# Scott Aaronson: Against AI Doomerism

Source: https://thegradientpub.substack.com/p/scott-aaronson-against-ai-doomerism
Kiwi News: https://news.kiwistand.com/stories/Scott-Aaronson-Against-AI-Doomerism?index=0x645e3773a76fed8d7130b4e767087b2561fe0ea39658df577d00039e176ed9273734e0b8
Submitted by seanhart.eth on 2023-05-12 UTC, 2 upvotes, 0 comments

## Summary

In an episode of The Gradient Podcast, Daniel Bashir interviews Scott Aaronson, Schlumberger Centennial Chair of Computer Science at the University of Texas at Austin, who has been on leave to research theoretical foundations of AI safety at OpenAI. The conversation covers the limits of quantum computing applied to machine learning, watermarking GPT outputs, and Aaronson's early skepticism of AI alignment work, which he revisits after unexpected improvements in modern AI. It also covers his "reform AI alignment stance," Eliezer Yudkowsky's recent comments, and cases for stopping scaling.

## Article

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