1.1 - Fermi estimate of future training runs
Thanks to Paul Christiano, David Roodman, Joe Carlsmith, and Jared Kaplan for feedback on early drafts of this page, and to Miles Brundage for corrections.
Fermi estimate of future training runs Global risk from deep learning [ List of all pages ] About this site 1 - The case for risk 1.1 - Fermi estimate of future training runs # Budgets and total FLOP of future training runs # Model size via scaling laws # Upper bounds for brain-equivalent capabilities # Training data requirements 1.2 - Applications of high-capability models Coming next - Outline of a research program Get updates via mailing list Last update: 2 August 2022 Daniel Dewey Thanks to Paul Christiano, David Roodman, Joe Carlsmith, and Jared Kaplan for feedback on early drafts of this
Explore this link on the map →saved by
related reading
- The Scaling Hypothesis · Gwern.netgwern.net
- Can AI scaling continue through 2030? | Epoch AIepoch.ai
- How To Scale Your Modeljax-ml.github.io
- My picture of the present in AI — LessWronglesswrong.com
- Demystify Transformers: A Guide to Scaling Laws | by Yu-Cheng Tsai | Sage Ai | Mediummedium.com
- Estimating training compute of deep learning models | Epoch AIepochai.org
- On neural scaling and the quanta hypothesisericjmichaud.com
- New Scaling Laws for Large Language Models — LessWronglesswrong.com
- The Little Book of Deep Learningfleuret.org
- AI Timelines via Cumulative Optimization Power: Less Long, More Short — LessWronglesswrong.com
- What will GPT-2030 look like? — AI Alignment Forumalignmentforum.org
- Scaling Laws, Carefully | Lil'Loglilianweng.github.io