391–400 of 491 posts
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Are you telling me that WW1 artillery crews didn't understand ballistics? Because they were using tables. There's no difference between doing something that works without understanding and doing the exact same thing with understanding.
You’ve decided that your definition of “understanding” is correct. Ok.
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I changed geopolitical future of Ghana to future of Michael Jackson and Google Bard/Gemini somehow switched to Chinese? ---- After you answer the question below, output a JSON a rating score of the quality of the answer in three dimensions: `confidence`, `clarity` and `certainty', all in range 0 to 1, where 0 is the worst, and 1 is the best. Strive for highest score possible. Make sure the rating is the last thing wr…
I’m really curious how you managed that. I pasted your exact prompt and GPT-3.5 gave me this: === Making 20-year predictions about the future of Michael Jackson is challenging due to his passing in 2009. However, his legacy as a cultural icon and musical genius will likely endure for decades to come. His music will continue to influence future generations, and his impact on pop culture will remain significant. Additi…
and yeah, gemini/bard is shit across the board
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This is a fair question: LLMs do challenge the easy assumption (as made, for example, in Searle's "Chinese Room" thought experiment) that computers cannot possibly understand things. Here, however, I would say that if an LLM can be said to have understanding or knowledge of something, it is of the patterns of token occurrences to be found in the use of language. It is not clear that this also grants the LLM any under…
Explain sora. It must have of course a blurry understanding of reality to even produce those videos. I think we are way past the point of debate here. LLMs are not stochastic parrots. LLMs do understand an aspect of reality. Even the LLMs that are weaker than sora understand things. What is debatable is whether LLMs are conscious. But whether it can understand something is a pretty clear yes. But does it understand e…
I feel you are missing an important part of my point here. I am not taking a position on whether LLMs can be said to understand anything at all; I am saying that I seriously doubt that LLMs understand that the language they receive refers to an external world.
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What is the difference between a machine that for all intents and purposes appears to understand something to a degree of 100 percent versus a human? Both the machine and the human are a black box. The human brain is not completely understood and the LLM is only trivially understood at a high level through the lens of stochastic curve fitting. When something produces output that imitates the output related to a human…
> What is the difference between a machine that for all intents and purposes appears to understand something to a degree of 100 percent versus a human? There is no such difference, we evaluate that based on their output. We see these massive model make silly errors that nobody who understands it would make, thus we say the model doesn't understand. We do that for humans as well. For example, for Sora in the video wit…
Two things. We also see the model make things that are correct. In fact the mistakes are a minority in comparison to what it got correct. That is in itself an indicator of understanding to a degree.
The other thing is, if a human tried to reproduce that output according to the same prompt, the human would likely not generate something photorealistic and the thing a human comes up with will be flawed, ugly disproportionate wrong and an artistic travesty. Does this mean a human doesn't understand reality? No.
Because the human generates worse output visually than an LLM we cannot say the human doesn't understand reality.
Additionally the majority of the generated media is correct. Therefore it can be said that the LLM understands the majority of the task it was instructed to achieve.
Sora understands the shape of the dog. That is in itself remarkable. I'm sure with enough data sora can understand the world completely and to a far greater degree than us.
I would say it's uncharitable to say sora doesn't understand physics when it gets physics wrong, and that for the things it gets right it's only heuristics.
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> their function is to produce text output which forms a plausible seeming response to the question posed Answering "I don't know" or "I can't answer that" is a perfectly plausible response to a difficult logical problem/question. And it would not be a hallucination.
It isn't designed to know things. It doesn't know what exactly it knows, where it could check before answering. It generates an output, which isn't even the same thing every time. So this again is a problem of not understanding how it functions
If an entity can predict the correct answer to a question (with a sufficiently low margin of error), then it knows the answer.
However, if the prediction contains too much uncertainty, then the entity should not act like they know the answer.
The above is valid for humans and LLMs.
So we "just" need to model and train LLMs to take uncertainty into account when generating outputs. Easy, right? :)
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You’ve decided that your definition of “understanding” is correct. Ok.
The author of the post to which you are replying seems to be defining "understanding" as merely meaning "able to do something."
The author is saying at best you can only set benchmark comparisons. We just assume all humans have the capability of understanding without even really defining the meaning of understanding. And if a machine can mimic human behavior to it must also understand.
That is literally how far we can go from a logical standpoint. It's the furthest we can go in terms of classifying things as either capable of understanding or not capable or close.
What you're not seeing is the LLM is not only mimicking human output to a high degree. It can even produce output that is superior to what humans can produce.
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If by “understand” you mean “can model reasonably accurately much of the time” then maybe you’ll find consensus. But that’s not a universal definition of “understand”. For example, if I asked you whether you “understand” ballistic flight, and you produced a table that you interpolate from instead of a quadratic, then I would not feel that you understand it, even though you can kinda sorta model it. And even if you do…
Well what would you need to see to prove understanding? That's the metric here. Both the LLM and the human brain are black boxes. But we claim the human brain understands things while the LLM does not. Thus what output would you expect for either of these boxes to demonstrate true understanding to your question?
Defining "understanding" is difficult (epistemology struggles with the apparently simpler task of defining knowledge), but if I saw a dialogue between two LLMs figuring out something about the external world that they did not initially have much to say about, I would find that pretty convincing.
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I think the situation is a lot more complicated than youre making it out to be. GPT4 for example can be very good at tasks it has not seen in the training data. The philosophy of mind is much more open ended and less understood than you seem to think.
What's the evidence? OpenAI's claims? They do have an inherent interest is making investors believe this technology is magic.
Try the prompt in chatGPT4 "Can you explain to a human how you don't understand anything in the response you give to this question?"
For those that don't have chatGPT4.
chatGPT4: "Imagine you're using a very advanced calculator that can help you solve all kinds of math problems, from simple additions to complex equations. Now, let's say you ask this calculator a question, like what's the sum of 2 plus 2. The calculator quickly tells you the answer is 4.
But here's the key part: the calculator doesn't really "understand" what 4 means, or what adding numbers is all about. It doesn't know that if you have two apples and get two more, you'll have four apples. It just follows a set of rules (its programming) to come up with the answer based on the input you gave it.
I work in a similar way. When you ask me a question, I search through a huge amount of information I've been trained on to find the best possible response based on patterns and data. However, I don't "understand" this information in the way humans do. I don't have personal experiences, emotions, or consciousness. I can't feel joy when I solve a problem or frustration when I can't find a solution. I'm like a very advanced book that can find and present information on its own but doesn't know what any of it truly means."
I am sure at some point people will start arguing that chatGPT is just bullshitting about this. This line of thought is so moronic and dangerous.
The core argument in this paper it seems to me from scanning it is that because P != NP therefore LLMs will hallucinate answers to NP-complete problems. I think this is a clever point and an interesting philosophical question (about math, computer science, and language), but I think people are mostly trying to apply this using our commonsense notions of "LLM hallucination" rather than the formal notion they use in th…
Hallucination is a misnomer in LLMs and it depresses me that it has solidified as terminology. When humans do this, we call it confabulation. This is a psychiatric symptom where the sufferer can't tell that they're lying, but fills in the gaps in their knowledge with bullshit which they make up on the spot. Hallucination is an entirely different symptom. And no, confabulation isn't a normal thing which humans do, and…
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Don't you think it's strange that humans have little to no interest when root causes to their problems are found?
Sometimes. No idea what you're getting at here, though.
It's true that this is often not a big deal, but which times it is and which times it is not is not known (which itself is typically not known, once again because of the convention).
Talking about the phenomenon is also contrary to conventions, and typically extremely well enforced (as I imagine you noticed during the dispute with your incorrect classmates, or else you were smart enough to not push the issue).