Comment by CamperBob2
Comment by CamperBob2 5 days ago
dangerous to students
It's fatally dangerous to students who ignore it or dismiss it out of hand. That much is already certain.
Comment by CamperBob2 5 days ago
dangerous to students
It's fatally dangerous to students who ignore it or dismiss it out of hand. That much is already certain.
Wait and see. You're not paying attention now, but it's not too late to start.
Go to your favorite programming puzzle site and see how you do against the latest models, for instance. If you can beat o1-pro regularly, for instance, then you're right, you have nothing to worry about and can safely disregard it. Same proposition that was offered to John Henry.
Please reformulate your argument, and I will check back tomorrow:
https://www.youtube.com/watch?v=aNSHZG9blQQ
LLMs are rules based search engines with higher dimension vector spaces encoding related topics. There is nothing intelligent about these algorithms, except the trick ones play on oneself interpreting well structured nonsense.
It is stunting kids development, as students often lack the ability to intuitively reason when they are being misled. "How many R's in 'Strawberry'?" is a classic example exposing the underlying pattern recognition failures. =3
I have never understood why the failure to answer the strawberry question has seen as a compelling argument as to the limits of AI. The AIs that suffer from this problem have difficulty counting. That has never been denied. Those AI's also do not see the letters of the words they are processing. Counting the letters in a word is a task that it is quite unsurprising that it fails. I Would say it is more surprising that that they can perform spelling tasks at all. More importantly the models where such weaknesses became apparent are all from the same timeframe where the models advanced so much that those weaknesses were visible only after so many other greater weaknesses had been overcome.
People didn't think that planes flying so high that pilots couldn't breathe exposed a fundamental limitation of flight, just that their success had revealed the next hurdle.
The assertion that an LLM is X and therefore not intelligent is not a useful claim to make without either proof that it is X and proof that X is insufficient. You could say brains are interconnected cells that send pulses at intervals dictated by a combination of the pulses they sense, and there is nothing intelligent about that. The premises must be true and you have to demonstrate that the conclusion follows from those premises. For the record I think your premises are false and your conclusion doesn't follow.
Without a proof you could hypothesise reasons why such a system might not be intelligent and come up with an example of a task that no system that satisfies the premises could accomplish. While that example is unsolved the hypothesis remains unrefuted. What would you suggest as a test that shows a problem that could not be solved by such a machine? It must be solvable by at least one intelligent entity to show that it is solvable by intelligence. It must be undeniable when the problem is solved.
(Shrug) If you're retired or independently wealthy, you can afford that attitude. Hopefully one of those describes you.
Otherwise, you're going to spend the rest of your career saying things like, "Well, OK, so the last model couldn't count the number of Rs in 'Strawberry' and the new one can, but..."
Personally, I dislike being wrong. So I don't base arguments on points that have a built-in expiration date, or that are based on a fundamental misunderstanding of whatever I'm talking about.
How so?