“Danny Hernandez on Forecasting and the Drivers of AI Progress”, 2020-05-22 (; similar):
Companies use about 300,000× more computation training the best AI systems today than they did in 2012 and algorithmic innovations have also made them 25 times more efficient at the same tasks.
These are the headline results of two recent papers—“AI and Compute” and “AI and Efficiency”—from the Foresight Team at OpenAI. In today’s episode I spoke with one of the authors, Danny Hernandez, who joined OpenAI after helping develop better forecasting methods at Twitch and Open Philanthropy. Danny and I talk about how to understand his team’s results and what they mean (and don’t mean) for how we should think about progress in AI going forward.
Debates around the future of AI can sometimes be pretty abstract and theoretical. Danny hopes that providing rigorous measurements of some of the inputs to AI progress so far can help us better understand what causes that progress, as well as ground debates about the future of AI in a better shared understanding of the field…In the interview, Danny and I also discuss a range of other topics, including:
The question of which experts to believe
Danny’s journey to working at OpenAI
The usefulness of “decision boundaries”
The importance of Moore’s law for people who care about the long-term future
What OpenAI’s Foresight Team’s findings might imply for policy
The question whether progress in the performance of AI systems is linear
The safety teams at OpenAI and who they’re looking to hire
One idea for finding someone to guide your learning
The importance of hardware expertise for making a positive impact
If you believe AI progress is fast, what would progress look like that would convince you it’s slow? Paint a picture of that 5 years from now. What does slow progress look like to you? And now you’re like, “Oh yeah, progress is actually slow”. And what could have happened that would convince you that it’s actually fast. But you can make what would update you clear to yourself and others and that for big decisions, this is generally worthwhile.