wischi

The "may" carries a lot of weight so it probably depends. The way US law works is pretty weird IMHO and the reason for many of such disclaimers/waivers. "Objects in mirror are closer than they appear", "Contents may be hot", etc.

But wouldn't you point still be true today that the best AI video models today would be the onces that are not available for consumers?

Coding isn't special you are right, but it's a thinking task and LLMs (including reasoning models) don't know how to think. LLMs are knowledgeable because they remembered a lot of the data and patterns of the training data, but they didn't learn to think from that. That's why LLMs can't replace humans.

That does certainly not mean that software can't be smarter than humans. It will and it's just a matter of time, but to get there we likely have AGI first.

To show you that LLMs can't think, try to play ASCII tic tac toe (XXO) against all those models. They are completely dumb even though it "saw" the entire Wikipedia article on how xxo works during training, that it's a solved game, different strategies and how to consistently draw - but still it can't do it. It loses most games against my four year old niece and she doesn't even play good/perfect xxo.

I wouldn't trust anything, which is claimed to do thinking tasks, that can't even beat my niece in xxo, with writing firmware for cars or airplanes.

LLMs are great if used like search engines or interactive versions of Wikipedia/Stack overflow. But they certainly can't think. For now, but likely we'll need different architectures for real thinking models than LLMs have.

I don't see how that follows because I did point out in another comment that they are very useful if used like search engines or interactive stack overflow or Wikipedia.

LLMs are extremely knowledgeable (as in they "know" a lot) but are completely dumb.

If you want to anthropomorphise it, current LLMs are like a person that read the entire internet, remembered a lot of it, but still is too stupid to win/draw tic tac toe.

So there is value in LLMs, if you use them for their knowledge.

Thank you. But are jellyfish really not that far off. Looks like a pretty huge step to me. Jellyfish look complex enough to not just magically reassemble if we grind them through a sieve.

I personally (but I'm not a biologists 🤣) definitely would consider a jellyfish an animal because different cells (at least ot very much looks like that) have different functions and thus throwing it in the meat grinder (even if individual cells are not damaged) I can't imagine how ot could reassemble itself.

But a sponge seems so homogeneous it (I guess) almost doesn't matter what goes where and that's why it can reassemble. That why (I personally) wouldn't think of that as an animal.

Are there other things that are technically animals that are that homogeneous?

Thank you.

Totally agree with that and I don't think anybody would see that as controversial. LLMs are actually good in a lot of things, but not thinking and typically not if you are an expert. That's why LLMs know more about the anatomy of humans than I do, but probably not more than most people with a medical degree.

I can't speak for Lemmy but I'm personally not against LLMs and also use them on a regular basis. As Pennomi said (and I totally agree with that) LLMs are a tool and we should use that tool for things it's good for. But "thinking" is not one of the things LLMs are good at. And software engineering requires a ton of thinking. Of course there are things (boilerplate, etc.) where no real thinking is required, but non-AI tools like code completion/intellisense, macros, code snippets/templates can help with that and never was I bottle-necked by my typing speed when writing software.

It was always the time I needed to plan the structure of the software, design good and correct abstractions and the overall architecture. Exactly the things LLMs can't do.

Copilot even fails to stick to coding style from the same file, just because it saw a different style more often during training.

There actually isn't really any doubt that AI (especially AGI) will surpass humans on all thinking tasks unless we have a mass extinction event first. But current LLMs are nowhere close to actual human intelligence.

A drill press (or the inventors) don't claim that it can do that, but with LLMs they claim to replace humans on a lot of thinking tasks. They even brag with test benchmarks, claim Bachelor, Master and Phd level intelligence, call them "reasoning" models, but still fail to beat my niece in tic tac toe, which by the way doesn't have a PhD in anything 🤣

LLMs are typically good in things that happened a lot during training. If you are writing software there certainly are things which the LLM saw a lot of during training. But this actually is the biggest problem, it will happily generate code that might look ok, even during PR review but might blow up in your face a few weeks later.

If they can't handle things they even saw during training (but sparsely, like tic tac toe) it wouldn't be able to produce code you should use in production. I wouldn't trust any junior dev that doesn't set their O right next to the two Xs.

I don't think it's cherry picking. Why would I trust a tool with way more complex logic, when it can't even prevent three crosses in a row? Writing pretty much any software that does more than render a few buttons typically requires a lot of planning and thinking and those models clearly don't have the capability to plan and think when they lose tic tac toe games.

Honest question. How is that sponge an animal and how is "animal" defined? If we grind something through a sieve and it reassembles surely the lifeform can't be too complicated.