🤣🤣🤣🤣 Le ven. 13 déc. 2024, 18:31, Terren Suydam <[email protected]> a écrit :
> Babies don't exist. > > On Fri, Dec 13, 2024 at 4:18 AM 'Cosmin Visan' via Everything List < > [email protected]> wrote: > >> When you base an invention on the world of finite forms, of course that >> invention will be limited. You will never replicate the powers of >> consciousness, because consciousness draws its powers from the infinite >> world of the formless. And drawing from an infinite source, it is able to >> produce infinite forms and it doesn't need quazillions of forms to learn. A >> baby learns to speak from just a few examples, because what the parents to >> is not to provide raw data to the baby, but to stimulate the baby's >> consciousness to access the formless source and to draw from there whatever >> forms it needs in order to be able to speak and generally learn anything. >> >> On Friday, 13 December 2024 at 09:29:37 UTC+2 Alan Grayson wrote: >> >>> On Thursday, December 12, 2024 at 7:38:11 PM UTC-7 Brent Meeker wrote: >>> >>> Magic is always the explanation of those who can't understand. >>> >>> Brent >>> >>> >>> *There's plenty of magic, under a different name, in physics. Another >>> pitfall is religating hidden knowledge, aka occult knowledge, such as the >>> Chakras in Yoga, to de facto magic or someone's overactive imagination. AG * >>> >>> On 12/12/2024 1:39 PM, 'Cosmin Visan' via Everything List wrote: >>> >>> Magic! >>> >>> On Thursday, 12 December 2024 at 20:00:58 UTC+2 John Clark wrote: >>> >>> *The number of "tokens" (words or parts of words) used to train LLMs is >>> 100 times larger than it was in 2020, the largest are now using tens of >>> trillions. if you only consider text then the entire Internet only >>> contains about 3,100 trillion tokens. The amount of text LLMs train on is >>> doubling every year but the amount of human generated text on the Internet >>> is only growing at about 10% a year, if that trend continues AIs will run >>> out of text somewhere around 2028. Does that mean AI progress is about to >>> hit a wall? I don't think so for the following reasons:* >>> >>> *For one thing, because of improvements in algorithms, the computing >>> power needed for a Large Language Model to achieve the same performance >>> has halved about every 8 months. * >>> >>> *ALGORITHMIC PROGRESS IN LANGUAGE MODELS* >>> <https://arxiv.org/pdf/2403.05812> >>> >>> >>> *And computer chips specialized for AI rather than general computing, >>> like those made by Nvidia and other companies, are getting faster even more >>> rapidly than Moore's Law. Also, the rate of growth of specialized data >>> sets, such as astronomical and biological data, are growing much much more >>> quickly than text is; that's how AIs got so good at predicting how proteins >>> fold up. * >>> >>> *And there is vastly more information if AI's are trained on other types >>> of data besides text, and some AI's are already being trained on unlabeled >>> images and videos. Yann LeCun, chief AI scientist at Meta, said that >>> "although the 10^13 tokens used to train a LLM sounds like a lot (it >>> would take a human 170,000 years to read that much) , a 4-year-old child >>> has absorbed a volume of data 50 times greater than that just by looking at >>> objects during his waking hours. We’re never going to get to human-level AI >>> by just training on language, that’s just not happening".* >>> >>> *And then there's synthetic data. AlphaGeometry was trained to solve >>> geometry problems using 100 million computer generated synthetic examples >>> with no human demonstrations, and it ended up being as good at solving >>> difficult geometry problems as the very best high school students in the >>> entire nation. * >>> >>> *Solving olympiad geometry without human demonstrations* >>> <https://www.nature.com/articles/s41586-023-06747-5> >>> >>> *AI researchers are starting to change their strategy and have their >>> AI's reread their training set many times because AI's operate in a >>> statistical way so rereading improves performance * >>> >>> >>> *Scaling Data-Constrained Language Models* >>> <https://arxiv.org/pdf/2305.16264> >>> >>> >>> *Andy Zou at Carnegie Mellon University says "once an AI has got a >>> foundational knowledge base that’s probably greater than any single person >>> could have, it no longer needs more data to get smarter. It just needs to >>> sit and think. I think we’re probably pretty close to that point.”* >>> >>> *John K Clark See what's on my new list at Extropolis >>> <https://groups.google.com/g/extropolis>* >>> >>> >>> -- >> You received this message because you are subscribed to the Google Groups >> "Everything List" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to [email protected]. >> To view this discussion visit >> https://groups.google.com/d/msgid/everything-list/d190939b-b49f-4bd8-a77f-2cec16f8816dn%40googlegroups.com >> <https://groups.google.com/d/msgid/everything-list/d190939b-b49f-4bd8-a77f-2cec16f8816dn%40googlegroups.com?utm_medium=email&utm_source=footer> >> . >> > -- > You received this message because you are subscribed to the Google Groups > "Everything List" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To view this discussion visit > https://groups.google.com/d/msgid/everything-list/CAMy3ZA8ryAUjTOWo8VfyxxppkEbhCo05%2BjTuzNT8Nk8dn9HHeg%40mail.gmail.com > <https://groups.google.com/d/msgid/everything-list/CAMy3ZA8ryAUjTOWo8VfyxxppkEbhCo05%2BjTuzNT8Nk8dn9HHeg%40mail.gmail.com?utm_medium=email&utm_source=footer> > . > -- You received this message because you are subscribed to the Google Groups "Everything List" group. 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