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

garymarcus.substack.com

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

#91

Post by mountainriver »

Spooky23 wrote: post #55
mountainriver wrote: post #17

Earlier quoted context omitted.

I’ll take critiques from someone who knows what a test train split is. The idea that a guy so removed from machine learning has something relevant to say about its capabilities really speaks to the state of AI fear

The idea that practitioners would try to discredit research to protect the golden goose from critique speaks to human nature.

No one is discrediting research from valid places, this is the victim alt-right style narrative that seems to follow Gary Marcus around. Somehow the mainstream is "suppressing" the real knowledge

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

#92

Post by hintymad »

wohoef wrote: post #52

Good article giving some critique to Apple's paper and Gary Marcus specifically. https://www.lesswrong.com/posts/5uw26uDdFbFQgKzih/beware-gen...

Honest question: does the opinion of Gary Marcus still count? His criticism seems more philosophical than scientific. It's hard for me see what he builds or reasons to get to his conclusions.

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

#93

Post by mountainriver »

devwastaken wrote: post #22
mountainriver wrote: post #17

Earlier quoted context omitted.

I’ll take critiques from someone who knows what a test train split is. The idea that a guy so removed from machine learning has something relevant to say about its capabilities really speaks to the state of AI fear

experts are often blinded by their paychecks to see how nonsense their expertise is

Not knowing the most basic things about the subject you are critiquing is utter nonsense. Defending someone who does this is even worse

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

#94

Post by wongarsu »

neepi wrote: post #83
wongarsu wrote: post #71

Earlier quoted context omitted.

A lot of the best internet services came around in the decade after the dot-com crash. There is a chance Anthropic or OpenAI may not survive when funding suddenly dries up, but existing open weight models won't be majorly impacted. There will always be someone willing to host DeepSeek for you if you're willing to pay. And while it will be sad to see model improvements slow down when the bubble bursts there is a lot o…

Someone might host DeepSeek for you but you'll pay through the nose for it and it'll be frozen in time because the training cost doesn't have the revenue to keep the ball rolling. I'm not sure the GPU market won't collapse with it either. Possibly taking out a chunk of TSMC in the process, which will then have knock on effects across the whole industry.

There are already inference providers like DeepInfra or inference.net whose entire business model is hosted inference of open-source models. They promise not to keep or use any of the data and their business model has no scaling effects, so I assume they are already charging a fair market rate where the price covers the costs and returns a profit.

The GPU market will probably take a hit. But the flip side of that is that the market will be flooded with second-hand enterprise-grade GPUs. And if Nvidia needs sales from consumer GPUs again we might see more attractive prices and configurations there too. In the short term a market shock might be great for hobby-scale inference, and maybe even training (at the 7B scale). In the long term it will hurt, but if all else fails we still have AMD who are somehow barely invested in this AI boom

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

#95

Post by woopsn »

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.

If the claims about AI were that it is a great or even incredible chat app, there would be no mismatch.

I think normal people understand curing all disease, replacing all value, generating 100x stock market returns, uploading our minds etc to be hype.

I said a few days ago, LLM is amazing product. Sad that these people ruin their credibility immediately upon success.

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

#96

Post by thomasahle »

> 1. Humans have trouble with complex problems and memory demands. True! But incomplete. We have every right to expect machines to do things we can’t. [...] If we want to get to AGI, we will have to better.

I don't get this argument. The paper is about "whether RLLMs can think". If we grant "humans make these mistakes too", but also "we still require this ability in our definition of thinking", aren't we saying "thinking in humans is a illusion" too?

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

#97

Post by Zigurd »

Spooky23 wrote: post #58
soulofmischief wrote: post #32

Earlier quoted context omitted.

[citation needed]

Remember Web 3.0? Lol

It's unfortunate that a discussion about LLM weaknesses is giving crypto bro. But telling. There are a lot of bubble valuations out there.

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

#98

Post by senko »

2muchcoffeeman wrote: post #46
senko wrote: post #25

Earlier quoted context omitted.

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’s the argument here that he’s not considering all the information regarding GenAI? That there’s a trend to his opinion? If I consider all the evidence regarding gravity, all my papers will be “gravity is real”. In what ways is he only choosing what he wants to hear?

Replied elsewhere in the thread: https://news.ycombinator.com/item?id=44279283

To your example about gravity, I argue that he goes from "gravity is real" to "therefore we can't fly", and "yeah maybe some people can but that's not really solving gravity and they need to go down eventually!"

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

#99

Post by FINDarkside »

ummonk wrote: post #40

Most of the objections and their counterarguments seem like either poor objections (e.g. ad hominem against the first listed author) or seem to be subsumed under point 5. It’s annoying that most of this post focuses so much effort on discussing most of the other objections when the important discussion is the one to be had in point 5: I.e. to what extent are LLMs able to reliably make use of writing code or using log…

I don't think most of the objections are poor at all apart from 3, it's this article that seems to make lots of strawmans. Especially the first objection is often heard because people claim "this paper proves LLMs don't reason". The author moves goalposts and is arguing against about whether LLMs lead to AGI, which is already a strawman for those arguments. And in addition, he even seems to misunderstand AGI, thinking it's some sort of super intelligence ("We have every right to expect machines to do things we can’t"). AI that can do everything at least as good as average human is AGI by definition.

It's especially weird argument considering that LLMs are already ahead of humans in Tower of Hanoi. I bet average person will not be able to "one-shot" you the moves to 8 disk tower of Hanoi without writing anything down or tracking the state with the actual disks. LLMs have far bigger obstacles to reaching AGI though.

5 is also a massive strawman with the "not see how well it could use preexisting code retrieved from the web" as well, given that these models will write code to solve these kind of problems even if you come up with some new problem that wouldn't exist in its training data.

Most of these are just valid the issues in the paper. They're not supposed to be some kind of arguments that try to make everything the paper said invalid. The paper didn't really even make any bold claims, it only concluded LLMs have limitations in its reasoning. It had a catchy title and many people didn't read past that.

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

#100

Post by g-b-r »

bobxmax wrote: post #63
senko wrote: post #25

Earlier quoted context omitted.

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]

I see the opposite, the wide majority of people commenting on Hacker News seem now very favorable to LLMs.
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