Apple Researchers Show Critical Flaw in AI
latimes.com
Apple Researchers Show Critical Flaw in AI
1–10 of 12 posts
Re: Apple Researchers Show Critical Flaw in AI
#2Instead of very accurate results at low cost, they produce inaccurate results at high cost.
Generalized intelligence and reasoning are not achievable by brute force statistical simulation --- regardless of the amount of money and hope invested/wasted.
Re: Apple Researchers Show Critical Flaw in AI
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Re: Apple Researchers Show Critical Flaw in AI
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#8I wonder if AI being really useful when it comes to programming caused some to miscalculate its usefulness in general.
Expecting real intelligence to "emerge" from a binary logic playback device (aka a computer as we know) is just a variation on the Infinite Monkey Theorem in my opinion. In other words, the odds are not quite zero --- but they are very near it.
https://www.sciencealert.com/scientists-confirm-monkeys-do-n...
Re: Apple Researchers Show Critical Flaw in AI
#9Finally, the highly reputable science publication LA Times provides proof that LLMs are in fact large language models, rather than large math solvers or large fact models.
And why not?
Math is the most logical and precise language ever invented.
If LLMs can truly think and reason and understand, I would expect them to excel at math problems. Or at least admit that it can't do math and logic.
Re: Apple Researchers Show Critical Flaw in AI
#10Finally, the highly reputable science publication LA Times provides proof that LLMs are in fact large language models, rather than large math solvers or large fact models.
...large language models, rather than large math solvers or large fact models. And why not? Math is the most logical and precise language ever invented. If LLMs can truly think and reason and understand, I would expect them to excel at math problems. Or at least admit that it can't do math and logic.
But they can't, which was kind of my point. They are clever token predictors, they know language, which makes them really good text generators ("stochastic parrots"), but even trivial tasks like counting the letters in a word is hit-or-miss, especially if the solution is not found in their training data.
I don't understand why people find this surprising. It's remarkable that LLMs can solve some problems at all, not the other way around.