151–160 of 179 posts
This is one application of AI that I absolutely like. Imagine in future the AI will be able to ingest any video, blog, books, manuals, license and whole lot of other things and we will just be able to ask questions to it or get a summary from it. I wonder what will happen to actual content then. Currently YouTube is showing info about most watched section of the clips. It saves so much time! Now imagine that happenin…
This is one application of AI that I absolutely like. Imagine in future the AI will be able to ingest any video, blog, books, manuals, license and whole lot of other things and we will just be able to ask questions to it or get a summary from it. I wonder what will happen to actual content then. Currently YouTube is showing info about most watched section of the clips. It saves so much time! Now imagine that happenin…
And similar questions.
And the answers were so much similar that it makes no difference. Perhaps Konjer might be better at the emotional part of the conversation, not the factual.
This is one application of AI that I absolutely like. Imagine in future the AI will be able to ingest any video, blog, books, manuals, license and whole lot of other things and we will just be able to ask questions to it or get a summary from it. I wonder what will happen to actual content then. Currently YouTube is showing info about most watched section of the clips. It saves so much time! Now imagine that happenin…
EDIT — answered my own question: there is indeed an OpenAI Embeddings API :
https://beta.openai.com/docs/guides/embeddings
That plus a vector similarity engine (FAISS for example) is the key to these types of apps (Thanks to Simon Willison’s blog, which he pointed to elsewhere in this thread)
How are you getting the book text into gpt3?
You don’t. You cut it into snippets. For those you create embeddings which allow you to rank them by semantic similarity to a query. You then prompt GPT3 with the question plus, say, the three most relevant snippets from the book. The most difficult thing about the process is preventing the model from making stuff up.
Hmm... the first question I asked was for "Sapiens" by Yuval Harari: "Is liberalism a religion?" This was one of the more interesting points that the book made very explicitly: > The modern age has witnessed the rise of a number of new natural-law religions, such as liberalism , Communism, capitalism, nationalism and Nazism. These creeds do not like to be called religions, and refer to themselves as ideologies. But t…
Earlier quoted context omitted.
How usable would the document embedding be for a nearest neighbor search if the dimensions were reduced to three?
can i use the three dimensions to encode a space-filling curve over a 1000-dimensional embedding?
I guess the problem is taking a new document (like a search term) in the higher dimensional embedding and reducing it to three dimensions for searching in that reduced space and expecting that to also maintain the same nearest neighbor ordering.
Would love to know how you built this ( even at a high level )
How do you get a chance to use the chat? For me it’s constantly “at capacity”