I would imagine there are many situations within Meta's applications where relatively small models can do a good job — sentiment analysis, abusive language detection, characterising users based on their posts, summarising a user's complaint so it can be ignored more efficiently, assessing whether ads are likely to be fraudulent so they can be run more often, etc.
replyBut Meta has their own models for anything non-SOTA, why buy from outside?
replyGoogle tends to be very good at vision and smaller/ edge
replyHmm ... I was assuming they were using these models for development, but I wonder if any of it might be for production instead - perhaps using vision models to analyze posted content? That would certainly be massive scale, but I'd have thought that scale would require them to be running in their own datacenters.
replyOTOH, if they are stressing Google's capacity then it seems it has to be for production use, which would relfect a massive failure on Meta's side given their investment in datacenters and AI. If they can't utilize their own models and datacenters, then maybe they should just rent the excess capacity to Google! :)
I double check with Gemini anything ML/AI related, anecdotal but I feel like it's much more solid explaining things and pointing out pitfalls.
replyNot really, especially recent gemini's tend to hallucinate unbelievably much especially with visual input.
replyAnd their safety tuning is neither effective nor precise on edge models.
> It's interesting that Meta is heavily using Google's models (as opposed to Anthropic or OpenAI)
replyWho says they aren't? Could be using all of them for "research".
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