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Ok I made a joke, sorry (e: 2nd Cognitive Turn ~~> no Bayesian Brain)

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My impression Cognitive Science was never
Bayesian Brain, so I guess I made a joke.

The time scale, its start in 1950s and that
it is still relative unknown subject,

would explain:
- why my father or mother never tried to
   educated me towards cognitive science.
   It could be that they are totally blank
   in this respect?

- why my grandfather or grandmothers never
   tried to educate me towards cognitive
   science. Dito It could be that they are totally
   blank in this respect?

- it could be that there are rare cases where
   some philosophers had already a glimps of
   cognitive science. But when I open for
   example this booklet:

System der Logic
Friedrich Ueberweg
Bonn - 1868
https://philpapers.org/rec/UEBSDL

   One can feel the dry swimming that is reported
   for several millennia.  What happened in the
   1950s was the possibility of computer modelling.

Mild Shock schrieb:
> Hi,
> 
> Yes, maybe we are just before a kind
> of 2nd Cognitive Turn. The first Cognitive
> Turn is characterized as:
> 
>  > The cognitive revolution was an intellectual
>  > movement that began in the 1950s as an
>  > interdisciplinary study of the mind and its
>  > processes, from which emerged a new
>  > field known as cognitive science.
> https://en.wikipedia.org/wiki/Cognitive_revolution
> 
> The current mainstream believe is that
> Chat Bots and the progress in AI is mainly
> based on "Machine Learning", whereas
> 
> most of the progress is more based on
> "Deep Learning". But I am also sceptical
> about "Deep Learning" in the end a frequentist
> 
> is again lurking. In the worst case the
> no Bayension Brain shock will come with a
> Technological singularity in that the current
> 
> short inferencing of LLMs is enhanced by
> some long inferencing, like here:
> 
> A week ago, I posted that I was cooking a
> logical reasoning benchmark as a side project.
> Now it's finally ready! Introducing 🦓 𝙕𝙚𝙗𝙧𝙖𝙇𝙤𝙜𝙞𝙘,
> designed for evaluating LLMs with Logic Puzzles.
> https://x.com/billyuchenlin/status/1814254565128335705
> 
> making it possible not to excell by LLMs
> in such puzzles, but to advance to more
> elaborate scientific models, that can somehow
> 
> overcome fallacies such as:
> - Kochen Specker Paradox, some fallacies
>    caused by averaging?
> - Gluts and Gaps in Bayesian Reasoning,
>    some fallacies by consistency assumptions?
> - What else?
> 
> So on quiet paws AI might become the new overlord
> of science which we will happily depend on.
> 
> Jeff Barnett schrieb:
>  > You are surprised; I am saddened. Not only have
> we lost contact with the primary studies of knowledge
> and reasoning, we have also lost contact with the
> studies of methods and motivation. Psychology
> was the basic home room of Alan Newell and many
> other AI all stars. What is now called AI, I think
> incorrectly, is just ways of exercising large amounts
> of very cheap computer power to calculate approximates
> to correlations and other statistical approximations.
> 
> The problem with all of this in my mind, is that we
> learn nothing about the capturing of knowledge, what
> it is, or how it is used. Both logic and heuristic reasoning
> are needed and we certainly believe that intelligence is
> not measured by its ability to discover "truth" or its
> infallibly consistent results. Newton's thought process
> was pure genius but known to produce fallacious results
> when you know what Einstein knew at a later time.
> 
> I remember reading Ted Shortliffe's dissertation about
> MYCIN (an early AI medical consultant for diagnosing
> blood-borne infectious diseases) where I learned about
> one use of the term "staff disease", or just "staff" for short.
> In patient care areas there always seems to be an in-
> house infection that changes over time. It changes
> because sick patients brought into the area contribute
> whatever is making them sick in the first place. In the
> second place there is rapid mutations driven by all sorts
> of factors present in hospital-like environments. The
> result is that the local staff is varying, literally, minute
> by minute. In a days time, the samples you took are
> no longer valid, i.e., their day old cultures may be
> meaningless. The underlying mathematical problem is
> that probability theory doesn't really have the tools to
> make predictions when the basic probabilities are
> changing faster than observations can be
> turned into inferences.
> 
> Why do I mention the problems of unstable probabilities
> here? Because new AI uses fancy ideas of correlation
> to simulate probabilistic inference, e.g., Bayesian inference.
> Since actual probabilities may not exist in any meaningful
> ways, the simulations are often based on air.
> 
> A hallmark of excellent human reasoning is the ability to
> explain how we arrived at our conclusions. We are also
> able to repair our inner models when we are in error if
> we can understand why. The abilities to explain and
> repair are fundamental to excellence of thought processes.
> By the way, I'm not claiming that all humans or I have theses
> reflective abilities. Those who do are few and far between.
> However, any AI that doesn't have some of these
> capabilities isn't very interesting.
> 
> For more on reasons why logic and truth are only part of human
> ability to reasonably reason, see
> 
> https://www.yahoo.com/news/opinion-want-convince-conspiracy-theory-100258277.html 
> 
> 
>     -- Jeff Barnett

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