Re: Why LLMs are bound to fail

The market already seems to have made up its mind.

https://finance.yahoo.com/news/generative-ai-getting-kicked-off-191657116.html

The two trillion dollar question is: where do we go from here?

KRR for AI and unraveling the brain processes key to memory formation,
storage, recall and cognition seem the way to go.

The latter are subject to issues of causality, nonlocality, quantum effects
and the free energy principle.

Buddhism sheds some light on causality and quantum effects.

But we have a long way to go in terms of figuring out the inner workings of
the human brain and the mathematical modeling of such and the hard problem
of consciousness and current clashes between computer scientists,
mathematicians and philosophers make it very clear that we need to figure
out the right questions as Dave mentioned.

That may very well be one of the hardest nuts to crack.

In mathematics we have the Langlands program. We may have to come up with
something similar for defining and discovering the linkages and
interactions between different theories and disciplines in cognitive
science, computational biology, neuroscience, philosophy, AI and
mathematical modeling (the latter including theoretical physics as related
to quantum physics and quantum biology).

This program will elucidate the right questions and consequently show the
path to KRR.


Milton Ponson
Rainbow Warriors Core Foundation
CIAMSD Institute-ICT4D Program
+2977459312
PO Box 1154, Oranjestad
Aruba, Dutch Caribbean


On Thu, Aug 8, 2024 at 6:29 AM Dave Raggett <dsr@w3.org> wrote:

> LLMs are designed to make statistical predictions for text continuations,
> and I am amazed by how well they do that. It is unsurprising that they are
> weak on semantic consistency, just as they are for learning from limited
> data. LLMs are good for summarisation, but not for deep insights. That’s
> fine for some applications, but not others. LLMs can certainly help with
> web search, but you still need to think for yourself given the limitations
> of LLMs. I very much doubt that today’s LLMs will provide the hoped for
> return on investment. That said, LLMs are useful and will remain
> commonplace.
>
> I am not sure that Buddhism can help much when it comes to the details of
> how neurons compute.  Next generation AI will depend on a much better
> understanding of human memory. How is it that we can remember and learn
> from single episodes?  That includes speech and music.  One of the
> challenges is how neurons can learn on the scale of seconds and much longer
> rather than milliseconds.  I am trying to figure out which questions are
> vital to developing a model that I can implement in code.  Progress is
> dependent on identifying the “right” questions.
>
>
> On 7 Aug 2024, at 17:24, Milton Ponson <rwiciamsd@gmail.com> wrote:
>
> https://www.theguardian.com/technology/article/2024/aug/06/ai-llms.
>
> Interesting article that stresses the point that relationships between
> facts differentiate humans (for now) and AI that uses stochastic
> information about tokens to come up with text generation and problem
> solving.
>
> Now here is the mind boggling aspect, Buddhists talk about dependent
> arising in knowledge, the free energy principle, causal cognition and the
> way the brain processes sensory input and assimilates and stores this all
> hint at complex sequential processing across multiple areas of the brain
> with particular wave activities surging back and forth between areas in the
> brain.
>
> This seems to make LLM generative AI not fit for modeling AGI.
>
>
> Milton Ponson
> Rainbow Warriors Core Foundation
> CIAMSD Institute-ICT4D Program
> +2977459312
> PO Box 1154, Oranjestad
> Aruba, Dutch Caribbean
>
>
> Dave Raggett <dsr@w3.org>
>
>
>
>