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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

#101

Post by bandrami »

js8 wrote: post #85
Gigachad wrote: post #60

Earlier quoted context omitted.

And then the person on the end is using AI to summarise the email back to normal English. To what end?

But look the GDP has increased!

But that's what I don't get: it hasn't in that scenario because that doesn't lead to a greater circulation of money at any point. And that's the big thing I'm looking for: something AI has created that consumers are willing to pay for. Because if that doesn't end up happening no amount of sunk investment is going to save the ecosystem.

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

#102

Post by thomasahle »

> 5. A student might complain about a math exam requiring integration or differentiation by hand, even though math software can produce the correct answer instantly. The teacher’s goal in assigning the problem, though, isn’t finding the answer to that question (presumably the teacher already know the answer), but to assess the student’s conceptual understanding. Do LLM’s conceptually understand Hanoi? That’s what the Apple team was getting at. (Can LLMs download the right code? Sure. But downloading code without conceptual understanding is of less help in the case of new problems, dynamically changing environments, and so on.)

Why is he talking about "downloading" code? The LLMs can easily "write" out out the code themselves.

If the student wrote a software program for general differentiation during the exam, they obviously would have a great conceptual understanding.

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

#103

Post by aeronaut80 »

bluefirebrand wrote: post #20
aeronaut80 wrote: post #11

Earlier quoted context omitted.

You probably can’t find a good source because sources say he has a negligible stake in OpenAI. https://www.cnbc.com/amp/2024/12/10/billionaire-sam-altman-d...

Interesting When I did a cursory search, this information didn't turn up either Thanks for correcting me. I suppose the stuff I saw the other day was just BS then

To be fair I struggle to believe he’s doing it out of the goodness of his heart.

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

#104

Post by autobodie »

thomasahle wrote: post #96

> 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 "thin…

Agree. Both sides of the argument are unsatisfying. They seem like quantitative answers to a qualitative question.

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

#105

Post by autobodie »

thomasahle wrote: post #102

> 5. A student might complain about a math exam requiring integration or differentiation by hand, even though math software can produce the correct answer instantly. The teacher’s goal in assigning the problem, though, isn’t finding the answer to that question (presumably the teacher already know the answer), but to assess the student’s conceptual understanding. Do LLM’s conceptually understand Hanoi? That’s what the…

If the student could reference notes a fraction of the size of the LLM then I would not be convinced.

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

#106

Post by labrador »

Jabrov wrote: post #88
labrador wrote: post #74

Earlier quoted context omitted.

You're assuming we're saying LLMs can't reason. That's not what we're saying. They can execute reasoning-like processes when they've seen similar patterns, but this breaks down when true novel reasoning is required. Most people do the same thing. Some poeple can come up with novel solutions to new problems, but LLMs will choke. Here's an example: Prompt: "Let's try a reasoning test. Estimate how many pianos there are…

That seems like a totally reasonable response ... ?

I think you missed the part where I had to give them hinits to solve it. All 3 initially couldn't or refused saying it was not a real problem on their first try.

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

#107

Post by zer00eyz »

hintymad wrote: post #92
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.

> seems more philosophical than scientific

I think this is a fair assessment but reason, and intelligence dont really have an established control or control group. If you build a test and say "Its not intelligent because it can't..." and someone goes out and add's that feature in is it suddenly now intelligent?

If we make a physics break through tomorrow is there any LLM that is going to retain that knowledge permanently as part of its core or will they all need to be re-trained? Can we make a model that is as smart as a 5th grader without shoving the whole corpus of human knowledge into it, folding it over twice and then training it back out?

The current crop of tech doesn't get us to AGI. And the focus to make it "better" is for the most part a fools errand. The real winners in this race are going to be those who hold the keys to optimization: short retraining times, smaller models (with less upfront data), optimized for lower performance systems.

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

#108

Post by FINDarkside »

thomasahle wrote: post #96

> 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 "thin…

Agreed. But also his point about AGI is incorrect. AI that will perform on the level of average human in every task is AGI by definition.

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

#109

Post by exe34 »

autobodie wrote: post #105
thomasahle wrote: post #102

> 5. A student might complain about a math exam requiring integration or differentiation by hand, even though math software can produce the correct answer instantly. The teacher’s goal in assigning the problem, though, isn’t finding the answer to that question (presumably the teacher already know the answer), but to assess the student’s conceptual understanding. Do LLM’s conceptually understand Hanoi? That’s what the…

If the student could reference notes a fraction of the size of the LLM then I would not be convinced.

I suspect human memory consists of a lot more bits than LLMs encode.

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

#110

Post by charcircuit »

barrkel wrote: post #16
jakewins wrote: post #9

Earlier quoted context omitted.

I thought this article seemed like well articulated criticism of the hype cycle - can you be more specific what you mean? Are the results in the Apple paper incorrect?

You need to read everything that Gary writes with the particular axe to grind he has in mind: neurosymbolic AI. That's his specialism, and he essentially has a chip in his shoulder about the attention probabilistic approaches like LLMs are getting, and their relative success. You can see this in this article too. The real question you should be asking is if there is a practical limitation in LLMs and LRMs revealed by…

>with tool use

A LLM with tool use can solve anything. It is interesting to try and measure its capabilities without tools.

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