IANAL and this is not legal advice, but to explain the issue further in a number of countries training is considered fair use. If someone training the model can find one country where this is the case (and there are no edge case issues from things like copying the data for training) training is legal. So far, the US hasn’t restricted importing models (which specifically for open source might not be possible due to free speech) so using that model in the US is legal as well.
And understood that there are strong opinions on both sides of the argument if this is good or bad.
I tested it against Flux Pro Kontext (also image editing) and while it's a very different style and approach I overall like Flux better. More focus on image consistency, adjusts the lighting correctly, fixes contradictions in the image.
I've been testing it against Flux Pro Kontext for several weeks. I would say it beats Flux in a majority of tests, but Flux still surprises from time-to-time. Banana definitely isn't the best 100% of the time -- it falls a bit short of that. Evolution, not revolution.
Agreed. I find myself alternating between Qwen Image Edit 20B, Kontext, and now Flash 2.5 depending on the situation and style. And of course, Flash isn't open-weights, so if you need more control / less censorship then you're SOL.
No deeper reason. I think there just a lot in LLMs happening right now which skewed it towards them. We would love to do something in the SD ecosystem.
Summarization is much more expensive than vector db's. Assume you have 1m tokens of context. You could run all through GPT-4 and summarize the information, but it would cost $60 (based on current prices) and take 10's of minutes of GPU time to do the inference.
Disclaimer: I work for a16z and on the infra team, so consider me biassed.
If you look through the comments here, folks are mostly referring to keeping for example a chat history. No one is doing 1m words of chat. A common pattern is to summarize a chat history and pass that in the prompt.
As for a corpus of documents (which is what you are presumably talking about), there are a couple problems with what you are saying:
First, you are implying that the content is always new - that's not true for many cases folks are talking about solving (like technical support or customer support), so it's a one time fee to summarize the corpus. You might run it periodically for updates.
Second, there is an assumption that a basic semantic search is the best way to search documents to find the most relevant content. That's questionable before the existence of LLMs, but with LLMs you are basically assuming your cosine similarity search on your vectors is better than an LLM can do with a simple table of contents and question "where should I search?" I haven't seen someone do a detailed study, but the implicit assumption that semantic search is the best idea for text could easily be a bad one.
Third, it assumes the quantum of data to search through is astronomically large and/or getting bigger compared to almost certain decreases in inference cost and increases in input tokens. This will be true for some subset of things, but unlikely to be many and in the cases it is true they'll do something more sophisticated than embeddings and embedding search. They'll probably fine tune the underlying model on an ongoing basis.
Regardless - the post you guys wrote seems... like a stretch for a definition of what this really is And, at least on the surface vector databases appear to be commodity infra. Pinecone might be growing fast now, but how do they ever make much money above their costs? But, you guys seem smart, so maybe there is something there?
Chat history may work, it depends on how long it is and the business model.
I don't quite understand how general summarization would work. If you use an LLM to simply to summarize in order to feed it into a prompt, the summarization needs to be specific to the query. i.e. "summarize what this text says about topic X". You can't summarize long text in a generic way without losing information. Or do I misunderstand the comment?
If you have a perfect table of context (or better, an index by topic) you may not need semantic search. But for the typical use case we are seeing you have unstructured data without an index (e.g. tech support knowledge db entries, company reports, emails). For that, semantic search work quite well.
For the sizes, the observation is that the data that people want to search over (e.g. your email, a wiki, JIRA, a knowledge base) is far larger than the context length. You are correct that we assume that inference cost and speed won't decrease sufficiently quickly in the near future. Why is a longer topic, but in a nutshell GPU speed increase is ~2.5x gen/gen and other than overtraining vs. Chinchilla we don't see immediate model gains. But that is speculative, we don't know what's in store.
To some degree we are just reacting to user adoption in the market. We don't build these systems, but if we see enough of them eventually we recognize the pattern. And while I am optimistic, we could be wrong. AI is major revolution and we are all students.
Yeah, everything here seems basically reasonable, I'd quibble with a couple things but it's debatable. And we might be talking past each other a little bit on use cases. Anyway, it's a fun space.
And if someone is building a chat interface which is effectively a search product then they are going to find these things useful. But it's not a generic LLM memory layer or something.
Wrong school? Our local schools (Silicon Valley, CA) encourage kids to use Wikipedia. Your school may just be a little behind the times.
The discussion now has moved to NLP models. GPT-3 models at this point can generate extremely high quality answers to complex questions. Is there still a point in asking a student to write a few paragraph on the definition and effect of acid rain if you can get that from OpenAI within seconds?
The point isn't to have the kid write the essay about acid rain. The point is to teach the kid about acid rain and have them demonstrate an understanding of it. If the kid just turns in an AI-written essay that they may not have even read, they have learned nothing.
And understood that there are strong opinions on both sides of the argument if this is good or bad.