@Brent The only woo-woo is your belief in "matter". On Saturday, 14 December 2024 at 01:46:07 UTC+2 Brent Meeker wrote:
> > > > On 12/13/2024 1:18 AM, 'Cosmin Visan' via Everything List 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. > > Let's see you produce and infinite form or two. > > > A baby learns to speak from just a few examples, because what the parents > to is not to provide raw data to the baby, > > Twins often invent their own language which the speak to each other. > Evolution has provided the raw data to create language. > > > 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. > > Woo-Woo magic. > > Brent > > > 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]. 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