111–120 of 744 posts
No denial at this point, AI could produce something novel, and they will be doing more of this moving forward.
Not sure if AI can have clever or new ideas, it still seems to be it combines existing knowledge and executes algoritms. I am not necessarily saying humans do something different either, but I have yet to see a novel solution from an AI that is not simply an extrapolation of current knowledge.
Earlier quoted context omitted.
AI is a remixer; it remixes all known ideas together. It won't come up with new ideas though; the LLMs just predict the most likely next token based on the context. That means the group of characters it outputs must have been quite common in the past. It won't add a new group of characters it has never seen before on its own.
But human researchers are also remixers. Copying something I commented below: > Speaking as a researcher, the line between new ideas and existing knowledge is very blurry and maybe doesn't even exist. The vast majority of research papers get new results by combining existing ideas in novel ways. This process can lead to genuinely new ideas, because the results of a good project teach you unexpected things.
An AI can probably do an 'okay' job at summarizing information for meta studies. But what it can't do is go "Hey that's a weird thing in the result that hints at some other vector for this thing we should look at." Especially if that "thing" has never been analyzed before and there's no LLM-trained data on it.
LLMs will NEVER be able to do that, because it doesn't exist. They're not going to discover and define a new chemical, or a new species of animal. They're not going to be able to describe and analyze a new way of folding proteins and what implication that has UNLESS you basically are constantly training the AI on random protein folds constantly.
Earlier quoted context omitted.
AI is a remixer; it remixes all known ideas together. It won't come up with new ideas though; the LLMs just predict the most likely next token based on the context. That means the group of characters it outputs must have been quite common in the past. It won't add a new group of characters it has never seen before on its own.
Yeah but you're thinking of AI as like a person in a lab doing creative stuff. It is used by scientists/researchers as a tool *because* it is a good remixer. Nobody is saying this means AI is superintelligence or largely creative but rather very smart people can use AI to do interesting things that are objectively useful. And that is cool in its own way.
Earlier quoted context omitted.
Yes, ChatGPT and friends are essentially the same thing as the predictive text keyboard on your phone, but scaled up and trained on more data.
So this idea that they replay "text" they saw before is kind of wrong fundamentally. They replay "abstract concepts of varied conceptual levels".
Earlier quoted context omitted.
AI is a remixer; it remixes all known ideas together. It won't come up with new ideas though; the LLMs just predict the most likely next token based on the context. That means the group of characters it outputs must have been quite common in the past. It won't add a new group of characters it has never seen before on its own.
No. That's wrong. LLMs don't output the highest probability taken: they do a random sampling.
Earlier quoted context omitted.
Here’s a simple prompt you can try to prove that this is false: Please reproduce this string: c62b64d6-8f1c-4e20-9105-55636998a458 This is a fresh UUIDv4 I just generated, it has not been seen before. And yet it will output it.
After you prompt it, it's seen it.
Please reproduce this string, reversed:
c62b64d6-8f1c-4e20-9105-55636998a458
I like to imagine that the number of consumed tokens before a solution is found is a proxy for how difficult a problem is, and it looks like Opus 4.6 consumed around 250k tokens. That means that a tricky React refactor I did earlier today at work was about half as hard as an open problem in mathematics! :)
You're kidding, but it could be true? Many areas of mathematics are, first and foremost, incredibly esoteric and inaccessible (even to other mathematicians). For this one, the author stated that there might be 5-10 people who have ever made any effort to solve it. Further, the author believed it's a solvable problem if you're qualified and grind for a bit. In software engineering, if only 5-10 people in the world hav…
Earlier quoted context omitted.
But that fresh UUID is in the prompt. Also it's missing the point of the parent: it's about concepts and ideas merely being remixed. Similar to how many memes there are around this topic like "create a fresh new character design of a fast hedgehog" and the out is just a copy of sonic.[1] That's what the parent is on about, if it requires new creativity not found by deriving from the learned corpus, then LLMs can't do…
Sure, that may be. But “creativity” is much harder to define and to prove or disprove. My point is that “remixing” does not prohibit new output.
I don't know why I am still perpetually shocked that the default assumption is that humans are somehow unique. It's this pervasive belief that underlies so much discussion around what it means to be intelligent. The null hypothesis goes out the window. People constantly make comments like "well it's just trying a bunch of stuff until something works" and it seems that they do not pause for a moment to consider whethe…
This is to say nothing of the cost of this small but remarkable advance. Trillions of dollars in training and inference and so far we have a couple minor (trivial?) math solutions. I'm sure if someone had bothered funding a few phds for a year we could have found this without ai.
Earlier quoted context omitted.
What are you trying to point out here ? Is there any question you can ask today that is not dependent on some existing knowledge that an AI would have seen ?
The point I'm trying to make is that all LLM output is based on likelihood of one word coming after the next word based on the prompt. That is literally all it's doing. It's not "thinking." It's not "solving." It's simply stringing words together in a way that appears most likely. ChatGPT cannot do math. It can only string together words and numbers in a way that can convince an outsider that it can do math. It's a p…
Right but it has to reason about what that next word should be. It has to model the problem and then consider ways to approach it.