Hacker News Reader

Comment by superxpro12 | Hacker News Reader

It cant "invent new things". It can only reuse what it already knows about.

Thats why the math breakthrough a few weeks ago was so hotly debated. Because OpenAI is desperate to demonstrate that AI isnt just a fancy regurgitation machine, but it can actually develop novel thought. Because that would be the stock price jumps to end all stock jumps.

But then it turned out it was really just listening in on a math professors supposed-to-be-private conversations with another instance of openai, and it used his novel work as the trigger to prove the breakthrough first.

The reason people conclude AI 'thinks' is because tt can reference obscure or poorly documented things quickly (which is its primary advantage along with processing natural language prompts into tasks), which is why a lot of people with emotions confuse that action with inventing things.

View on HN

> It cant "invent new things"

I've heard that before but it just doesn't make sense in context of what I've seen llms do. If I ask an llm to write a poem about magnetic resonance and vampire rabbits it can do that it created a new thing. I can ask it to build a website for managing rabbit breeding that's also a new thing.

Another way of looking at it is that human beings, just like llms, can produce output based on their inputs. Most literature is inspired by other literature. Most music is inspired by other music. Most software is inspired by other software.

So I think we need to work on defining " new things" before we can definitely exclude them from llm's capabilities

I'm not sure how you'd land on an LLM being unable to invent anything new. You'd have to both understand exactly what LLMs are and aren't capable of today and what allows human creativity. I don't think there are good answers to either.

It seems unlikely that recursively predicting the next word would lead to creativity or invention, but it doesn't seem impossible. Similarly, it seems unlikely that human thought works in a similar prediction loop, but it doesn't seem impossible.

Human ingenuity is creative by definition, that's where the concept arises. The burden of proof is on AI labs to prove that their models are rising to the level of actual synthesis, rather than very impressive 200-level regurgitation that looks like synthesis if you squint a bit; because the model has access to more raw data than your average sophomore student.

> Human ingenuity is creative by definition, that's where the concept arises

Sure, but until we can turn that tautology into something more rigorous, we can't tell if the thing humans do is more or less than what some arbitrary non-human (machine, animal, or eventually perhaps alien) does.

Its not a logical tautology, we just don't have other examples. We know that even humans have to clear a well defined bar to synthesize new information. The challenge is actually proving that the LLM isn't merely surfacing obscure data. The mechanism is interesting, but not actually necessary for this exercise.

Its tautological because you defined it by human ingenuity, and that's only known based on us knowing what its like to be human. Of course another species has yet to seem from the outside like they present human ingenuity or experience life similar to us.

That has nothing to do with creativity as its own concept. Its creative to invent flight similar to the Wright brothers. Its also creative to use a stick to get termites from their nest or come up with new techniques for hunting pray.

That goes in circles though.

Ignore the fact that we are human and thing our ingenuity is unique. Is definitionally linking human ingenuity and creativity useful when trying to compare to others?

I could say earth is habitable so any other planet must prove it is similar to earth to be habitable. That simply doesn't matter, though, if different environments could lead to similarly complex living systems regardless of atmospheric makeup, precense of liquid water, etc.

I'd argue the burden is on you to prove that the definition you propose is meaningful beyond our own hubris, rather than expecting an alternative to now have to prove that the hubris was unjustified.

I just don’t know enough to agree with you, but I would argue, probably poorly, that an AI or collection of agents could “invent things” simply by virtue of trying essentially everything in a reasonable bound and stumbling into a solution.

Call that brute force perhaps, but I would consider it technically inventing something on the merit that it would at least be an abstraction above naively throwing everything against a wall to only throwing things that would most likely be sticky.

Nature brute forces a ton of problems. Hell, humans also do it by having 1 to 8 billion copies of ourselves around too. Even then we are the most guilty species of "copying someone else's work" right behind viruses. We copy things nature has figured out by brute force, then use slightly more targeted methods of forcing to see if it creates new things.

> that an AI or collection of agents could “invent things” simply by virtue of trying essentially everything in a reasonable bound and stumbling into a solution

on a surface level a human solving an (unsolved) math problem can look like this, and of course the tree of all possible symbols you can send to a proving assistant is much wider than what the human samples, and the same goes true for an LLM in a proving loop. It isn't "truly random", it can't possibly be (and solve the problem). Both humans and LLMs solving unsolved math problems are aggressively pruning mathematical syntax and logical strategy trees.

"AI" is a token generator, it does not think while the human brain is a lot more complex with a lot more sensory inputs.

> the human brain is a lot more complex with a lot more sensory inputs

How are you measuring complexity here? How are you measuring inputs? Your average llm is trained on a corpus that vastly exceeds the amount of data I could read in my lifetime.

>How are you measuring complexity here?

In terms of how it functions, because no matter how much data you feed an LLM it's still predicting tokens. That makes it incapable of any thought.

>Your average llm is trained on a corpus that vastly exceeds the amount of data I could read in my lifetime.

But that doesn't mean they are useless, they are good at consuming large amounts of data and collating it.

I don't know how correct I am but that's my understanding and it won't change, I feel pretty confident in my simplified view of things because the basics are still there.

» an LLM it's still predicting tokens

That's a rather dismissive way of putting it. Moreover it's confusing the output format with the complexity of the output. I could just as easily say that no matter what the human brain does it's just generating stimulus to motor neurons. Such a simplification is just as wrong as saying that it's just as misleading is saying that an llm is just a token generator. What makes an llm able or unable to be complex is the process that creates those tokens

Complexity != breadth. The human brain is MASSIVELY more complex than any LLM!

> Complexity != breadth.

Okay but how is that relevant for measuring whether an llm is capable of creating original thoughts? Why do you think that complexity, as you say, is a more relevant Factor than simply the number of weights?

> "AI" is a token generator, it does not think

2024 called, it wants its talking points back. I don't think claims like these are defensible after all the progress we have witnessed in the last year alone.

That's quite literally how an LLM works though? That's like scoffing at people calling computers binary generators. Or am I not getting with the programme enough and it emits pixie dust and rainbows?

Yes, LLMs emit tokens. And human brains emit nerve impulses. Both devices are "trained" on corpora of data.

That has nothing to do with the question of whether the mechanism that generates those signals is complex enough to say that it's "creative."

Okay, please educate me on this progress and how LLMs are different to generating the next most statistically probable token.

How about _you_ educate us how _you_ are any different than generating the most statistically probable token?

Lol

a human has motivation and agency.

An "AI" is a box which lies dormant until a human, with motivation and agency, enters a prompt into it.

A person or company can use it in a way where it might invent something. but at the end of the day, its a tool, and its actually not doing anything on its own.

Its not solving math problems, a mathetmatician is using it to solve math problems. Just cus OpenAI is acting like its AI is solving stuff, its really not. They're just paying people to use AI to hammer problems.

If my boss tells me to fix a bug in our database, and I fix it, is it really my boss who fixed it, since he has the agency and I'm just a tool he uses?

I'd say that people's motivation and agency, like their taste, creativity, and opinions, are shaped by external inputs, so saying that their agency is "owned" by them is a stretch.

The alternative would be the proposition that a slave, lacking agency, has no ability to solve problems. I find that idea unpalatable.

> then it turned out it was really just listening in on a math professors supposed-to-be-private conversations

No, it didn't turn out to be that. Someone made a claim, which is silly for many reasons. There's no serious support for this happening.

> it was really just listening in on a math professors supposed-to-be-private conversations with another instance of openai, and it used his novel work as the trigger to prove the breakthrough first.

This didn't actually happen.

> Following an investigation, we have confirmed that Buckmaster’s Codex prompts over the two months preceding this announcement and paper on September 8, 2026, could not have influenced the system in any way, including through training.

https://openai.com/index/navier-stokes-solution/

You're overconfident in what you're saying. Nobody really knows (yet) if LLMs can or cannot "think" or can or cannot "invent" new things. I'm inclined to think the opposite from you but smart people actually try not to hold the absolut stances and present them as facts when they're in fact not. What I have seen so far throughout a daily use on complicated things suggests that humanity developed a new form of an intelligence.

i think the proper understanding of LLM based AI is "contexting"; we describe the world we want them to fill, we build/direct the outside context for them to understand, then "they" take off from there.

If we do a bad job building context, they do a horrible job contexting. People who have trouble working with AI have the same problem people have in general: if they can't figure out the context of the direction, then they make random decisions of doing anything. On the flip side, if you can build the proper context around a sufficiently powerful LLM, they can derive the context via the contexting they're good at.

This is why building documents, tests, and code all in some intent pattern via prompting allows them to do a significant amount of work a normal person would have a great effort t

>It cant "invent new things". It can only reuse what it already knows about.

This is outdated. With RLVF, LLMs can create their own training data instead of relying on what it's been fed.

and that training data will not be a "new thing", because AI can't invent new things. Just recycling of what it already "knows".

Yes it is. Do you not know how RLVF works? The AI tries on a task, comes up with a potential solution, and is trained based on whether the solution is valid or not. If proving a previously unproven theorem and then using that information to solve even more theorems doesn't count as creating new information, then literally everything a mathematician does is just recycling what they've been taught.

Yes, but that process doesn't bring anything new, just permutation of what's been in the training data.

> and that training data will not be a "new thing", because AI can't invent new things. Just recycling of what it already "knows".

Regardless of the truth of the underlying assertion, this is about as textbook an example of circular logic as it gets. (at least combined with the implicit beginning assertion that "AI can't invent new things.")

You’re assuming the “listening in” aspect, which as far as I know is not proven (even in the less loaded “the model was trained on sessions including the ones in question” form of the claim.

Plausible? Absolutely. Did OpenAI behave badly in other ways regarding this issue? Yes. Does it help to assume unproven facts and then accuse people of reaching emotional decisions? Nope.

Maybe the conditions of the singularity are not going to be recognized, at first, for having produced new original decisions, and thus thought.

Perhaps it is going to be more like, we will see the singularity predict the future.

While “the model was trained on sessions including the ones in question” is one aspect; ‘the model produced an accurate prediction of future human thought’, I think, is another very interesting facet.

Models predicting future things might be how we see, actually, how we ourselves formulate thought - by saying, in big and small words, ‘something is about to happen’.

>unproven facts

This isn’t a court room. We’re discussing ways by which we humans both succeed and fail at reigning in our creations. The OpenAI kerfuffle is pretty much irrelevant already. Of course AI will fill in the gaps of human thought - it is literally constructed from the stuff, in every squeeze of the curd and whey.

It's 2026. "Stochastic parrot" has been dead and buried some 2 years ago.

Reply on news.ycombinator.com