I'm not convinced that humans don't reason in a similar fashion. When I'm asked to produce pointless bullshit at work my brain puts in a similar level of reasoning to an LLM.

Think about "normal" programming: An experienced developer (that's self-trained on dozens of enterprise code bases) doesn't have to think much at all about 90% of what they're coding. It's all bog standard bullshit so they end up copying and pasting from previous work, Stack Overflow, etc because it's nothing special.

The remaining 10% is "the hard stuff". They have to read documentation, search the Internet, and then—after all that effort to avoid having to think—they sigh and start actually start thinking in order to program the thing they need.

LLMs go through similar motions behind the scenes! Probably because they were created by software developers but they still fail at that last 90%: The stuff that requires actual thinking.

Eventually someone is going to figure out how to auto-generate LoRAs based on test cases combined with trial and error that then get used by the AI model to improve itself and that is when people are going to be like, "Oh shit! Maybe AGI really is imminent!" But again, they'll be wrong.

AGI won't happen until AI models get good at retraining themselves with something better than basic reinforcement learning. In order for that to happen you need the working memory of the model to be nearly as big as the hardware that was used to train it. That, and loads and loads of spare matrix math processors ready to go for handing that retraining.

Just like me

That just means they'd be great CEOs!

According to Wall Street.

Sorry, I can see why my original post was confusing, but I think you've misunderstood me. I'm not claiming that I know the way humans reason. In fact you and I are on total agreement that it is unscientific to assume hypotheses without evidence. This is exactly what I am saying is the mistake in the statement "AI doesn't actually reason, it just follows patterns". That is unscientific if we don't know whether or "actually reasoning" consists of following patterns, or something else. As far as I know, the jury is out on the fundamental nature of how human reasoning works. It's my personal, subjective feeling that human reasoning works by following patterns. But I'm not saying "AI does actually reason like humans because it follows patterns like we do". Again, I see how what I said could have come off that way. What I mean more precisely is:

It's not clear whether AI's pattern-following techniques are the same as human reasoning, because we aren't clear on how human reasoning works. My intuition tells me that humans doing pattern following seems equally as valid of an initial guess as humans not doing pattern following, so shouldn't we have studies to back up the direction we lean in one way or the other?

I think you and I are in agreement, we're upholding the same principle but in different directions.

ITT: people who obviously did not study computer science or AI at at least an undergraduate level.

Y'all are too patient. I can't be bothered to spend the time to give people free lessons.

Define, "reasoning". For decades software developers have been writing code with conditionals. That's "reasoning."

LLMs are "reasoning"... They're just not doing human-like reasoning.

Can’t really replace. At best, this tech will make employees more productive at the cost of the rainforests.

But reasoning about it is intelligent, and the point of this study is to determine the extent to which these models are reasoning or not. Which again, has nothing to do with emotions. And furthermore, my initial question about whether or not pattern following should automatically be disqualified as intelligence, as the person summarizing this study (and notably not the study itself) claims, is the real question here.

To be fair, the world of JavaScript is such a clusterfuck... Can you really blame the LLM for needing constant reminders about the specifics of your project?

When a programming language has five hundred bazillion absolutely terrible ways of accomplishing a given thing—and endless absolutely awful code examples on the Internet to "learn from"—you're just asking for trouble. Not just from trying to get an LLM to produce what you want but also trying to get humans to do it.

This is why LLMs are so fucking good at writing rust and Python: There's only so many ways to do a thing and the larger community pretty much always uses the same solutions.

JavaScript? How can it even keep up? You're using yarn today but in a year you'll probably like, "fuuuuck this code is garbage... I need to convert this all to [new thing]."

"Artificial" has several meanings.

One is:

not being, showing, or resembling sincere or spontaneous behavior : fake

AI in video games literally means "fake intelligence"

Unlike fear-mongering from the right about immigrants, current iterations of AI development:

• literally consume the environment (they are using electricity and water)
• are taking jobs and siphoning money from the economy towards centralized corporate revenue streams that don't pay a fair share of taxes
• I don't know of headlines claiming they will sexually assault you, but many headlines note that they can be used as part of sophisticated catfishing scams, which they are

All of these things aren't scare tactics. They're often overblown and exaggerated for clicks, but the fundamental nature of the technology and corporate implementation of it indisputable.

Open-source AI can change the world for the better. Corporate-controlled AI in some limited cases will improve the world, but without reasonable regulations they will severely harm it first.

as it is defined in the article

If the situation gets dire, it's likely that the weather will be manipulated. Countries would then have to be convinced not to use this for military purposes.

Howabout uh...

The ability to take a previously given set of knowledge, experiences and concepts, and combine or synthesize them in a consistent, non contradictory manner, to generate hitherto unrealized knowledge, or concepts, and then also be able to verify that those new knowledge and concepts are actually new, and actually valid, or at least be able to propose how one could test whether or not they are valid.

Arguably this is or involves meta-cognition, but that is what I would say... is the difference between what we typically think of as 'machine reasoning', and 'human reasoning'.

Now I will grant you that a large amount of humans essentially cannot do this, they suck at introspecting and maintaining logical consistency, that they are just told 'this is how things work', and they never question that untill decades later and their lives force them to address, or dismiss their own internally inconsisten beliefs.

But I would also say that this means they are bad at 'human reasoning'.

Basically, my definition of 'human reasoning' is perhaps more accurately described as 'critical thinking'.

That sounds really floppy.

That's only part of the problem. Yes, JavaScript is a fragmented clusterfuck. Typescript is leagues better, but by no means perfect. Still, that doesn't explain why the LLM can't recall that I'm using Yarn while it's processing the instruction that specifically told it to use Yarn. Or why it tries to start editing code when I tell it not to. Those are still issues that aren't specific to the language.

python code for reversing the linked list.

“Late to the hype” is actually a good thing. Gen AI is a scam wrapped in idiocy wrapped in a joke. That Apple is slow to ape the idiocy of microsoft is just fine.

If emissions dropped to 0 tonight, we would be substantially better off than if we maintain our current trajectory. Doomerism helps nobody.

Literally what I'm talking about. They have been pushing anti AI propaganda to alienate the left from embracing it while the right embraces it. You have such a blind spot you this, you can't even see you're making my argument for me.