281–290 of 385 posts
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That’s not the AGI’s state. That’s just some past information.
State is a function of accumulated past information.
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> Anyway, the question of whether computers can think is as interesting as the question whether submarines can swim. Given the amount of ink spilled on the question, gotta disagree with you there.
Endless ink has been spilled on the most banal and useless things. Deconstructing ice cream and physical beauty from a Marxist-feminist race-conscious postmodern perspective.
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State is a function of accumulated past information.
State is a function of accumulated past. That does not mean that having some past written down makes you stateful. A stateful thing has to incorporate the ongoing changes.
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Every single discussion of ‘AGI’ has endless comments exactly like this. Whatever criticism is made of an attempt to produce a reasoning machine, there’s always inevitably someone who says ‘but that’s just what our brains do, duhhh… stop trying to feel special’. It’s boring, and it’s also completely content-free. This particular instance doesn’t even make sense: how can it be exactly the same , yet more sophisticated…
As the comment I replied to very correctly said, we don’t know how the brain produces cognition. So you certainly cannot discard the hypothesis that it works through “parroting” a weighted average of training data just as LLMs are alleged to do. Considering that LLMs with a much smaller number of neurons than the brain are in many cases producing human-level output, there is some evidence, if circumstantial, that our…
"A neuron in a neural network typically evaluates a sequence of tokens in one go, considering them as a whole input." -- ChatGPT
You could consider an RTX 4090 to be one neuron too.
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Why is any of this "interesting"? Is the response interesting because you don't know it? —How does it know this? Is the response interesting because you do know it or might have offered it yourself? —Confirmation bias. Is it interesting because a lot of training references contextualize it as "interesting?" —Begged question. Is it contextually interesting? —What is the context? A robot refers to robots? How unexpecte…
You are missing that this is precisely what we would expect a human to answer without further context (for instance without knowing how much you know about the topic). A human would pick similarly pick something which isn't too nerdy but also not obvious and the LLM did well here. If the LLM can fail that is fine, because the task is inherently hard.
Do you find it interesting that some LLMs routinely qualify responses to prompts to report something interesting with a statement that the response is interesting which can't reliably be suppressed by including a sub-prompt requesting suppression?
I don't, because I have no idea why I should expect any prompt to produce any sort of response.
I spent a few days goofing around with Stable Diffusion and found it frustrating because it could render a response to some prompts that that I found relevant and satisfying, but I couldn't get it to reliably render my intentions. I soon encountered obvious limits of its training set, and the community is adapting to these limits with with networks of domain-specific accessory models.
This experience greatly tempered my expectations: I see AI as a magic paintbrush or story reader. I see no evidence of thinking machine.
If we're going to establish an equivalence comparison between any AI and humans we need a theory for both.
I have yet to see a coherent theory of the AI but I believe there in such in a language I don't understand, just as there's a theory of Conway's Game of Life, which leads to continual fascination with the machine's behavior.
But I've been unable to find any theory of the human, nor will I expect any such theory, because to my eyes life looks like a realm of complexity incomparable any game.
I do have interest in seeing nerds struggle to explain AI, but am surprised that after several years no common vernacular from which a theory might be assembled has yet to appear.
An open-ended article about what AIs can't do seems hopelessly daft. It has already been formally established there are domains of what computation can never do. So to be interesting, a treatment of the limits of AI, being a form of a computer, had better start with a consideration of those domains. But this article does not, nor do any of the comments.
So whatever is going on with this discourse, it appears to me to have nothing to do with understanding of AIs.
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>We don't fully understand why current LLMs are bad at these tasks. In complete seriousness, can anyone can explain why LLMs are good at some tasks?
LLMs are good at tasks that don't require actual understanding of the topic. They can come up with excellent (or excellent-looking-but-wrong) answers to any question that their training corpus covers. In a gross oversimplification, the "reasoning" they do is really just parroting a weighted average (with randomness injected) of the matching training data. What they're doing doesn't really match any definition of "und…
Please show me where the training data exists in the model to perform this lookup operation you’re supposing. If it’s that easy I’m sure you could reimplement it with a simple vector database.
Your last two paragraphs are just dualism in disguise.
Why can't we resolve this with synthetic data? Just take the original statements and ask another LLM to flip around the relation, then train on both. I tried this with GPT-4 and it seems to understand the task [0]: Me: For each of the following relational statements, flip the relation around and generate a new statement or question answer pair. Input: Bluey's father is Bandit Output: Bandit's daughter is Bluey Input:…
If you think about it, they're making an extraordinary claim given how fluently LLMs engage in language and reasoning.
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That doesn't seem straightforward - although it's blind to letters because all it sees are tokens, it doesn't have much training data ABOUT tokens.
What parent is saying is that instead of asking the LLM to play a game of Wordle with tokens like TIME,LIME we ask it to play with tokens like T,I,M,E,L. This is easy to do.
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> There are problems that are easy for human beings but hard for current LLMs (and maybe impossible for them; no one knows). Examples include playing Wordle and predicting cellular automata (including Turing-complete ones like Rule 110). We don’t fully understand why current LLMs are bad at these tasks. I thought we did know for things like playing Wordle, that its because they deal with words as sequence of tokens t…
"Since there is no objective definition of AGI or test for it, there’s no basis for any meaningful speculation on what can or cannot achieve it; discussions about it are quasi-religious, not scientific." This is such a weird thing to say. Essentially _all_ scientific ideas are, at least to begin with, poorly defined. In fact, I'd argue that almost all scientific ideas remain poorly defined with the possible exception…
Would LLMs cross this threshold if we were able to train them only on works that are “objectively good”? if someone has better language than this, please enlighten me)
That is to say: coherent, empathetic, transparent, free from bias, substantiated, free from “fluff”.
For example: For science one cannot simply train from all works published in scientific journals because of the papers that have been written irrespective of facts, or had the data changed, or have been written with specific agendas. In most cases even the experts have a hard time weeding out all the papers that are not “objectively good”. How could an LLM hope to make the determination during training?