The Gradient: Perspectives on AI

Scott Aaronson: Against AI Doomerism

May 11, 2023

01:09:32

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Highlights

AI Chapters

Episode notes

AI as abad actor: We will see ot coming

01:33

The Rise and Fall of a Viral AI Service

02:02

Challenges and Hype around Quantum Computers in AI

01:06

AI's Impact on the World: Surprising Results and Empirical Tests

02:00

Introduction

3min

The Importance of Self-Confidence in Quantum Computing

3min

The Importance of Error Correction in AI

4min

How Quantum Computers Are Going to Revolutionize AI and Machine Learning

2min

Quantum Algorithms and the Deep Learning Revolution

3min

The Prospects for a Quantum Computer to Revolutionize AI

4min

The Limits of Quantum Machine Learning

2min

The Intersection Between Quantum Computing and Machine Learning

3min

The Depressing Truth About AI

2min

10.

The Evolution of AI

3min

11.

Theoretical Computer Science Contributions to AI Safety

4min

12.

Dolly's Compositionality at OpenAI

5min

13.

Watermarking the Outputs of GPT

6min

14.

The Problem With Watermarking Language Models

3min

15.

The Problem With Watermarking Language Models

4min

16.

The Coherent Extrapolated Volition of AI

3min

17.

The Hidden Clique Problem in Artificial Intelligence

5min

18.

The Future of AI Alignment

2min

19.

AI and the Future of Humanity

2min

20.

The Death Toll From GPT Stands at Zero

2min

21.

The Dangers of Language Models

6min

In episode 72 of The Gradient Podcast, Daniel Bashir speaks to Professor Scott Aaronson.

Scott is the Schlumberger Centennial Chair of Computer Science at the University of Texas at Austin and director of its Quantum Information Center. His research interests focus on the capabilities and limits of quantum computers and computational complexity theory more broadly. He has recently been on leave to work at OpenAI, where he is researching theoretical foundations of AI safety.

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Outline:

* (00:00) Intro

* (01:45) Scott’s background

* (02:50) Starting grad school in AI, transitioning to quantum computing and the AI / quantum computing intersection

* (05:30) Where quantum computers can give us exponential speedups, simulation overhead, Grover’s algorithm

* (10:50) Overselling of quantum computing applied to AI, Scott’s analysis on quantum machine learning

* (18:45) ML problems that involve quantum mechanics and Scott’s work

* (21:50) Scott’s recent work at OpenAI

* (22:30) Why Scott was skeptical of AI alignment work early on

* (26:30) Unexpected improvements in modern AI and Scott’s belief update

* (32:30) Preliminary Analysis of DALL-E 2 (Marcus & Davis)

* (34:15) Watermarking GPT outputs

* (41:00) Motivations for watermarking and language model detection

* (45:00) Ways around watermarking

* (46:40) Other aspects of Scott’s experience with OpenAI, theoretical problems

* (49:10) Thoughts on definitions for humanistic concepts in AI

* (58:45) Scott’s “reform AI alignment stance” and Eliezer Yudkowsky’s recent comments (+ Daniel pronounces Eliezer wrong), orthogonality thesis, cases for stopping scaling

* (1:08:45) Outro

Links:

* Scott’s blog

* AI-related work

* Quantum Machine Learning Algorithms: Read the Fine Print

* A very preliminary analysis of DALL-E 2 w/ Marcus and Davis

* New AI classifier for indicating AI-written text and Watermarking GPT Outputs

* Writing

* Should GPT exist?

* AI Safety Lecture

* Why I’m not terrified of AI

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