Seven replies to the viral Apple reasoning paper and why they fall short • Hacker News Board

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#61

Post by aucisson_masque » Sat, Jun 14, 2025, 10:03 PM UTC

labrador wrote: post #31

The key insight is that LLMs can 'reason' when they've seen similar solutions in training data, but this breaks down on truly novel problems. This isn't reasoning exactly, but close enough to be useful in many circumstances. Repeating solutions on demand can be handy, just like repeating facts on demand is handy. Marcus gets this right technically but focuses too much on emotional arguments rather than clear explanat…

That’s the opposite of reasoning tho. Ai bros want to make people believe LLM are smart but they’re not capable of intelligence and reasoning.

Reasoning mean you can take on a problem you’ve never seen before and think of innovative ways to solve it.

LLM can only replicate what is in its data, it can in no way think or guess or estimate what will likely be the best solution, it can only output a solution based on a probability calculation made on how frequent it has seen this solution linked to this problem.

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#62

Post by bobxmax » Sat, Jun 14, 2025, 10:04 PM UTC

mentalgear wrote: post #29

AI hype-bros like to complain that real AI experts are too much concerned about debunking current AI then improving it - but the truth is that debunking bad AI IS improving AI. Science is a process of trial and error which only works by continuously questioning the current state.

> AI hype-bros like to complain that real AI experts are too much concerned about debunking current AI then improving it

You're acting like this is a common ocurrence lol

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#63

Post by bobxmax » Sat, Jun 14, 2025, 10:05 PM UTC

senko wrote: post #25

bluefirebrand wrote: post #2

I'm glad to read articles like this one, because I think it is important that we pour some water on the hype cycle If we want to get serious about using these new AI tools then we need to come out of the clouds and get real about their capabilities Are they impressive? Sure. Useful? Yes probably in a lot of cases But we cannot continue the hype this way, it doesn't serve anyone except the people who are financially i…

Gary Marcus isn't about "getting real", it's making a name for himself as a contrarian to the popular AI narrative. This article may seem reasonable, but here he's defending a paper that in his previous article he called "A knockout blow for LLMs". Many of his articles seem reasonable (if a bit off) until you read a couple dozen a spot a trend.

[flagged]

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#64

Post by lossolo » Sat, Jun 14, 2025, 10:07 PM UTC

labrador wrote: post #54

lossolo wrote: post #45

Earlier quoted context omitted.

They can't create anything novel and it's patently obvious if you understand how they're implemented. But I'm just some anonymous guy on HN, so maybe this time I will just cite the opinion of the DeepMind CEO, who said in a recent interview with The Verge (available on YouTube) that LLMs based on transformers can't create anything truly novel.

"I don't think today's systems can invent, you know, do true invention, true creativity, hypothesize new scientific theories. They're extremely useful, they're impressive, but they have holes." Demis Hassabis On The Future of Work in the Age of AI (@ 2:30 mark) https://www.youtube.com/watch?v=CRraHg4Ks_g

Yes, this one. Thanks

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#65

Post by travisgriggs » Sat, Jun 14, 2025, 10:07 PM UTC

DiogenesKynikos wrote: post #28

bluefirebrand wrote: post #2

I'm glad to read articles like this one, because I think it is important that we pour some water on the hype cycle If we want to get serious about using these new AI tools then we need to come out of the clouds and get real about their capabilities Are they impressive? Sure. Useful? Yes probably in a lot of cases But we cannot continue the hype this way, it doesn't serve anyone except the people who are financially i…

I don't understand what people mean when they say that AI is being hyped. AI is at the point where you can have a conversation with it about almost anything, and it will answer more intelligently than 90% of people. That's incredibly impressive, and normal people don't need to be sold on it. They're just naturally impressed by it.

I get even better results talking to myself.

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#66

Post by avsteele » Sat, Jun 14, 2025, 10:07 PM UTC

foldr wrote: post #37

avsteele wrote: post #21

Earlier quoted context omitted.

Papers make specific conclusions based on specific data. The paper I linked specifically rebuts the conclusions of the paper. Gary makes vague statements that could be interpreted as being related. It is scientific malpractice to write a post supposedly rebutting responses to a paper and not directly address the most salient one.

This sort of omission would not be considered scientific malpractice even in a journal article, let alone a blog post. A rebuttal of a position that fails to address the strongest arguments for it is a bad rebuttal, but it’s not scientific malpractice to write a bad paper — let alone a bad blog post. I don’t think I agree with you that GM isn’t addressing the points in the paper you link. But in any case, you’re not…

Malpractice slightly hyperbolic.

But anybody relying on Gary's posts in order to be be informed on this subject is being being mislead. This isn't an isolated incident either.

People need to be made be aware when you read him it is mere punditry, not substantive engagement with the literature.

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#67

Post by aucisson_masque » Sat, Jun 14, 2025, 10:08 PM UTC

Jabrov wrote: post #35

labrador wrote: post #31

The key insight is that LLMs can 'reason' when they've seen similar solutions in training data, but this breaks down on truly novel problems. This isn't reasoning exactly, but close enough to be useful in many circumstances. Repeating solutions on demand can be handy, just like repeating facts on demand is handy. Marcus gets this right technically but focuses too much on emotional arguments rather than clear explanat…

I’m so tired of hearing this be repeated, like the whole “LLMs are _just_ parrots” thing. It’s patently obvious to me that LLMs can reason and solve novel problems not in their training data. You can test this out in so many ways, and there’s so many examples out there. ______________ Edit for responders, instead of replying to each: We obviously have to define what we mean by "reasoning" and "solving novel problems"…

> It’s patently obvious that LLMs can reason and solve novel problems not in their training data.

Would you care to tell us more ?

« It’s patently obvious » is not really an argument, I could say just as well that everyone know LLM can’t resonate or think (in the way we living beings do).

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#68

Post by [deleted] » Sat, Jun 14, 2025, 10:08 PM UTC

Jabrov wrote: post #35

labrador wrote: post #31

The key insight is that LLMs can 'reason' when they've seen similar solutions in training data, but this breaks down on truly novel problems. This isn't reasoning exactly, but close enough to be useful in many circumstances. Repeating solutions on demand can be handy, just like repeating facts on demand is handy. Marcus gets this right technically but focuses too much on emotional arguments rather than clear explanat…

I’m so tired of hearing this be repeated, like the whole “LLMs are _just_ parrots” thing. It’s patently obvious to me that LLMs can reason and solve novel problems not in their training data. You can test this out in so many ways, and there’s so many examples out there. ______________ Edit for responders, instead of replying to each: We obviously have to define what we mean by "reasoning" and "solving novel problems"…

[deleted]

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#69

Post by newswasboring » Sat, Jun 14, 2025, 10:08 PM UTC

senko wrote: post #25

bluefirebrand wrote: post #2

I'm glad to read articles like this one, because I think it is important that we pour some water on the hype cycle If we want to get serious about using these new AI tools then we need to come out of the clouds and get real about their capabilities Are they impressive? Sure. Useful? Yes probably in a lot of cases But we cannot continue the hype this way, it doesn't serve anyone except the people who are financially i…

Gary Marcus isn't about "getting real", it's making a name for himself as a contrarian to the popular AI narrative. This article may seem reasonable, but here he's defending a paper that in his previous article he called "A knockout blow for LLMs". Many of his articles seem reasonable (if a bit off) until you read a couple dozen a spot a trend.

What exactly is your objection here? That the guy has an opinion and is writing about it?

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#70

Post by andrewmcwatters » Sat, Jun 14, 2025, 10:09 PM UTC

Jabrov wrote: post #35

labrador wrote: post #31

The key insight is that LLMs can 'reason' when they've seen similar solutions in training data, but this breaks down on truly novel problems. This isn't reasoning exactly, but close enough to be useful in many circumstances. Repeating solutions on demand can be handy, just like repeating facts on demand is handy. Marcus gets this right technically but focuses too much on emotional arguments rather than clear explanat…

I’m so tired of hearing this be repeated, like the whole “LLMs are _just_ parrots” thing. It’s patently obvious to me that LLMs can reason and solve novel problems not in their training data. You can test this out in so many ways, and there’s so many examples out there. ______________ Edit for responders, instead of replying to each: We obviously have to define what we mean by "reasoning" and "solving novel problems"…

It's definitely not true in any meaningful sense. There are plenty of us practitioners in software engineering wishing it was true, because if it was, we'd all have genius interns working for us on Mac Studios at home.

It's not true. It's plainly not true. Go have any of these models, paid, or local try to build you novel solutions to hard, existing problems despite being, in some cases, trained on literally the entire compendium of open knowledge in not just one, but multiple adjacent fields. Not to mention the fact that being able to abstract general knowledge would mean it would be able to reason.

They. Cannot. Do it.

I have no idea what you people are talking about because you cannot be working on anything with real substance that hasn't been perfectly line fit to your abundantly worked on problems, but no, these models are obviously not reasoning.

I built a digital employee and gave it menial tasks that compare to current cloud solutions who also claim to be able to provide you paid cloud AI employees and these things are stupider than fresh college grads.