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Comment by axpy906 | Hacker News Reader

Nice article. I remember the original work. Can you elaborate on this one Max? > Even if the generative AI industry crashes

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It's a note that embeddings R&D is orthogonal to whatever happens with generative AI even though both involve LLMs.

I'm not saying that generative AI will crash but if it's indeed at the top of the S-curve there could be issues, notwithstanding the cost and legal issues that are only increasing.

While there is no real definition of LLM I’m not sure I would say both involve LLMs. There is a trend towards using the hidden state of an LLM as an embedding but this is relatively recent, and overkill for most use-cases. Plenty of embedding models are not large, and it’s fairly trivial to train a small domain-specific embedding model that has incredible utility.

To some approximation, if you understood what BERT was at the time it was released, you'd consider it the first modern LLM. GPT-1 was OpenAI's BERT.

Timeline would be viewed as:

2017: transformers

2018: bert

2018: gpt-1

2019: gpt-2

2020: gpt-3

2022: gpt-3.5 (chatgpt)

BERT was not large (it was under a billion parameters). And it wasn't an autoregressive language model like GPT

Relative to today's models, it is small.

For the time, it was large.

Re: that it's not auto regressive, that's correct.

Things built on eachother smoothly. Transformers to BERT to GPT.

I think the author is implying that even if you can't extract real world value from generative AI, the current AI hype has evolved embeddings to a point they can provide real world value to a lot of projects (like the semantic search demonstrated in the article, where no generative AI was used).

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