@theadhocracy @Noisecolor @xvf17 @emilymbender I am going to side with the extremely rude person here on the definition of learning, because it *does* have applicability in Machine Learning. It's a dual use word. It does *not* have the same implications in machine use as in humans, and the mechanism is different. But the term is applicable.
@SomeVeganCheeseIsOk @Noisecolor @xvf17 @emilymbender I can agree with that, but the term in this case was being used in a definition, specifically "machines that can learn like a human". That's why I was referencing it.
I did explicitly add a comment about how "learning" can be applied to algorithmic behaviour (ML etc.), but 1) that isn't the way the term is used in the definition of AI (scope is narrower), and 2) I don't agree that's enough to make the leap for LLMs to *intelligence*
@SomeVeganCheeseIsOk @Noisecolor @xvf17 @emilymbender But yeah, in this instance, I still think its a real stretch to claim that GenAI models **learn**. They may be born from reinforcement algorithms, and we may have adopted "learning" to describe aspects of that behaviour, but imo that is a marketing term.
Biological algorithms actually *learn* and modify their behaviour without oversight. So I'm not saying that ML is never learning. Just that I disagree that such a broad redefinition is okay
@theadhocracy
You are twisting and turning and doing mental gymnastics in order to get even an imaginary straw in which you can maybe be right.
While we have called ai romba robots for years without any issues form anyone.
And now some people developed a big problem with lmms. A very specific group of people that hate ai. Do you see it? So you see something not quite right with this situation?
@Noisecolor @SomeVeganCheeseIsOk @xvf17 @emilymbender Speaking of strawmen... Yes, lots of people mocked and were openly concerned about using "AI" as marketing for smart home devices and tools like Roombas. Those are also not AI, no one in the industry thinks so, there's no gotcha here.
Plus, one of the big distinctions is that the general public didn't fall for it in the same way, partially because a Roomba can't self-reinforce a delusional loop with its user, making them think its sentient.
@Noisecolor @SomeVeganCheeseIsOk @xvf17 @emilymbender Simply saying "we've called other things AI before, even when they clearly weren't" isn't a good argument, it's literally the point we're all trying to make.
Is an LLM a "more advanced" form of software than the path-finding in a Roomba? Yes. Do both have capacity to capture data and update their output over time? Yes.
Are either of them "intelligent"? No. [cont.]
@Noisecolor @SomeVeganCheeseIsOk @xvf17 @emilymbender What do I mean be "intelligent"? I don't mean the laymen definition in this instance, but the scientific definition.
The definition we use when discussing whether another species is "intelligent". You could call this "higher order intelligence"; I don't love that term.
The point is, if you class an LLM as "intelligent" in that way, you broaden the definition to every form of life. Even an amoeba "learns" in this manner. [cont.]
@Noisecolor @SomeVeganCheeseIsOk @xvf17 @emilymbender But, to return to your own argument, that isn't what the term "AI" was meant for when it was coined. It was explicitly talking about machines that learned **like humans**. That reasoned. That *understood*.
A Roomba no more understands what a chair is when its avoiding its leg as an LLM understands why there aren't four Rs in strawberry. Both have a form of data memory. Neither have *understanding*. Therefore, neither are AI.
@theadhocracy
I think this exchange has ran it's course. You clearly have a very subjective and narrow definition of ai and intelligence in general and you know what. Good for you.
It's not shared by almost no people and certainly almost no experts. You know who shares it, only the anti ai crowd. Strange?
I was hoping for an inkling of an open mind but I see now that's not there
Have a nice day. Take it easy.
@theadhocracy @Noisecolor @xvf17 @emilymbender "machine learning" isn't a marketing term. It's a research term that's been around for a couple decades now. It originated as a linguistic way to describe compute systems incorporating self-adaptive functionality. A better term might have been training, but learning was what they picked.
@SomeVeganCheeseIsOk @Noisecolor @xvf17 @emilymbender To be clear: not entirely what I meant. I have no issues with the umbrella term "machine learning"; I actually think it works pretty well.
I am specifically talking about GenAI models/LLMs. Again, I think this is a case of linguistic drift. Are they born of ML models? Yes. Do they themselves **learn**. I think that's arguable.
They reincorporate additional data over time. But the core rules don't adapt (caveat incoming)...
@SomeVeganCheeseIsOk @Noisecolor @xvf17 @emilymbender ... unless they're in some kind of feedback loop, running via multiple layers of models, some of which then manipulate those they have greater write level over. The "shepherd and sheep" model stuff.
But that isn't, again, what most people are talking about. An Agent "learning" your habits isn't the same thing. The input/output model isn't adapting, it's just getting more input.
I think we broadly agree anyway 😅