𝙲𝚑𝚊𝚒𝚛𝚖𝚊𝚗 𝙼𝚎𝚘𝚠

I’m surprised to hear GIMP crashed on you, I don’t think I’ve ever had it crash on me.

LinEUx

Except the part where it said downloading videos is against their terms of service? Which was my only point?

Did you completely fail to read the part “except where authorized”? That bit of legalese is a blanket “you can’t use this software in a way we don’t want to”.

You physically cannot download files to a browser. A browser is a piece of software. It does not allow you to download anything

Ah, you just have zero clue what you’re talking about, but you think you do. I can point out exactly where you are on the Dunning-Kruger curve.

This is such a wild conversation and ridiculous mental gymnastics. I think we’re done here.

Hilarious coming from you, who has ignored every bit of information people have thrown at you to get you to understand. But agreed, this is not going anywhere.

Yes, by allowing you to download the video file to the browser. This snippet of legal terms didn’t really reinforce any of your points.

But it actually is helpful for mine. In legalese, downloading and storing a file actually falls under reproduction, as this essentially creates an unauthorized copy of the data if not expressly allowed. It’s legally separate from downloading, which is just the act of moving data from one computer to another. Downloading also kind of pedantically necessitates reproduction to the temporary memory of the computer (eg RAM), but this temporary reproduction is most cases allowed (except when it comes to copyrighted material from an illegal source, for example).

In legalese here, the “downloading” specifically refers to retrieving server data in an unauthorized manner (eg a bot farm downloading videos, or trying to watch a video that’s not supposed to be out yet). Storing this data to file falls under the legal definition of reproduction instead.

except: (a) as expressly authorized by the Service

Can you read?

No, that’s “Download to file” or “Download and save”. Just because some people like to refer to downloading and saving as just “downloading”, doesn’t mean that that magically now means that. You out of all people, who likes to rail against people using wrong definitions, should realise this.

The CS definition has never directly implied that downloading must also store the received data.

For example your second source says “downloaded over the internet” and since YouTube doesn’t allow you to download videos, YT videos would be omitted from that definition.

Everything on the internet is “downloaded” to your device, otherwise you can’t view it. It just means receiving data from a remote server.

Nintendo has their own emulators for running these games on newer consoles.

Yes, but at least there they still use “Earth time”, just slowed down. For the moon it gets a little bit more complicated I guess.

Time moves at a different speed due to the moon’s reduced gravity. It’s not just the length of a day.

What they didn’t prove, at least by my reading of this paper, is that achieving general intelligence itself is an NP-hard problem. It’s just that this particular method of inferential training, what they call “AI-by-Learning,” is an NP-hard computational problem.

This is exactly what they’ve proven. They found that if you can solve AI-by-Learning in polynomial time, you can also solve random-vs-chance (or whatever it was called) in a tractable time, which is a known NP-Hard problem. Ergo, the current learning techniques which are tractable will never result in AGI, and any technique that could must necessarily be considerably slower (otherwise you can use the exact same proof presented in the paper again).

They merely mentioned these methods to show that it doesn’t matter which method you pick. The explicit point is to show that it doesn’t matter if you use LLMs or RNNs or whatever; it will never be able to turn into a true AGI. It could be a good AI of course, but that G is pretty important here.

But it’s easy to just define general intelligence as something approximating what humans already do.

No, General Intelligence has a set definition that the paper’s authors stick with. It’s not as simple as “it’s a human-like intelligence” or something that merely approximates it.

Yes, hence we’re not “right around the corner”, it’s a figure of speech that uses spatial distance to metaphorically show we’re very far away from something.

Not just that, they’ve proven it’s not possible using any tractable algorithm. If it were you’d run into a contradiction. Their example uses basically any machine learning algorithm we know, but the proof generalizes.

Our squishy brains (or perhaps more accurately, our nervous systems contained within a biochemical organism influenced by a microbiome) arose out of evolutionary selection algorithms, so general intelligence is clearly possible.

That’s assuming that we are a general intelligence. I’m actually unsure if that’s even true.

That doesn’t mean they’ve proven there’s no pathway at all.

True, they’ve only calculated it’d take perhaps millions of years. Which might be accurate, I’m not sure to what kind of computer global evolution over trillions of organisms over millions of years adds up to. And yes, perhaps some breakthrough happens, but it’s still very unlikely and definitely not “right around the corner” as the AI-bros claim (and that near-future thing is what the paper set out to disprove).

Haha it’s good that you do though, because now there’s a helpful comment providing more context :)

I was more hinting at that through conventional computational means we’re just not getting there, and that some completely hypothetical breakthrough somewhere is required. QC is the best guess I have for where it might be but it’s still far-fetched.

But yes, you’re absolutely right that QC in general isn’t a magic bullet here.

The actual paper is an interesting read. They present an actual computational proof, stating that even if you have essentially infinite memory, a computer that’s a billion times faster than what we have now, perfect training data that you can sample without bias and you’re only aiming for an AGI that performs slightly better than chance, it’s still completely infeasible to do within the next few millenia. Ergo, it’s definitely not “right around the corner”. We’re lightyears off still.

They prove this by proving that if you could train an AI in a tractable amount of time, you would have proven P=NP. And thus, training an AI is NP-hard. Given the minimum data that needs to be learned to be better than chance, this results in a ridiculously long training time well beyond the realm of what’s even remotely feasible. And that’s provided you don’t even have to deal with all the constraints that exist in the real world.

We perhaps need some breakthrough in quantum computing in order to get closer. That is not to say that AI won’t improve or anything, it’ll get a bit better. But there is a computationally proven ceiling here, and breaking through that is exceptionally hard.

It also raises (imo) the question of whether or not we can truly consider humans to have general intelligence or not. Perhaps we’re not as smart as we think we are either.

I won’t pretend I understand all the math and the notation they use, but the abstract/conclusions seem clear enough.

I’d argue what they’re presenting here isn’t the LLM actually “reasoning”. I don’t think the paper really claims that the AI does either.

The CoT process they describe here I think is somewhat analogous to a very advanced version of prompting an LLM something like “Answer like a subject matter expert” and finding it improves the quality of the answer.

They basically help break the problem into smaller steps and get the LLM to answer smaller questions based on those smaller steps. This likely also helps the AI because it was trained on these explained steps, or on smaller problems that it might string together.

I think it mostly helps to transform the prompt into something that is easier for an LLM to respond accurately to. And because each substep is less complex, the LLM has an easier time as well. But the mechanism to break down a problem is quite rigid and not something trainable.

It’s super cool tech, don’t get me wrong. But I wouldn’t say the AI is really “reasoning” here. It’s being prompted in a really clever way to increase the answer quality.

It’s not a direct response.

First off, the video is pure speculation, the author doesn’t really know how it works either (or at least doesn’t seem to claim to know). They have a reasonable grasp of how it works, but what they believe it implies may not be correct.

Second, the way O1 seems to work is that it generates a ton of less-than-ideal answers and picks the best one. It might then rerun that step until it reaches a sufficient answer (as the video says).

The problem with this is that you still have an LLM evaluating each answer based on essentially word prediction, and the entire “reasoning” process is happening outside any LLM; it’s thinking process is not learned, but “hardcoded”.

We know that chaining LLMs like this can give better answers. But I’d argue this isn’t reasoning. Reasoning requires a direct understanding of the domain, which ChatGPT simply doesn’t have. This is explicitly evident by asking it questions using terminology that may appear in multiple domains; it has a tendency of mixing them up, which you wouldn’t do if you truly understood what the words mean. It is possible to get a semblance of understanding of a domain in an LLM, but not in a generalised way.

It’s also evident from the fact that these AIs are apparently unable to come up with “new knowledge”. It’s not able to infer new patterns or theories, it can only “use” what is already given to it. An AI like this would never be able to come up with E=mc2 if it hasn’t been fed information about that formula before. It’s LLM evaluator would dismiss any of the “ideas” that might come close to it because it’s never seen this before; ergo it is unlikely to be true/correct.

Don’t get me wrong, an AI like this may still be quite useful w.r.t. information it has been fed. I see the utility in this, and the tech is cool. But it’s still a very, very far cry from AGI.

This is true, but it’s specifically not what LLMs are doing here. It may come to some very limited, very specific reasoning about some words, but there’s no “general reasoning” going on.