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Exploring the impact of withholding training data on AI intelligence and macroeconomic forecasting
The chapter explores the concept of withholding certain training data to improve AI models' generalization and reasoning abilities, particularly in macroeconomic forecasting where limited datasets present challenges. It also examines the benefits of training AI on diverse datasets, fine-tuning models for various predictions, and the difficulties in balancing fundamental capabilities with task-specific intelligence.
This is the second part of our marathon interview with Carl Shulman. The first episode is on the economy and national security after AGI. You can listen to them in either order!
If we develop artificial general intelligence that's reasonably aligned with human goals, it could put a fast and near-free superhuman advisor in everyone's pocket. How would that affect culture, government, and our ability to act sensibly and coordinate together?
It's common to worry that AI advances will lead to a proliferation of misinformation and further disconnect us from reality. But in today's conversation, AI expert Carl Shulman argues that this underrates the powerful positive applications the technology could have in the public sphere.
Links to learn more, highlights, and full transcript.
As Carl explains, today the most important questions we face as a society remain in the "realm of subjective judgement" -- without any "robust, well-founded scientific consensus on how to answer them." But if AI 'evals' and interpretability advance to the point that it's possible to demonstrate which AI models have truly superhuman judgement and give consistently trustworthy advice, society could converge on firm or 'best-guess' answers to far more cases.
If the answers are publicly visible and confirmable by all, the pressure on officials to act on that advice could be great.
That's because when it's hard to assess if a line has been crossed or not, we usually give people much more discretion. For instance, a journalist inventing an interview that never happened will get fired because it's an unambiguous violation of honesty norms — but so long as there's no universally agreed-upon standard for selective reporting, that same journalist will have substantial discretion to report information that favours their preferred view more often than that which contradicts it.
Similarly, today we have no generally agreed-upon way to tell when a decision-maker has behaved irresponsibly. But if experience clearly shows that following AI advice is the wise move, not seeking or ignoring such advice could become more like crossing a red line — less like making an understandable mistake and more like fabricating your balance sheet.
To illustrate the possible impact, Carl imagines how the COVID pandemic could have played out in the presence of AI advisors that everyone agrees are exceedingly insightful and reliable. But in practice, a significantly superhuman AI might suggest novel approaches better than any we can suggest.
In the past we've usually found it easier to predict how hard technologies like planes or factories will change than to imagine the social shifts that those technologies will create — and the same is likely happening for AI.
Carl Shulman and host Rob Wiblin discuss the above, as well as:
Chapters:
Producer and editor: Keiran Harris
Audio engineering team: Ben Cordell, Simon Monsour, Milo McGuire, and Dominic Armstrong
Transcriptions: Katy Moore
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