That said, Google’s Gemini integration with Google Workplace apps is useful right now, and seems to be getting better. For some strange reason Google does not have Gemini integration with Google Calendar and asking the GMail integration what is on my schedule is only accurate if information is in emails.
I don’t intend to dump on Google, I liked working there and I use their paid for products like GCP, YouTube Plus, etc., but I don’t use their search all that often. I am paying for their $20/month LLM+Google One bundle, and I hope that evolves into a paid for high quality, no ad service.
Interesting how far LLMs can get? Looks like we are close to scale-up limit. It's technically difficult to get bigger models. The way to go probably is to add assisting sub-modules. Examples would be web search, have it already. Database of facts, similar to search. Compilers, image analyzers, etc. With this approach LLM is only responsible for generic decisions and doesn't need to be that big. No need to memorize all data. Even logic can be partially outsourced to sub-module.
Models are pretty much fungible at this point if you’re not trying to do any LoRAs or fine tunes.
I agree scoreboards are not a highly accurate ranking of model capabilities for a variety of reasons.
It's complicated tasks requiring step by step logical reasoning where GPT-4 is clearly still very much in a league of its own.
Google's business is largely not predicated on AI the way everyone else is. Sure they hope it's a driver of growth, but if the entire LLM industry disappeared, they'd be fine. Google doesn't need AI "Superiority", they need "good enough" to prevent the masses from product switching.
If the entire world is saturated in AI, then it no longer becomes a differentiator to drive switching. And maybe the arms race will die down, and they can save on costs trying to out-gun everyone else.