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Many in the AI field think the bigger-is-better approach is running out of road

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Re: Many in the AI field think the bigger-is-better approach is running out of road

#291
Is this really a bad thing, or a problem at all? Some researchers found that increased scale resulted in increased utility. LLMs aren't generalized AI, but they seem to be good at some stuff.

Their must intrinsically be some limits to this approach. But that doesn't mean it is a problem or that LLMs cant serve some useful role. Just consider for a moment if we could train an LLM on the combined experiences of all humans that have ever lived. No one is going to suggest that we must go bigger.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#292

It's been just a few months since GPT4. Calm down.

Really, looks like our expectations are doubling every 6 months. This question needs a little more exploring, although it's bound to happen somewhere and we'd love believing we are close to the maximum possible.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#293

Isn't the fundamental problem that LLM's don't actually understand anything (as greater concepts), but rather operate as complex probability machines? My 2 month active experience with ChatGPT-4 gave me the following takeaways: - when it's right, it's amazing; and when you, the operator, can recognize the niche use case where it performs really well, it can be a game-changer (although you could have programmed a tool…

I still don’t understand what it means when people say stuff like “ChatGPT just predicts the most likely next word with the highest probability” or “ChatGPT is just a probability machine”. Concretely, given the N most recent words, what algorithm are you proposing/claiming it uses to assign probabilities to word N+1? Just saying “it chooses the next word with highest probability” doesn’t explain how the probabilities are estimated.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#294

Earlier quoted context omitted.

I agree that is a fundamental difference. That’s what I meant about reinforcement learning. Our ‘model weights’ are being updated with new data all the time. I was just referring to what happens at a specific instance in time when someone asks me for example ‘What’s the capital of Norway?’

That one’s not a great example. Either you know the capital or you don’t. There’s no process (other than research) by which you can learn the name while attempting to answer. A question I get much more often is “how do I solve this math problem?” Many times, the problem is one I’ve never seen before. So in the process of answering the question, I also learn how to solve the problem too.

While you can apply zero shot learning and get the answer to a new math problem, you are only apply the learning to significant depth after a fine-tuning session - sleep.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#295
post #271

Earlier quoted context omitted.

The burden is on you to prove your claims.

I'm not trying to demonstrate that my claims are true. I'm trying to demonstrate that it is meaningless to discuss these topics in the first place, because we don't understand the workings of the mind nearly well enough to distinguish things like "truthfulness" and "concepts".

The fact that we don't know exactly how our brains work, doesn't mean we cannot observe the results of their work.

And as I have demonstrated above, humans, and for that matter other species on this planet featuring capable brains like Corvidae or Cetaceans, do in fact have a concept of truth: They are capable of recognizing false or misleading information as being incongruous with objective reality: A raven that sees me putting food into my left hand, will not jump to a patch of ground where I pretend to put food with my right hand.

This is despite the fact that my actions of "hiding the food" with the empty hand are stochastically indistinguishable from an action of actually hiding food from with my left hand.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#296
post #155

Earlier quoted context omitted.

> A LM doesn't understand "truthfulness". It has no concept of a sequence being true or not, only of a sequence being probable. I claim that the human brain doesn't understand "truthfulness" either. It merely creates the impression that understanding is taking place, by adapting to social and environmental pressures. The brain has no "concepts" at all, it just generates output based on its input, its internal wiring,…

> Do you have any evidence that contradicts that claim? Empirical evidence? Yes I do. The brain commands an entity that has to exist and function in the context of objective reality. Being unable to verify it's internal state against that, would have been negatively selected some time ago, because stating: "I'm sure that rumbling cave bear with those big sharp teeth is a peaceful herbivore" won't change the objective…

Wouldn't that type of response fit in with how LMs work though? That caveman likely learned a lot of things over time, like: large animals can end life more likely than small ones, animals making loud noises are likely more dangerous, sharp teeth/claws are dangerous, or I saw one of those kill another caveman. All of those things tilt the probability of associating that loud cave bear with a high risk of death. That doesn't mean there's some inherit 'truth' that the caveman brain 'knows', it's just a high probability that it's a correct assessment of the input. Every true thing is really just an evaluation of probability in the end.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#297
post #11

We need a way to make tight little specialist models that don't hallucinate and reliably report when they don't know. Trying to cram all of the web into a LLM is a dead end.

> and reliably report when they don't know. Then we need a new system, because LMs, no matter if they are large or not, cannot do that, for a very simple reason: A LM doesn't understand "truthfulness". It has no concept of a sequence being true or not, only of a sequence being probable. And that probability cannot work as a standin for truthfulness, because the LM doesn't produce improbable sequences to begin with...…

>The LM simply has no way of knowing whether the sequence it just predicted is grounded in reality or not.

Base GPT-4 was excellently calibrated. So this is just wrong.

https://imgur.com/a/3gYel9r

Re: Many in the AI field think the bigger-is-better approach is running out of road

#298

Earlier quoted context omitted.

What's needed, ideally, is a checker. Something that takes the LLM's output, can go back to the training material, and verify the output for consistency with it. I don't think those steps are out of the bounds of possibility, really.

> Something that takes the LLM's output, can go back to the training material, and verify the output for consistency with it. The problem is what you mean when you say "consistency". The LM checks if sequences are stochastically consistent with other sequences in the training data. Within that realm, the sentence: "In the Water Wars of 1999, the Antarctic Coalitions aramada of Hovercraft valiantly faught in the battl…

>But to know that, one has to understand what the data means semantically. And our current AIs ... well, don't.

Another wrong statement, you're on a roll today.

https://arxiv.org/abs/2305.11169

https://arxiv.org/abs/2306.12672

There's a word we would use to describe your confidently erroneous statements were it one of the outputs an LLM. Wonder what that might be..

Re: Many in the AI field think the bigger-is-better approach is running out of road

#299

Earlier quoted context omitted.

Where, exactly, did the LLM see this epilogue to The Great Gatsby before? https://twitter.com/tsimonite/status/1653065940463157248

I responded 'way to not getsby the point'. How is that in ANY WAY an epilogue to The Great Gatsby? This is exactly the problem. That original story builds with a series of revelations into the conclusion 'so we beat on, boats against the current, borne back ceaselessly into our past': establishing a PURPOSE, perhaps a bleak and unwelcome one. Fitzgerald's revealing an insight into the delusions of humanity. He's pict…

In so doing, it's less than Gatsby and way less than Fitzgerald. There is nothing here in this 'epilogue'.

"This Commodore 64 is useless. It can't even run Crysis."

Snark aside, I didn't ask if the epilogue was any good or not, I asked where it came from. It came from our collective consciousness as embedded in the language model. It turns out that what we've been dismissing as mere "language" is an insanely powerful thing, maybe the only thing.

We're still in the first publicly-visible generation of LLM technology, and the model that generated the epilogue was already behind the leading edge in many respects. Anyone who's not blown away by this is whistling past the graveyard. Computers are now doing what we do. Yes, they still kinda suck at it. But they will get better at it much faster than we will.

I mean, really. What is a human author thinking, if not, "What would feel nice here? OK, now what seems like it would go with this sort of thing? OK, something else, let's have more, what kind of concepts go here? What do people normally say when they talk in this way?" It's been understood since the Greek classical period that there is only a finite amount of ore in the original-story mine. Everything after the first seven or so basic ideas is just implementation.

I have to wonder what you'd think of Anthony Burgess's epilogue to A Clockwork Orange, the one that the book's American publisher and Kubrick both chose to leave on the proverbial cutting-room floor. The one where Alex grew out of his rebel phase, got a job, and started a family. This epilogue reminds me of that, somehow. I could easily see Burgess's final chapter emerging fully-formed from an LLM in the not-too-distant future.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#300

Earlier quoted context omitted.

That hasn’t worked since about three months after companies found out people do it. It’s all astroturfing now days anyway and if it applies to products (which it for sure does) you can be sure that government actors caught on as well.

I'm not really sure what you're saying. It seems to me the number of shills creating content is vastly outnumbered by normal people creating content, so the trick of adding "reddit" to the end of queries is still very much useful (blackout protests aside) since it's usefulness derives from getting information from normal people and then having normal people upvote the "best" comments. Just the other day I tried this…

What I’m getting at is that those threads have a high likelihood of being gamed to make you think a certain way
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