The video from the factored cognition lab meeting is up:
Description:
Ought cofounders Andreas and Jungwon describe the need for process-based machine learning systems. They explain Ought’s recent work decomposing questions to evaluate the strength of findings in randomized controlled trials. They walk through ICE, a beta tool used to chain language model calls together. Lastly, they walk through concrete research directions and how others can contribute.
Outline:
00:00 − 2:00 Opening remarks 2:00 − 2:30 Agenda 2:30 − 9:50 The problem with end-to-end machine learning for reasoning tasks 9:50 − 15:15 Recent progress | Evaluating the strength of evidence in randomized controlled trials trials 15:15 − 17:35 Recent progress | Intro to ICE, the Interactive Composition Explorer 17:35 − 21:17 ICE | Answer by amplification 21:17 − 22:50 ICE | Answer by computation 22:50 − 31:50 ICE | Decomposing questions about placebo 31:50 − 37:25 Accuracy and comparison to baselines 37:25 − 39:10 Outstanding research directions 39:10 − 40:52 Getting started in ICE & The Factored Cognition Primer 40:52 − 43:26 Outstanding research directions 43:26 − 45:02 How to contribute without coding in Python 45:02 − 45:55 Summary 45:55 − 1:13:06 Q&A
The video from the factored cognition lab meeting is up:
Description:
Outline:
The Q&A had lots of good questions.