#ai #interview #research
Jacob Steinhardt believes that future AI systems will be qualitatively different than the ones we know currently. We talk about how emergence happens when scaling up, what implications that has on AI Safety, and why thought experiments like the Paperclip Maximizer might be more useful than most people think.
OUTLINE:
0:00 Introduction
1:10 Start of Interview
2:10 Blog posts series
3:56 More Is Different for AI (Blog Post)
7:40 Do you think this emergence is mainly a property from the interaction of things?
9:17 How does phase transition or scaling-up play into AI and Machine Learning?
12:10 GPT-3 as an example of qualitative difference in scaling up
14:08 GPT-3 as an emergent phenomenon in context learning
15:58 Brief introduction of different viewpoints on the future of AI and its alignment
18:51 How does the phenomenon of emergence play into this game between the Engineering and the Philosophy viewpoint?
22:41 Paperclip Maximizer on AI safety and alignment
31:37 Thought Experiments
37:34 Imitative Deception
39:30 TruthfulQA: Measuring How Models Mimic Human Falsehoods (Paper)
42:24 ML Systems Will Have Weird Failure Models (Blog Post)
51:10 Is there any work to get a system to be deceptive?
54:37 Empirical Findings Generalize Surprisingly Far (Blog Post)
1:00:18 What would you recommend to guarantee better AI alignment or safety?
1:05:13 Remarks
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