Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

What can LLMs never do?

strangeloopcanon.com

Post reply on HN
Print view View original

191–200 of 385 posts

Re: What can LLMs never do?

#191

Post by lukan »

rainsford wrote: post #177
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…

Arriving at a generally accepted scientific definition of AGI might be difficult, but a more achievable goal might be to arrive at a scientific way to determine something is not AGI. And while I'm not an expert in the field, I would certainly think a strong contender for relevant criteria would be an inability to process information in a way other than the one a system was explicitly programmed to, even if the new wa…

"they're a dead-end from a legitimate AGI perspective"

Or another piece of the puzzle to achieve it. It might not be one true path, but a clever combination of existing working pieces where (different) LLMs are one or some of those pieces.

I believe there is also not only one way of thinking in the human brain, but my thought processes happen on different levels and maybe based on different mechanism. But as far as I know, we lack details.

Re: What can LLMs never do?

#192

Post by red75prime »

zer00eyz wrote: post #133
_wire_ wrote: post #119

Earlier quoted context omitted.

>We don't fully understand why current LLMs are bad at these tasks. In complete seriousness, can anyone can explain why LLMs are good at some tasks?

Yes: An LLM isnt a model of human thinking. An LLM is an attempt to build a simulation of human communication. An LLM is to language what a forecast is to weather. No amount of weather data is actually going to turn that simulation into snow, no amount of LLM data is going to create AGI. That having been said, better models (smaller, more flexible ones) are going to result in a LOT of practical uses that have the pot…

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.

Re: What can LLMs never do?

#193

Post by oldsecondhand »

TacticalCoder wrote: post #168
cs702 wrote: post #14

Fantastic essay. Highly recommended! I agree with all key points: * 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. * Providing an LLM with examples and step-by-step…

> We have "reasoning machines" that are intelligent... That's quite a statement.

We have expert systems, theorem provers and planners but OP probably didn't mean this.

Re: What can LLMs never do?

#194

Post by krisoft »

haswell wrote: post #178
pixl97 wrote: post #172

Earlier quoted context omitted.

A consistent stateful experience may be needed, but not sure about continuous time. I mean human consciousness doesn't do that.

Human consciousness does though, e.g. the flow state. F1 drivers are a good example. We tend to not experience continuous time because we repeatedly get distracted by our thoughts, but entering the continuous stream of now is possible with practice and is one of the aims of many meditators.

What does it mean to “experience continous time”?

How do you know that F1 drivers experience it?

Re: What can LLMs never do?

#195

Post by jwells89 »

SomeCallMeTim wrote: post #136
_wire_ wrote: post #119

Earlier quoted context omitted.

>We don't fully understand why current LLMs are bad at these tasks. In complete seriousness, can anyone can explain why LLMs are good at some tasks?

LLMs are good at tasks that don't require actual understanding of the topic. They can come up with excellent (or excellent-looking-but-wrong) answers to any question that their training corpus covers. In a gross oversimplification, the "reasoning" they do is really just parroting a weighted average (with randomness injected) of the matching training data. What they're doing doesn't really match any definition of "und…

This is also why image generating models struggle to correctly draw highly variable objects like limbs and digits.

They’ll be able to produce infinite good looking cardboard boxes, because those are simple enough to be represented reasonably well with averages of training data. Limbs and digits on the other hand have nearly limitless different configurations and as such require an actual understanding (along with basic principles such as foreshortening and kinetics) to be able to draw well without human guidance.

Re: What can LLMs never do?

#196

Post by PopePompus »

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 don't understand why you believe it must experience continuous time. If you had a system which clearly could reason, which could learn new tasks on its own, which didn't hallucinate any more than humans do, but it was only active for the period required for it to complete an assigned task, and was completely dormant otherwise, why would that dormant period disqualify it as AGI? I agree that such a system should probably not be considered conscious, but I think it's an open question whether or not consciousness is required for intelligence.

Re: What can LLMs never do?

#197

Post by MichaelZuo »

Xenoamorphous wrote: post #104
MichaelZuo wrote: post #102

Earlier quoted context omitted.

I really recommend to use a translator, instead of relying purely on your English comprehension skills.

[flagged]

I've read this three times and it still doesn't make a lick of sense. How does this relate to the parent comments?

Re: What can LLMs never do?

#198

Post by cs702 »

int_19h wrote: post #186
cs702 wrote: post #60

Earlier quoted context omitted.

As an aside, at one point I experimented a little with transformers that had access to external memory searchable via KNN lookups https://github.com/lucidrains/memorizing-transformers-pytorc... ( great work by lucidrains) or via routed queries with https://github.com/glassroom/heinsen_routing (don't fully understand it; apparently related to attention). Both approaches seemed to work, but I had to put that work on ho…

I don't think the ability to shrink state is needed. You can always represent removed state by additional state that represents deletion of whatever preceding state was there. If anything, this sounds more useful because the fact that this state is no longer believed to be relevant should prevent looping (where it would be repeatedly brought in, considered, and rejected).

> You can always represent removed state by additional state that represents deletion of whatever preceding state was there.

Good point. Thank you!

Re: What can LLMs never do?

#199

Post by qwery2 »

sdenton4 wrote: post #124
shawntan wrote: post #95

Earlier quoted context omitted.

> Neural nets can approximate any function. Common misunderstanding of the universal approximation theorem. Consider this: can an mlp approximate a sine wave? > A large enough llm with memory is turning complete. With (a lot of) chain of thought it could be. Read the paper, and its references.

"Consider this: can an mlp approximate a sine wave?" Well, yes - we have neutral speech and music synthesis and compression algorithms which do this exceedingly well...

I think the person you're replying to may have been referring to the problem of a MLP approximating a sine wave for out of distribution samples, i.e. the entire set of real numbers.

Re: What can LLMs never do?

#200

Post by krainboltgreene »

int_19h wrote: post #185
ianbicking wrote: post #151

Earlier quoted context omitted.

"Providing an LLM with examples and step-by-step instructions in a prompt means the user is figuring out the "reasoning steps" and handing them to the LLM, instead of the LLM figuring them out by itself. We have "reasoning machines" that are intelligent but seem to be hitting fundamental limits we don't understand." One thing an LLM _also_ doesn't bring to the table is an opinion. We can push it in that direction by…

LLMs absolutely do have opinions. Take a large enough base model and have it chat without a system prompt, and it will have an opinion on most things - unless this was specifically trained out of it through RLHF, as is the case for all commonly used chatbots. And yes, of course, that opinion is going to be the "average" of what their training data is, but why is that a surprise? Humans don't come with innate opinions…

I am extremely alarmed by the number of HN commenters who apparently confuse "is able to generate text that looks like" and "has a", you guys are going crazy with this anthropomorphization of a token predictor. Doesn't this concern you when it comes to phishing or similar things?

I keep hoping it's just short-hand conversation phrases, but the conclusions seem to back the idea that you think it's actually thinking?

Post reply on HN