AI Alignment as a Solvable Problem | Leopold Aschenbrenner & Richard Hanania

In the popular imagination, the AI alignment debate is between those who say everything is hopeless, and others who tell us there is nothing to worry about.

Leopold Aschenbrenner graduated valedictorian from Columbia in 2021 when he was 19 years old. He is currently a research affiliate at the Global Priorities Institute at Oxford, and previously helped run Future Fund, which works on philanthropy in AI and biosecurity.

He contends that, contrary to popular perceptions, there aren’t that many people working on the alignment issue. Not only that, but he argues that the problem is actually solvable. In this podcast, he discusses what he believes some of the most promising paths forward are. Even if there is only a small probability that AI is dangerous, a small chance of existential risk is something to take seriously.

AI is not all potential downsides. Near the end, the discussion turns to the possibility that it may supercharge a new era of economic growth. Aschebrenner and Hanania discuss fundamental questions of how well GDP numbers still capture what we want to measure, the possibility that regulation strangles AI to death, and whether the changes we see in the coming decades will be on the same scale as the internet or more important.

Listen in podcast form here, or watch on YouTube.

Links:

Leopold Aschenbrenner, “Nobody’s on the Ball on AGI Alignment.”

Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt, “Discovering Latent Knowledge in Language Models Without Supervision.”

Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov, “Locating and Editing Factual Associations in GPT.”

Leopold’s Tweets:

Using GPT4 to interpret GPT2 .

What a model says is not necessarily what’s it’s“thinking” internally.

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