"without training" describes transfer learning
LLMs can see and hear without any training
51–60 of 74 posts
Re: LLMs can see and hear without any training
#52[deleted]
Re: LLMs can see and hear without any training
#53"without training" describes transfer learning with an actor / critic approach
Re: LLMs can see and hear without any training
#54"without training" describes transfer learning
hey what the hell? it said the username was taken?? bug???
Re: LLMs can see and hear without any training
#55I just remember Zuck's comments about AI and how the idea of it dooming our species is a bit silly, etc
This is the wrong approach to take. At minimum you have to say things like "well yes we're always on the lookout for this kind of thing". With him? Not a care in the world
Re: LLMs can see and hear without any training
#56Earlier quoted context omitted.
> zero shot I really wish we would find a different term for this. Doing something always takes at least one attempt, i.e. "one shotting". "Zero shotting" is an oxymoron, which makes it a term that only creates more confusion rather than succinctly conveying something.
Array indexing can start at 0 or 1.
For an array of zero shots, the indexing doesn’t matter.
Re: LLMs can see and hear without any training
#57Earlier quoted context omitted.
> zero shot I really wish we would find a different term for this. Doing something always takes at least one attempt, i.e. "one shotting". "Zero shotting" is an oxymoron, which makes it a term that only creates more confusion rather than succinctly conveying something.
It's a shot from position zero
No it isn't. The number of shots (examples) is zero.
Re: LLMs can see and hear without any training
#58That looks like a classic Actor/Critic setup, yet it's not mentioned even once in the paper. Am I missing some large difference here?
In actor/critic the actor and critic are normally learned, i.e., their weights are adjusted during the process. The paper is correct that their method is zero-shot, but it doesn't mention that their method is essentially equivalent to a few rounds of training but then discarding the training update. Anyone who works with deep architectures and momentum-based optimizers knows that the first few updates alone provide l…
> discarding the training update
But they aren't updating the model weights. They're iteratively updating the prompt. It's automating the process that humans use with generative models.
Agreed that it's conceptually equivalent though.
Re: LLMs can see and hear without any training
#59I’ve read the paper and the skeptical comments here, to wit: it’s just an actor/critic pipeline by another name. I’ll bite and say this is actually interesting — and the paper title is misleading. What they’ve done here is hooked up a text-only LLM to multimodal critics, given it (mostly) an image diffusion generation task, and asked it to improve its prompting of the multimodal generation by getting a set of scores…
> zero shot I really wish we would find a different term for this. Doing something always takes at least one attempt, i.e. "one shotting". "Zero shotting" is an oxymoron, which makes it a term that only creates more confusion rather than succinctly conveying something.
My favorite AI term to ridicule is the recent "Test Time Compute" nonsense, which has nothing whatsoever to do with testing. It literally just means "inference time".
And if I hear someone say "banger", "cooking", "insane", or "crazy", one more time I'm going to sledge hammer my computer. Can't someone, under 40 please pick up a book and read. Yesterday Sam Altman tried to coin "Skillsmaxxing" in a tweet. I threw my coffee cup at my laptop.
Re: LLMs can see and hear without any training
#60Earlier quoted context omitted.
> zero shot I really wish we would find a different term for this. Doing something always takes at least one attempt, i.e. "one shotting". "Zero shotting" is an oxymoron, which makes it a term that only creates more confusion rather than succinctly conveying something.
My favorite AI term to ridicule is the recent "Test Time Compute" nonsense, which has nothing whatsoever to do with testing. It literally just means "inference time". And if I hear someone say "banger", "cooking", "insane", or "crazy", one more time I'm going to sledge hammer my computer. Can't someone, under 40 please pick up a book and read. Yesterday Sam Altman tried to coin "Skillsmaxxing" in a tweet. I threw my…
Speaking of old-timers and "inference time" - there was a time when "inference" meant inferring parameters from data (i.e. training). And now it means "test-time". (or maybe the difference is if it's statistics community vs ML community).
e.g. Bishop's textbook says:
5.2.4 Inference and decision
We have broken the classification problem down into two separate stages, the inference stage in which we use training data to learn a model for p(Ck|x) and the subsequent decision stage in which we use these posterior probabilities to make op- timal class assignments.