> They have no intelligence. These are very very very refined prediction engines.
Silly and pointless criticism. Why do the semantics of the word intelligence matter?
Also, you need to understand that language evolves over time, and people often use a word to describe a thing that is newly discovered or invented that's similar to the word being used, because it's a helpful way of describing it that the listener will understand better than a longwinded technical description. The purpose of language is to communicate thoughts, not engage in pedantry.
> But things like this slip by a frontier model in the same way AI from a few years ago would generate an image with seven fingers.
Yes, you make an excellent point here that over time, the capabilities of these models to recognize certain categories of problems have increased dramatically. I expect this will continue!
> it's foolish to give them "a company" to run, because they cannot understand when they've made a mistake the way even the least competent human can.
The words of someone who has not spent time with the least competent human, or anything close to it.
> Silly and pointless criticism. Why do the semantics of the word intelligence matter?
It matters because, we're still sorting out what intelligence means for an AI agent. As you pointed out, language evolves over time. The question remains whether or not attributing intelligence to the current iteration of models is correct. This is not settled and I don't see why it's wrong to bring it up.
It's not wrong to bring it up, but the other comment did not "bring it up". It purported to correct someone by categorically stating "it is not intelligence, you are wrong and I am right", while not once saying what, then, intelligence is.
One thing is clear: LLMs at least already are capable of 1) not making the same mistake that person made, and 2) clearly seeing why the other person's post was "wrong" in both reasoning and tone.
I mean, here's a pretty good starting point then: https://aclanthology.org/2020.acl-main.463.pdf
Language is indeed evolving.
Being good in chess was (and still is) associated with being intelligent. But if a computer does it, it is just a calculator (and it is).
Then go, the great game for intelligence, too complex for calculators to have a chance against intelligent humans. Until it was solved.
And then text, the original Turing test solved, AI capable enough to fool humans. And now already replacing humans in jobs strongly associated with intelligence - programming.
I find it hard to debate, that we don't have created artificial intelligence, by the way we used to use the word "intelligence" before.
So if AI is really understanding something?
Likely not in the way we use that term. But it definitely shows intelligent behavior and actions.
Go isn't solved.
Go is solved.
Hey, look I can make unsupported claims with just as much evidence as you!
Maybe you'd like to add some details as to why AlphaGo and its successors haven't "solved" Go (insofar as a game like Go can ever be "solved")?
When people talk about solving a game like go or chess, they mean proving mathematically if there exists a way to guarantee a win, or if the second player can always guarantee a draw, among related questions. Current game engines have no such knowledge, they just pick statistically what the think the best move is and hope for the best.
No, that is not what "people" in general mean, this is only what some people mean.
Other people know there is no solving go with calculating, but using statistics to achieve the goal of becoming better at humans. And they are. (With a recent unexpected exception unlikely to be repeated more often)
I don't think people "in general" refer to games as "solved" or "not solved" at all. Among people who do, chess and go are definitely not referred to as (fully) solved.
"to have a chance against intelligent humans. Until it was solved."
But this was the original statement - so solved references "chance against intelligent humans". And this is clearly solved. No comment on that there cannot be better go engines, but no matter how painful it is, they beat the best humans. (And it was painful, they did cry)
That's all the more reason to stop talking about it and actually discuss concrete capabilities or lack thereof.
We're all well aware that we have different thresholds for what we consider intelligence, and that these thresholds are constantly changing.
Even if we agree that "LLMs are AI!!!" or "LLMs are not AI!!!" in this thread, all we've established is that this particular set of commentors share a similar enough definition at this point in time.
So let's say we make up a new word, machilligence to serve as a parallel term to intelligence but strictly for machines. How is the world different in that case vs. if we call it intelligence?
Or I guess to put it another way, if we want to coin a new term for the intelligence-esque thing that AI has, but the key differentiator is that it's an AI thing and not a human thing, then what linguistic value does the new word have? If I said "Claude is intelligent" then the fact that we're talking about AI "intelligence" is already captured in the sentence anyway; no new word needed
I think "intelligence" carries baggage. When most people hear it, they're thinking of the constellation of things: judgment, consistency, moral reasoning, the ability to decide something and stick with it. An intelligent person has an internal model of the world, values they apply consistently, and the capacity to learn from mistakes in a meaningful way. LLMs don't do any of that. They perform statistical pattern matching on text at an extraordinary scale. They're shockingly good at mimicking the surface features of intelligent behavior. I think the overuse of the word intelligence is something to criticise as grandma is not across the tech details.
It matters the same way that calling a dog or a cat intelligent matters. It humanizes the thing, even if that isn't your intention. Right now that's not a big deal since most people agree that machines should not have rights, but I wouldn't take that for granted.
It's not the word I'm taking issue with, it's the concept. From your post you are surprised that the model makes mistakes and does not recognize things, and these are only surprising if you imagine the models to be thinking about things.
We can argue about words like thinking and intelligence all day long, and so can an LLM. But at the end of the day the LLM is only mimicking the processes you or I use.
Last week I tried out gpt sol 5.6 and asked it to count the letter r in a massive string of letters, without using an external app. It succeeded until I made the garbage sentence suitably large and then it consistently, confidently failed. Each time the "thinking" showed that it was teething to find a "gotcha" each time. "Ah, the first time I forgot to count the letters in the instruction itself" etc. at no point did it just understand that it had miscounted. It seems to be incapable of considering that it just made a regular mistake. Even a six year old child would just try again the same way and end up with the right answer
I know people who have run this experiment, with companies in the 7figs ARR, indie devs. Both of them have reverted to hiring humans.
[dead]