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Another paper in a long series that confuses "our tests against currently available LLMs tuned for specific tasks found that they didn't perform well on our task" with "LLMs are architecturally unsuitable for our task".
There is no reason to believe (evidence) that any meaning ascribed to an LLM's utterances comes from the LLM rather than being pareidolia. If you've found some, please let everyone know.
Earlier quoted context omitted.
> what does this mean? I don't know, take your own conclusions, I tried what I tried with the results I got. And the reason I created a Monte Carlo Engine to play the game was specifically because of this, I expected ChatGPT to be able to make moves but actually not being good with the game. You can try yourself, the code is available. > Also in this game if I don't move the queen I force a draw, right? I don't know…
>I don't know, take your own conclusions The API cost of the game is getting noticeable, but I think you were just being naive about LLM limitations, there is simply no way that it can answer all questions simply by memorization. A simpler way is to just invent a programming language and ask the model to solve problems with it, at least I don't have to write down the position of a game ahah Also I have trained models…
My exact entire point was that it would be really surprising that chatgt only knowing chess rules could play at 1400 ELO level.
And I did not give a crap about how to play a single game and was not meant to see how smart HN users are but how chatGPT is, but whatever conclusion a human would take without playing a single game (not trying to move the queeen) is what chatgpt should do, right?
BTW, I simulated few games of the engine against the engine to see what happened and it does not seem as obvious as you say.
No LLMs don't think like people, they're architecturally incapable of doing so. They have, physically unlike humans no access to their own internal state and they're, save for a small context window, static systems. They also have no insights. There's a hilarious video about LLM Jailbreaks by Karpathy[1] from a week ago, where he shows how you can break model responses by asking the same question with a base64 string…
I was right there with you until you mentioned Searle. :-) The Chinese room argument is bad in that it hides an assumption of mind/body dualism. If you believe that humans have "souls" and other things do not, then you have a qualitative difference between a human or a machine. On the other hand, if you are a materialist then you are faced with the problem that humans don't have much understanding of semantics either…
It's because our bandwith and monkey brains are so slow that we're forced to operate at a level of semantics. We can't just make inferences from almost infinite amounts of data the same way we can't play chess like Stockfish or do math like a calculator. The dualism is precisely in the opposite view, that computation is somehow "substrate independent". Searle argues we can have AI that has understanding the way we do, just that it's going to look more like an organic brain as a result.
The important insight from LLMs is that they're not like us at all but that doesn't make them less effective or intelligent. We do have plenty of understanding, we need to because we rely on a particular kind of reasoning, but artificial systems don't need to converge on that.
I think that if they would, that would be very surprising and indicative of a lot of wastefulness inside the model architecture. All these tests are simple single prompt experiments, so the LLM's get no chance to reason about their responses. They're just system 1 thinking, the equivalent of putting a gun to someone's head and asking them to solve a large division in 2 seconds. I bet a lot of these experiments would…
The equivalent for a human would be an reflexive response to a question, the kind you could immediately answer after being woken up at 3am in the morning. That type of answer has been deeply trained into the human networks and also requires no deep insight. But if a human is allowed time and internal reasoning iterations, so should the LLM when determining if it has deep insight. Right now we're simply observing inpu…
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Maybe the soul is social, and oriented towards others? I believe it can be constructed. If you assume that "the eyes are the window to the soul", you notice some interesting properties. 1. It is far more observable from the outside (eyes open/lidded/closed, emotion read in eyes) 2. It affects behavior in a diffuse way 3. It pays attention but does not dictate
> Maybe the soul is social My pet theory about human consciousness is that is that consciousness is simply recursive theory of mind. Theory of mind [1] is our ability to simulate and reason about the mental states of others. It's how we predict what people are thinking and how they will react to our actions, which is critical for choosing how to act in a social environment. But when you're thinking about what's in so…
No LLMs don't think like people, they're architecturally incapable of doing so. They have, physically unlike humans no access to their own internal state and they're, save for a small context window, static systems. They also have no insights. There's a hilarious video about LLM Jailbreaks by Karpathy[1] from a week ago, where he shows how you can break model responses by asking the same question with a base64 string…
As for the rest of it, the LLM is basically "raw compute". You need a self-referential loop and long-term memories for it to even have the notion of self. But looking at it at that level and discounting it as "incapable of thinking" is missing the point - it's the larger system of which LLM is one part, albeit a key one (and which we're still trying to figure out how to build) that might actually be conscious etc.
Earlier quoted context omitted.
The equivalent for a human would be an reflexive response to a question, the kind you could immediately answer after being woken up at 3am in the morning. That type of answer has been deeply trained into the human networks and also requires no deep insight. But if a human is allowed time and internal reasoning iterations, so should the LLM when determining if it has deep insight. Right now we're simply observing inpu…
This reminds me of The Last Question by Isaac Asimov. I also think if we stopped expecting all LLMs to have an immediate answer, it would be relatively easy to shim some kind of "conscience" to direct the output in different ways. Similar to the safeties already in place in LLMs, but instead of it just saying "NO DON'T SAY THAT" it can dialog internally to change what the output is until it reaches what it believes t…
Or we could just give them three laws
Earlier quoted context omitted.
I was right there with you until you mentioned Searle. :-) The Chinese room argument is bad in that it hides an assumption of mind/body dualism. If you believe that humans have "souls" and other things do not, then you have a qualitative difference between a human or a machine. On the other hand, if you are a materialist then you are faced with the problem that humans don't have much understanding of semantics either…
Searle's one of the most die-hard materialist philosophers of mind around. It's his materialism that leads him to make his argument. Computers and human brains are both made out of atoms but that doesn't mean they're not qualitatively different. By that logic I"d be no different from a tree. There's qualitative differences between computers and human brains. Our cognition is biochemical and horribly slow , just by vi…
"A chief goal of levers (cranes, etc.) engineering would be to build devices that lift like people"
> A chief goal of artificial intelligence [would be] to build machines that think like people "A chief goal of levers (cranes, etc.) engineering would be to build devices that lift like people"