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 Zouat Carnegie Mellon Universitysays "/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>*
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