𝙲𝚑𝚊𝚒𝚛𝚖𝚊𝚗 𝙼𝚎𝚘𝚠

I was friends with her and I trusted that she would handle it well if she wasn’t interested. And turns out she indeed wasn’t interested, but we did talk about it and decided to just stay friends. It was a little awkward as my feelings for her still lingered a bit, but eventually that passed and I’m now with a wonderful girl who I think is a much better match for me.

We’re still friends to this day.

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

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.

I meant a library unknown to me specifically. I do encounter hallucinations every now and then but usually they’re quickly fixable.

It’s made me a little bit faster, sometimes. It’s certainly not like a 50-100% increase or anything, maybe like a 5-10% at best?

I tend to write a comment of what I want to do, and have Copilot suggest the next 1-8 lines for me. I then check the code if it’s correct and fix it if necessary.

For small tasks it’s usually good enough, and I’ve already written a comment explaining what the code does. It can also be convenient to use it to explore an unknown library or functionality quickly.

If producing an AGI is intractable, why does the human meat-brain exist?

Ah, but here we have to get pedantic a little bit: producing an AGI through current known methods is intractable.

The human brain is extremely complex and we still don’t fully know how it works. We don’t know if the way we learn is really analogous to how these AIs learn. We don’t really know if the way we think is analogous to how computers “think”.

There’s also another argument to be made, that an AGI that matches the currently agreed upon definition is impossible. And I mean that in the broadest sense, e.g. humans don’t fit the definition either. If that’s true, then an AI could perhaps be trained in a tractable amount of time, but this would upend our understanding of human consciousness (perhaps justifyingly so). Maybe we’re overestimating how special we are.

And then there’s the argument that you already mentioned: it is intractable, but 60 million years, spread over trillions of creatures is long enough. That also suggests that AGI is really hard, and that creating one really isn’t “around the corner” as some enthusiasts claim. For any practical AGI we’d have to finish training in maybe a couple years, not millions of years.

And maybe we develop some quantum computing breakthrough that gets us where we need to be. Who knows?

This is a gross misrepresentation of the study.

That’s as shortsighted as the “I think there is a world market for maybe five computers” quote, or the worry that NYC would be buried under mountains of horse poop before cars were invented.

That’s not their argument. They’re saying that they can prove that machine learning cannot lead to AGI in the foreseeable future.

Maybe transformers aren’t the path to AGI, but there’s no reason to think we can’t achieve it in general unless you’re religious.

They’re not talking about achieving it in general, they only claim that no known techniques can bring it about in the near future, as the AI-hype people claim. Again, they prove this.

That’s a silly argument. It sets up a strawman and knocks it down. Just because you create a model and prove something in it, doesn’t mean it has any relationship to the real world.

That’s not what they did. They provided an extremely optimistic scenario in which someone creates an AGI through known methods (e.g. they have a computer with limitless memory, they have infinite and perfect training data, they can sample without any bias, current techniques can eventually create AGI, an AGI would only have to be slightly better than random chance but not perfect, etc…), and then present a computational proof that shows that this is in contradiction with other logical proofs.

Basically, if you can train an AGI through currently known methods, then you have an algorithm that can solve the Perfect-vs-Chance problem in polynomial time. There’s a technical explanation in the paper that I’m not going to try and rehash since it’s been too long since I worked on computational proofs, but it seems to check out. But this is a contradiction, as we have proof, hard mathematical proof, that such an algorithm cannot exist and must be non-polynomial or NP-Hard. Therefore, AI-learning for an AGI must also be NP-Hard. And because every known AI learning method is tractable, it cannor possibly lead to AGI. It’s not a strawman, it’s a hard proof of why it’s impossible, like proving that pi has infinite decimals or something.

Ergo, anyone who claims that AGI is around the corner either means “a good AI that can demonstrate some but not all human behaviour” or is bullshitting. We literally could burn up the entire planet for fuel to train an AI and we’d still not end up with an AGI. We need some other breakthrough, e.g. significant advancements in quantum computing perhaps, to even hope at beginning work on an AGI. And again, the authors don’t offer a thought experiment, they provide a computational proof for this.

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.

I do not think this is a strong argument. Nobody considers NASA to be the “space Nazis” either, just because some employees had connections with Nazi Germany. It’s a huge leap to claim NATO follows or is connected to some Nazi ideology based on this.

Shareholders can demand external audits under threat of selling the stock. There’s plenty shareholders can do (and have done in the past). They don’t just sit idle and not do anything you know.

Shareholders seek to maximize profits. If that includes a lawsuit to squeeze out even more investments, then why not?

They never bothered to check if Boeing did what they had to do security wise. Only once it threatened their profits they sprang into action.

That’s a very specific usecase though that the majority of programmers likely will never have to face.

Trump was officially nominated by the RNC as their candidate. Biden hadn’t been nominated by the DNC yet, that was supposed to happen at the conference in a couple weeks. But now that he has pulled out, he will not be nominated and thus not be the official candidate.

In the Netherlands 112 is fine, most critical systems are. It’s mostly airports that are getting fucked by this it seems.

Banks and PSPs are fine here too.