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Watching the Generative AI Hype Bubble Deflate
(ash.harvard.edu)
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this post was submitted on 22 Dec 2024
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I know I'm an enthusiast, but can I just say I'm excited about NotebookLLM? I think it will be great for documenting application development. Having a shared notebook that knows the environment and configuration and architecture and standards for an application and can answer specific questions about it could be really useful.
"AI Notepad" is really underselling it. I'm trying to load up massive Markdown documents to feed into NotebookLLM to try it out. I don't know if it'll work as well as I'm hoping because it takes time to put together enough information to be worthwhile in a format the AI can easily digest. But I'm hopeful.
That's not to take away from your point: the average person probably has little use for this, and wouldn't want to put in the effort to make it worthwhile. But spending way too much time obsessing about nerd things is my calling.
Being able to summarize and answer questions about a specific corpus of text was a use case I was excited for even knowing that LLMs can't really answer general questions or logically reason.
But if Google search summaries are any indication they can't even do that. And I'm not just talking about the screenshots people post, this is my own experience with it.
Maybe if you could run the LLM in an entirely different way such that you could enter a question and then it tells you which part of the source text statistically correlates the most with the words you typed; instead of trying to generate new text. That way in a worse case scenario it just points you to a part of the source text that's irrelevant instead of giving you answers that are subtly wrong or misleading.
Even then I'm not sure the huge computational requirements make it worth it over ctrl-f or a slightly more sophisticated search algorithm.
Isn’t this what the best search engines were doing before the AI summaries?
The main problem now is the proliferation of AI “sources” that are really just keyword stuffed junk websites that take over the first page of search results. And that’s apparently a difficult or unprofitable problem for the search algorithms to solve.
That's what Google was trying to do, yeah, but IMO they weren't doing a very good job of it (really old Google search was good if you knew how to structure your queries, but then they tried to make it so you could ask plain English questions instead of having to think about what keywords you were using and that ruined it IMO). And you also weren't able to run it against your own documents.
LLMs on the other hand are so good at statistical correlation that they're able to pass the Turing test. They know what words mean in context (in as much they "know" anything) instead of just matching keywords and a short list of synonyms. So there's reason to believe that if you were able to see which parts of the source text the LLM considered to be the most similar to a query that could be pretty good.
There is also the possibility of running one locally to search your own notes and documents. But like I said I'm not sure I want to max out my GPU to do a document search.