𝙲𝚑𝚊𝚒𝚛𝚖𝚊𝚗 𝙼𝚎𝚘𝚠

Johnson’s position on Ukraine is one of the few things where he did in fact show some strong leadership. Couldn’t fault him for that.

He failed in a lot of other areas unfortunately.

Women ended up not really voting for her either. She got worse results than Clinton did with women.

Morality is a product of civilisation and community. It’s the ability of groups to decide on a single set of rules by which they would lime to be treated by, as breach of those rules can cause physical or emotional harm. And then there’s simple evolution, where certain “moral rules” allowed civilisations to survive and thrive better than others.

At no point is “god” required here.

LinEUx

Currencies going up in value tends to not be great for an economy, as people will save instead of spend. It stops being a currency and becomes somewhat of an asset. A slowly depreciating currency tends to foster the most economic growth.

Doesn’t higher interests mean more money is spent paying those interests, meaning less money is available to spend on other things which in turn reduces the monetary supply in circulation which curbs inflation?

Terrorism is more about the intent rather than the result. Did Israel intend to instill terror in the civilian population or did they genuinely try to target Hezbollah militants (and perhaps didn’t care much about any civilian casualties)?

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.

RFCs aren’t really law you know. They can deviate, it just means less compatibility.

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.

The building you just linked was built by a cooperative association as well, many of whom now live in that building.

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.

For a month, we’d talk about aliens. Then, we’d talk about Trump or Musks latest shit takes about aliens.