Anton Troynikov, the founder of Chroma and Stable Attribution, discusses embeddings, the trade-offs and optimizations involved with working with embeddings at scale, the "Stable Attribution" project, and the big picture of recent AI developments.
(4:00) Anton breaks down the advantages of vector databases
(4:45) How embeddings have created an AI-native way to represent data
(11:50) Anton identifies the watershed moment and step changes in AI
(12:55) Open AI’s pricing
(18:50) How Chroma works
(33:04) Stable Attribution and systematic bias
(36:48) How latent diffusion models work
(51:26) How AI is like the early days of aviation
(56:01) How Disney inspired the release of Stable Attribution
(59:53) Why noise can lead to generalization
(1:01:04) Nathan’s KPI for The Cognitive Revolution
(1:01:59) Other use cases for embedding
(1:03: 19) Anton touches on the applications for biotech
(1:04:35) Anton on doomerism hysteria and what actually worries him
(1:11:43) - Nathan sums up a plausible doomer scenario
(1:20:17) What AI tools does Anton use and why?
(1:22:55) Anton’s hopes for the future
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-Same Energy visual search engine: https://same.energy/ (Beta)
-Wright Brothers Bio (https://www.amazon.com/Wright-Brothers-David-McCullough/dp/1476728755)
-Ajeya Cotra's article in Less Wrong (https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to)