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What can LLMs never do?

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Re: What can LLMs never do?

#231

Post by Wohlf »

stainablesteel wrote: post #209
Daub wrote: post #206

>What can LLMs never do? Produce convincingly bad digital paintings. I teach digital painting. Some of the students have incorporated AI into their working process, which I support. Others have tried to cheat by simply copying AI generated output. Such cases are super-easy to spot: they carry the visual signature of AI art (which are mostly scrappings from artstation). This visual signature seems impossible to overri…

that's an interesting statement, i've always considered experts to be people who can do things simultaneously better and worse than a beginner my favorite example is when a great dancer who tries to mockingly mimic a bad dancer still looks good and does it hilariously

Many experts completly forget what it was like to be a beginner. That's why I've found it's generally best for absolute beginners to learn from an apprentice, and an apprentice to learn from a journeyman, because they still remember what it was like to be at the previous level.

Re: What can LLMs never do?

#232

Post by ziml77 »

aurareturn wrote: post #76
donatj wrote: post #62

I have been trying to generate some text recently using the ChatGPT API. No matter how I word “Include any interesting facts or anecdotes without commenting on the fact being interesting” it ALWAYS starts out “One interesting fact about” or similar phrasing. I have honestly spent multiple hours trying to word the prompt so it will stop including introductory phrases and just include the fact straight. I have gone so…

Not an expert but I sense that it's following a higher OpenAI "built in" prompt that asks it to always include an introductory phrase. Hence, we do need powerful and less censored LLMs if we want to better integrate LLMs into applications.

No it just seems that it becomes blind, so to speak, to the negatives and the inclusion of the words you were negating makes it more likely to apply them in the positive. This is how ChatGPT has seemed to behave whenever I've tried to get it to not include something.

Re: What can LLMs never do?

#233

Post by adrianN »

jncfhnb wrote: post #131
dragonwriter wrote: post #120

Earlier quoted context omitted.

> There are problems that are easy for human beings but hard for current LLMs (and maybe impossible for them; no one knows). Examples include playing Wordle and predicting cellular automata (including Turing-complete ones like Rule 110). We don’t fully understand why current LLMs are bad at these tasks. I thought we did know for things like playing Wordle, that its because they deal with words as sequence of tokens t…

I think one should feel comfortable arguing that AGI must be stateful and experience continuous time at least. Such that a plain old LLM is definitively not ever going to be AGI; but an LLM called in a do while true for loop might.

I try my best not to experience continuous time for at least eight hours a day.

Re: What can LLMs never do?

#234

Post by kevindamm »

yosito wrote: post #219

> If there exist classes of problems that someone in an elementary school can easily solve but a trillion-token billion-dollar sophisticated model cannot solve, what does that tell us about the nature of our cognition? I think what it tells us is that our cognition is capable of more than just language modeling. With LLMs we are discovering (amazing) capabilities and the limits of language models. While language mode…

What about sudoku makes it a good fit for CNNs? Or do you mean the machine vision for converting the pixels into an awareness of the sudoku puzzle's initial conditions?

A relatively simple graph theory algorithm can solve it (and at multiple orders of magnitude fewer calculations). Even a naive brute force search is considered tractable, considering the problem size. Although, search could be considered one of the AI tools in your proposed toolbox.

Re: What can LLMs never do?

#235

Post by sdenton4 »

ryanblakeley wrote: post #222
sdenton4 wrote: post #123

"The models, in other words, do not well generalise to understand the relationships between people." Curiously, the need to do this well - handling the quadratic complexity of a shifting set of human relationships, grudges, and alliances - is thought to be one of the things that led is to higher levels of intelligence.

Just to be clear, these models can answer questions about relationships between people if you mean family relationships. Answering questions about what you're describing sounds really interesting. What would a training set be like that describes a bunch of complex human relationships and then asks questions about them with objective answers?

Of course, it would be easy to put such questions together, and I'm sure the LLM would do fine with them - there's a massive amount of human text about human relationships.

One difference, as in all ml training, is interactivity. Looking at ape studies, knowing the relationships is partly diagnostic, but it's also about planning and competition. And that competitive/adaptive aspect is what is what looks like a real evolutionary driver. If you can understand, navigate, and manipulate relationships successfully, you get more mating opportunities. Doing /that/ well involves both reasoning and long term planning, both of which are apparent in chimps.

A good book on this topic is 'are we smart enough to understand how smart animals are' by Frans de Waal.

https://wwnorton.com/books/Are-We-Smart-Enough-to-Know-How-S...

Re: What can LLMs never do?

#236

Post by adrianN »

xanderlewis wrote: post #203
danenania wrote: post #149

Earlier quoted context omitted.

> the "reasoning" they do is really just parroting a weighted average (with randomness injected) of the matching training data Perhaps our brains are doing exactly the same, just with more sophistication?

Every single discussion of ‘AGI’ has endless comments exactly like this. Whatever criticism is made of an attempt to produce a reasoning machine, there’s always inevitably someone who says ‘but that’s just what our brains do, duhhh… stop trying to feel special’. It’s boring, and it’s also completely content-free. This particular instance doesn’t even make sense: how can it be exactly the same , yet more sophisticated…

The problem is that we currently lack good definitions for crucial words such as "understanding" and we don't know how brains work, so that nobody can objectively tell whether a spreadsheet "understands" anything better than our brains. That makes these kinds of discussions quite unproductive.

Re: What can LLMs never do?

#237

Post by adrianN »

zer00eyz wrote: post #210
red75prime wrote: post #192

Earlier quoted context omitted.

Ugh. Really? Those "simulated water isn't wet"(when applied to cognition) "arguments" were punched so many times it even hurts to look at them.

No simulated water isnt wet. But an LLM isn't even trying to simulate cognition. It's a model that is predicting language. It has all the problems of a predictive model... the "hallucination" problem is just the tyranny of Lorenz.

We don't really know what "cognition" is, so it's hard to tell whether a system is doing it.

Re: What can LLMs never do?

#238

Post by anon-3988 »

This is a chicken and egg problem, of course we only value and optimize for what we can do and deem anything that we can't do as unnecessary. There are things that we human simply cannot think of therefore it must not be important or does not exist.

We cannot think of anything beyond 4 dimension, so therefore there must be nothing beyond that or that things that exist in those dimension doesn't matter that much. Or more precisely, we simply cannot appreciate those things.

If we are simply trying to mimic human intelligence...well, you are going to end up with a human brain.

Suppose we have a concept X that humans simply cannot comprehend, appreciate or solve, well, why bother create an intelligence to solve that?

From this hypothesis, I personally think that any intelligence that we create will simply be an augmentation of what human desire. That is, there will always be a human part in the cog because human is the only thing can appreciate what is being created so any and all output must cater to the human involved. This will inevitably happen because we want whatever it is the human brain is doing, without doing whatever it is that the human brain is doing.

That is until we unleash a different intelligence system with agency.

Re: What can LLMs never do?

#239

Post by kromem »

fragmede wrote: post #161
kromem wrote: post #140

LLMs can't is such an anti-pattern at this point I'm surprised that anyone still dares to stake it. The piece even has an example of a $10k bet around a can't being proven false in under a day, but somehow doesn't think maybe their own can't examples are on similarly thin ice? In particular, the line about "what models can't do tells us what they don't know" is infuriating. No, that's not the case at all. At least in…

interestingly enough, it seems that ChatGPT-4 can now detect the variations of the classic logic puzzle and solve it, so that can't is now a couldn't .

We may be talking about different logic puzzles? The only model I've seen that didn't need some rather extreme adjustments to eventually solve it was Mistral large.

Re: What can LLMs never do?

#240

Post by naasking »

jncfhnb wrote: post #131
dragonwriter wrote: post #120

Earlier quoted context omitted.

> There are problems that are easy for human beings but hard for current LLMs (and maybe impossible for them; no one knows). Examples include playing Wordle and predicting cellular automata (including Turing-complete ones like Rule 110). We don’t fully understand why current LLMs are bad at these tasks. I thought we did know for things like playing Wordle, that its because they deal with words as sequence of tokens t…

I think one should feel comfortable arguing that AGI must be stateful and experience continuous time at least. Such that a plain old LLM is definitively not ever going to be AGI; but an LLM called in a do while true for loop might.

Some good prompt-reply interactions are probably fed back in to subsequent training runs, so they're still stateful/have memory in a way, there's just a long delay.
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