Challenges of Merging AI Models and the Concept of ROT

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The chapter delves into the difficulties of merging AI models like GPT-3 and GPT-4 together, highlighting the consistent approach of training new AI systems from scratch. It explores the concept of 'rot' in software, human brains, and potentially in large language models, raising concerns about sustaining a single long-lasting super AI. The discussion also delves into the ethical treatment of language models and the parallels drawn with historical perspectives on slavery.

In this episode, Nathan sits down with Robin Hanson, associate professor of economics at George Mason University and researcher at Oxford’s Future of Humanity Institute. They discuss the comparison of human brains to LLMs and legacy software systems, what it would take for AI and automation to significantly impact the economy, our relationships with AI and the moral weight it has, and much more. Try the Brave search API for free for up to 2000 queries per month at https://brave.com/api

LINKS:

Robin’s Book, The Age of Em: https://ageofem.com/
Robin’s essay on Automation: https://www.overcomingbias.com/p/no-recent-automation-revolutionhtml
Robin’s Blog: https://www.overcomingbias.com/
AI Scouting Report: https://www.youtube.com/watch?v=0hvtiVQ_LqQ&list=PLVfJCYRuaJIXooK_KWju5djdVmEpH81ee&pp=iAQB
Dr. Isaac Kohane Episode: https://www.youtube.com/watch?v=pS5Vye671Xg

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TIMESTAMPS

(00:00) Preview

(07:10) Why our current time is a “dream time” and the move back to a Malthusian world

(13:30) What sort of world should we be striving for?

(13:40) Sponsor - Brave

(17:50) Distinguishing value talk from factual talk

(18:00) Comparing and contrasting Ems to LLMs

(22:30) The comparison of human brains to legacy software systems

(30:52) Sponsor - Netsuite

(41:01) AIs in medicine

(53:30) A several century innovation pause

(55:30) Achieving full human level AI in the next 60-90 years

(1:03:55) Chess and routine benchmarks not a good predictor of AI performance in the economy

(1:07:44) Reaching and exceeding human-level AI in the next 1000 years

(1:11:40) Losing technologies tied to scale economies

(1:12:00) Why AI is hard to maintain in the long run

(1:12:20) Standard deviation in automation

(1:14:05) Computing power grows exponentially but automation grows steadily

(1:15:50) AI art generation and deepfakes

(01:21:42) The economics of AI-powered coding

(1:33:51) Merging LLMs

(1:36:02) Rot in software and the human brain

(1:40:18) Parallelism in LLMs and brain design

(1:41:00]) Moral weight for AIs, enslavement, and cooperation with AI

(1:47:10) What would change Robin’s mind about the future

(1:49:18) Wrap

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