[hinkley]

I think I could trust AI more if we used it to do heuristics for expensive deterministic processes. Sort of a cross between Bloom Filters and speculative execution. Determine the odds the expensive operation 1 will indicate that expensive operation 2 needs to happen, and then start expensive operation 2 while we determine if it’s actually needed. If its right 95% of the time, which is the sort of ranges AI can aspire to, that’s skipping the high latency task chaining 19 times out of 20, which would be pretty good.

[hnuser123456]

There are Bayesian neural networks that could apparently track probability rather than just e.g. randomly selecting one output from the top-k based on probability, but I'm still learning up on them myself. Sounds like they're not normally combined with language models.

[inkysigma]

Iirc, the problem with Bayesian neural networks is that they're significantly more difficult to train. Using stuff like SVI reduces a lot of the representational ability of the distribution over weights. It's also questionable how useful the uncertainty over weights is.

I suppose in the tradition of Bayesian influence, VAEs and the like are still common though.

[rjsw]

There have been comments that some leading AI researchers were switching away from working on language models to do stuff with "real world data".

[p1esk]

What do you mean?

[tornikeo]

Meaning a GPT but next token is a live sensor reading or a servo angle or accelerometer state. Then connect that GPT with an actual LLM as a controller and you (hopefully) have a physical machine with arms, legs and a mind.

[0-_-0]

CPU branch predictors use neural networks

[hinkley]

CPU branch predictors aren’t going to run long expensive operations in the background. This is like saying bloom filters are speculative memory fetches. That’s not completely untrue but it misses the point.