Machine Learning Street Talk (MLST)

#029 GPT-3, Prompt Engineering, Trading, AI Alignment, Intelligence

08 Nov 2020

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Description

This week Dr. Tim Scarfe, Dr. Keith Duggar, Yannic Kilcher and Connor Leahy cover a broad range of topics, ranging from academia, GPT-3 and whether prompt engineering could be the next in-demand skill, markets and economics including trading and whether you can predict the stock market, AI alignment, utilitarian philosophy, randomness and intelligence and even whether the universe is infinite! 00:00:00 Show Introduction 00:12:49 Academia and doing a Ph.D 00:15:49 From academia to wall street 00:17:08 Quants -- smoke and mirrors? Tail Risk 00:19:46 Previous results dont indicate future success in markets 00:23:23 Making money from social media signals? 00:24:41 Predicting the stock market 00:27:20 Things which are and are not predictable 00:31:40 Tim postscript comment on predicting markets 00:32:37 Connor take on markets 00:35:16 As market become more efficient.. 00:36:38 Snake oil in ML 00:39:20 GPT-3, we have changed our minds 00:52:34 Prompt engineering a new form of software development? 01:06:07 GPT-3 and prompt engineering 01:12:33 Emergent intelligence with increasingly weird abstractions 01:27:29 Wireheading and the economy 01:28:54 Free markets, dragon story and price vs value 01:33:59 Utilitarian philosophy and what does good look like? 01:41:39 Randomness and intelligence 01:44:55 Different schools of thought in ML 01:46:09 Is the universe infinite? Thanks a lot for Connor Leahy for being a guest on today's show. https://twitter.com/NPCollapse -- you can join his EleutherAI community discord here: https://discord.com/invite/vtRgjbM

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Machine Learning Street Talk (MLST)

#029 GPT-3, Prompt Engineering, Trading, AI Alignment, Intelligence

This episode hasn't been transcribed yet

Other episodes from Machine Learning Street Talk (MLST)

Abstraction & Idealization: AI's Plato Problem [Mazviita Chirimuuta]

Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION]

Adversarial Examples and Data Modelling - Andrew Ilyas (MIT)

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