231–240 of 331 posts
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This is not about finding the most effective solution, it’s about showing that they “understand” the problem. Could they write the algorithm if it were not in their training set?
If that's the point, shouldn't they ask the model to explain the principle for any number of discs? What's the benefit of a concrete application?
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I am not a slave to capital. I am a slave to the harsh nature of the world. I get too hot in summer and too cold in winter. I die of hunger. I am harassed by critters of all sorts. And when my bed breaks, to keep my fragile spine from straining at night, I _want_ some trees to be cut, some mattresses to be provisioned, some designers to be provisioned etc. And capital is what gets me that, from people I will never me…
Considering capitalism is a very new phenomenon in human history, how do you think people survived and thrived for the other 248000 years? It's as ludicrous to believe that capitalism is some kind of force of nature as it is to believe kings were chosen by god.
But the first civilizations in the world around 3000BC had trade, money, banking, capital accumulation, divison of labour etc.
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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!"
I’m not sure I buy your longer argument either.
I have a feeling the nay sayers are right on this. The next leap in AI isn’t something we’re going to recognise. (Obviously it’s possible - humans exist)
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I suspect human memory consists of a lot more bits than LLMs encode.
I rest my case — the question concerns a quality, not a quantity. These juvenile comparisons are mere excuses.
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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.
I think the second is interesting for comparing models, but not interesting for determining the limits of what models can automate in practice.
It's the prospect of automating labour which makes AI exciting and revolutionary, not their ability when arbitrarily restricted.
> 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…
Humans use tools to extend their abilities. LLM can do the same. In this paper they didn’t allow tool use. When others gave the tower of hanoi task to llms with tool use, like a python env, they were able to complete the task.
What happens when some novel Tower of Hanoi-esque puzzle is presented and there's nothing available in its training set to reference as an executable solution? A human can reason about and present a solution, but an LLM? Ehh...
But they definitely could and were [0]. You just employ multiple, and cross check - with the ability of every single one to also double check and correct errors.
LLMs cannot double check, and multiples won't really help (I suspect ultimately for the same reason - exponential multiplication of errors [1])
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Humans use tools to extend their abilities. LLM can do the same. In this paper they didn’t allow tool use. When others gave the tower of hanoi task to llms with tool use, like a python env, they were able to complete the task.
But the Tower of Hanoi can be solved without "tools" by humans, simply by understanding the problem, thinking about the solution, and writing it out. Having the LLM shell out to a Python example that it "wrote" (or rather, "pasted" since surely a Python solution to the Tower of Hanoi was part of its training set) is akin to a human Googling "program to solve Tower of Hanoi", copy-pasting and running the solution. Yes…
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But the Tower of Hanoi can be solved without "tools" by humans, simply by understanding the problem, thinking about the solution, and writing it out. Having the LLM shell out to a Python example that it "wrote" (or rather, "pasted" since surely a Python solution to the Tower of Hanoi was part of its training set) is akin to a human Googling "program to solve Tower of Hanoi", copy-pasting and running the solution. Yes…
LLMs are perfectly capable of writing code to solve problems that are not in their training set. I ask LLMs to write code for niche problems that you won't find answers to just by Googling all the time. The LLMs usually get it right.
Examples of these problems? You'll probably find that they're simply compositions of things already in the training set. For example, you might think that "here's a class containing an ID field and foobar field. Make a linked list class that stores inserted items in reverse foobar order with the ID field breaking ties" is something "not in" the training set, but it's really just a composition of the "make a linked list class" and "sort these things based on a field" problems.
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> It puts LLMs in an impossible position; if they are right, they memorized it, if they are wrong, they cannot reason. Both of those can be true at the same time though. They memorize a lot of things, but its fuzzy and when they remember wrong they cannot fix it via reasoning.
It's more than fuzzy, they are packing exabytes, perhaps zetabytes of training data into a few terabytes. Without any reasoning ability it must be divine intervention that they ever get anything right...
We have LLMs that can produce copious text but cannot stop themselves from attempting to solve a problem they have no idea how to solve and making a mess of things as a result. This puts an LLM on the level of an overly enthusiastic toddler at best.