Scott Aaronson: Against AI Doomerism

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May 11, 2023

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Scott Aaronson

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ANECDOTE

From RoboCup To Quantum Breakthrough

Scott Aaronson started in AI with RoboCup and grad school but shifted to quantum computing after solving a major problem.
That early robotics experience convinced him programming alone wasn't how he wanted to spend his career.

INSIGHT

Quantum Speedups Are Narrow

Quantum computers give exponential speedups only for specialized problems like quantum simulation and factoring.
For general AI tasks, the best generic quantum improvement we know is Grover's square-root speedup, which often isn't enough in practice.

INSIGHT

Quantum Outputs Can Be Unreadable

Some quantum algorithms produce answers as quantum states that are hard to read by measurement.
That makes claimed exponential speedups practically unusable unless the state can be used directly within a larger algorithm.

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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.

Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pub

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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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