It matters if your civilizational system is built on assigning rights or responsibilities to things because they have consciousness or "interiority." Intelligence fits here just as well.

Currently many of our legal systems are set up this way, if in a fairly arbitrary fashion. Consider for example how sentience is used as a metric for whether an animal ought to receive additional rights. Or how murder (which requires deliberate, conscious thought) is punished more harshly than manslaughter (which can be accidental or careless.)

If we just treat intelligence as a descriptive quality and apply it to LLMs, we quickly realize the absurdity of saying a chatbot is somehow equivalent, consciously, to a human being. At least, to me it seems absurd. And it indicates the flaws of grafting human consciousness onto machines without analyzing why.

"Making predictions about the world" is a reductive and childish way to describe intelligence in humans. Did David Lynch make Mulholland Drive because he predicted it would be a good movie?

The most depressing thing about AI summers is watching tech people cynically try to define intelligence downwards to excuse failures in current AI.

I think that intelligence requires, or rather, is the development and use of a model of the problem while the problem is being solved, i.e. it involves understanding the problem. Accurate predictions, based on extrapolations made by systems trained using huge quantities of data, are not enough.

From a practical standpoint, all the talk of "true understanding", "sentience" and the likes is pointless.

The only real and measurable thing is performance. And the performance of AI systems only goes up.

Imagine LLM is conscious (as Anthropic wants us to believe). Imagine LLM is made to train on so much data which is far beyond what its parameter count allows for. Am I hurting the LLM by causing it intensive cognitive strain?

Predict and create, that's all that matters.

> Language doesn't just describe reality; it creates it.

I never under stand these kinds of statements.

Does the sun not exist until we have a word for it, did "under the rock" not exist for dinosaurs?

> I've always had the feeling that AI researchers want to build their own human without having to change diapers being part of the process. Just skip to adulthood please, and learn to drive a car without having experience in bumping into things and hurting yourself.

I partially agree, but the idea about AI is that you need to bump into things and hurt yourself only once. Then you have a good driver you can replicate at will

I think this might be the paper being referenced:

Melanie Mitchell (2021) "Why AI is Harder Than We Think." https://arxiv.org/abs/2104.12871

That sentence is not from this paper.

*skip to slavery

Are swimming and sailing the same, because they both have the result of moving through the water?

I'd say, no, they aren't, and there is value in understanding the different processes (and labeling them as such), even if they have outputs that look similar/identical.

It has absolutely nothing to do with reasoning, and I don't understand how anyone could think it's"close enough".

Reasoning models are simply answering the same question twice with a different system prompt. It's a normal LLM with an extra technical step. Nothing else.

[dead]

Yeah I think that's an important dimension. David Hume said that there was no action without passion and I think that's a key difference with AIs. They sit there passive until we interact with them. They dont want anything, they dont have goals, desires, motivations. The emotional part of the human psyche does a lot of work - we aren't just calculating sums

Brute force will always be part of the story, but it's not the solution. It just allows us to take an already working solution and make it better.

Not the author, but to extend this quote from the article:

> Its [Large Language Models] ability to write code and summarize text feels like a qualitative leap in generality that the monkey-and-moon analogy doesn't quite capture. This leaves us with a forward-looking question: How do recent advances in multimodality and agentic AI test the boundaries of this fallacy? Does a model that can see and act begin to bridge the gap toward common sense, or is it just a more sophisticated version of the same narrow intelligence? Are world models a true step towards AGI or just a higher branch in a tree of narrow linguistic intelligence?

I'd put the expression common sense on the same level as having causal connections, and would also assume that SOTA LLMs do not create an understanding based on causality. AFAICS this is known as the "reversal curse"[0].

[0]: https://youtu.be/zjkBMFhNj_g?t=750

It's AI effect let loose.

A lot of people really, really don't want LLMs to be "actually intelligent", so they oppose any use of any remotely "anthropomorphic" terms in application to LLMs on that principle alone.

IMO, anthropomorphizing LLMs is at least directionally correct in 9 cases out of 10.

It was, but it punted in the conclusion...

> Mitchell in her paper compares modern AI to alchemy. It produces dazzling, impressive results but it often lacks a deep, foundational theory of intelligence.

> It’s a powerful metaphor, but I think a more pragmatic conclusion is slightly different. The challenge isn't to abandon our powerful alchemy in search of a pure science of intelligence.

But alchemy was wrong and chasing after the illusions created by the frauds who promoted alchemy held back the advancement of science for a long time.

We absolutely should have abandoned alchemy as soon as we saw that it didn't work, and moved to figuring out the science of what worked.

I think you are on to something. Chasing AGI is - I believe - ultimately useless endeavour, but we can already use the existing tools we have in ingenious and creative ways. And no I don’t mean endless barrage of AI lofi hip hop or the same ”cool” album cover with random kanji that all of them have. For instance, it is pretty amazing to have a private tutor which with you can discuss why Charles XII of Sweden ultimately failed in his war against Russia or why roughly 30% of people seems to have a personality that leans toward authoritanianism - this is how people have learned since the very beginning of language. But conversation is an art and you get out from it what you bring into it. It also does not give you a readymade result which you can immediatedly capitalise on, which is what investors want, but what could and can ultimately be useful to humanity.

However, almost all models (worst is ChatGPT) are made virtually useless in this respect, since they are basically sycophantic yesmen - why on earth does an ”autocorrect on steroids” pretend to laugh at my jokes?

Next step is not to built faster models or throw more computing power at them, bit to learn to play the piano.

You can shuffle a deck of 52 cards, and be reasonably confident that nobody has ever gotten that exact shuffle (or probably ever will, until the universe dies). But at least in this case, we are sure that a deck of 52 cards can be arranged in any permutation of 52 cards. We know we can reach any state from any other state.

This is not the case for LLMs. We don't know what the full state space looks like. Just because the state space that LLMs (lossily) compress, is unimaginably huge, doesn't mean that you can assume that the state you want is one of them. So yeah, you might get a string of symbols that nobody has seen before, but you still have no way of knowing whether A) it's the string of symbols you wanted, and B) if it isn't, whether the string of symbols you wanted can ever be generated by the network at all.

The fact that it can copy smartly exactly ONE of the information in a given prompt (which is a complex sentence only humans could process before) and not others is absolutely a progress in computer science, and very useful. I’m still amazed by that everyday, I never thought I’d see an algorithm like that in my lifetime. (Calling it parroting is of course pejorative)

I let them know today — when i laid on my horn while passing a Waymo stopped at a green light blocking the left turn lane — with its right blinker on.

Re: Tesla, this company paid me nearly $250,000 under multiple lemon law claims for their “self driving” software issues i identified that affected safety.

We all know what happened with Cruise, which was after i declared myself constructively dismissed.

I think the characterization in the article is fair, “self driving” is not quite there yet.

They know. There’s a big difference being able to navigate the 80% of everyday driving situations and doing the 20% most people manage just fine but cars struggle with. There’s a road in these parts: narrow, twisty in three dimensions, unmarked, trees close to the road. Gets jolly slippery in the winter. I can drive that road in the middle of the night in sleet. Can an autonomous car?

Part of the points of fallacies one and four is that a human can get out of the car and walk into work as a CPA or whatever, while even the autonomous-ish offerings of Waymo et al don’t necessarily advance the ball on other domains

Way to argue a sentence out of context that has very little to do with the overall post.

Someone should let the rest of this pack know. Waymo is in a different league.

I honestly didnt understand the arguments. Could someone TLDR please ?

Waymo doesn’t drive on highways and needs huge break in periods to even expand its boundaries in cities it’s already operating in.