
#78 – Danny Hernandez on forecasting and the drivers of AI progress
About this episode
Companies use about 300,000 times 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.
Links to learn more, summary and full transcript.
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.
If this research sounds appealing, you might be interested in applying to join OpenAI's Foresight team — they're currently hiring research engineers.
In the interview, Danny and I (Arden Koehler) 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
Chapters:
- Rob’s intro (00:00:00)
- The interview begins (00:01:29)
- Forecasting (00:07:11)
- Improving the public conversation around AI (00:14:41)
- Danny’s path to OpenAI (00:24:08)
- Calibration training (00:27:18)
- AI and Compute (00:45:22)
- AI and Efficiency (01:09:22)
- Safety teams at OpenAI (01:39:03)
- Careers (01:49:46)
- AI hardware as a possible path to impact (01:55:57)
- Triggers for people’s major decisions (02:08:44)
Producer: Keiran Harris
Audio mastering: Ben Cordell
Transcriptions: Zakee Ulhaq
Get every episode summarized
Each time 80,000 Hours Podcast publishes, we email you a written briefing from the transcript — the topics, who appeared, and any specific claims, with the ad reads skipped.
Email me new episodesFree for 3 shows. No card needed.
No transcript yet
This episode has not been transcribed. Request it and it moves to the front of the queue.
More episodes
More from 80,000 Hours Podcast

'Godfather of AI': I Now See a Path to Safe Superintelligent AI | Yoshua Bengio
80,000 Hours Podcast

'95% of AI Pilots Fail': The hidden agenda behind the viral stat that misled mil...
80,000 Hours Podcast

#242 – Will MacAskill on how we survive the 'intelligence explosion,' AI charact...
80,000 Hours Podcast

Risks from power-seeking AI systems (article narration by Zershaaneh Qureshi)
80,000 Hours Podcast