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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
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 In a days time, the samples you took are no longer valid, i.e., their day old cultures may be meaningless.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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