Conscious?

3min

Welcome to another episode of Sean Carroll's Mindscape. Today, we're joined by Raphaël Millière, a philosopher and cognitive scientist at Columbia University. We'll be exploring the fascinating topic of how artificial intelligence thinks and processes information. As AI becomes increasingly prevalent in our daily lives, it's important to understand the mechanisms behind its decision-making processes. What are the algorithms and models that underpin AI, and how do they differ from human thought processes? How do machines learn from data, and what are the limitations of this learning? These are just some of the questions we'll be exploring in this episode. Raphaël will be sharing insights from his work in cognitive science, and discussing the latest developments in this rapidly evolving field. So join us as we dive into the mind of artificial intelligence and explore how it thinks.

[The above introduction was artificially generated by ChatGPT.]

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Raphaël Millière received a DPhil in philosophy from the University of Oxford. He is currently a Presidential Scholar in Society and Neuroscience at the Center for Science and Society, and a Lecturer in the Philosophy Department at Columbia University. He also writes and organizes events aimed at a broader audience, including a recent workshop on The Challenge of Compositionality for Artificial Intelligence.

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