Linear Transformations and Embedding Spaces in Machine Learning

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There has been a belief that a linear transform is insufficient to transform one embedding space into another when the semantics are similar, but recent research has shown that this may not be the case.
Models tend to learn similar representations of the same thing, making mapping between them straightforward.
Research in machine learning could benefit from a more empirical approach, observing and demonstrating findings like the invariant nature of embeddings to rotations and scaling.
The invariant nature of operations in vector space opens up the possibility of performing computations on data without knowing its specific content, potentially enabling homomorphic computation.

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(1:17) Sponsor

(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

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Show Notes & references:

-https://same.energy/ (Beta)

-Wright Brothers Bio (https://www.amazon.com/Wright-Brothers-David-McCullough/dp/1476728755)

-Ajeya Cotra article in Less Wrong (https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to)

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