Sadly, artificially intelligent systems, like ChatGPT, are becoming oracles in education. And, that is dangerous. It was always astounding. How many question authority. The typical secondary school student will not heed an instructor’s guidance concerning sobriety, setting a personal curfew, or other acts of a self-disciplined nature. And, they will do what authority says cannot be done, questioning it openly and freely. However, it an instructor poses a tough yet solvable problem and says “You cannot do this!” The typical student will not accept the challenge. Which is a saddening commentary.
And, we as instructors are well-aware of that scenario where
a STEM student simply reports what the calculator read. Albeit, the result
given is many magnitudes of ten greater or smaller than it should be. Seeing, a
student state that a one-kilogram mass will reach the ground when dropped from
a height of ten meters in 3.0 x 10-8 seconds is always great for a
laugh. But when, he says, “Well, that is what the calculator read.” It should
solicit tears. Along with, that standard question every instructor has asked
with a somber tone, “Does that seem practical, son?”
Well, it is said that everything is relative. The same is
true with artificially intelligent systems who dazzle us with their reasoning
speed and amazing insightfulness, at times. As, a large cohort of a generation
of students are calculator-delimited. So,
will it be with artificial intelligence. As, it was with the calculator.
Some of whom are AI-delimited will build the next generation of automatons
built atop machine learning algorithms. With the ceiling of the last generation
of AI systems resting as a tight-fitting intelligence cap upon their collective
head, they will be incapable of surpassing that previous era’s average general
intelligence level. Albeit, some bright spots might shine through. And as such,
a generational and recursive descent shall occur. Mankind thinking it has
become eversowise will be a world of utter fools.
For example, the inverted orientation of rights and lefts
used among modern computing literature has proliferated in recent years. Could
you find the right of the terminal in front of you? If, it is facing you. Big
hint, it is on your left-side. If, you are convinced that it is on your right
side. You might be classifiable by a older psychoeducational standard as irreversibly
impaired. But, the computing documentation reads backward. So, the “impaired”
orientation must be correct? Or, is it? Could the great mathematicians and
computer scientist who gave us these modern marvels be classified as such?
Mankind anthropomorphizes inanimate objects as a means of
communication. We communicate through these vessels called computers, smart
phones, and the like. So, right on left and left on right simply is more
natural. The inverted orientation with right on right and left on left is
traditionally associated with irreparable and degenerative cognitive disorders.
Plus, regular communication promotes and enhances the intellectual response.
Without it, men become dulled.
As the mouse clicks, the tail extends in the direction of
the terminal and it faces one’s palm. So, where is the right rear of the mouse?
Why is that called by so many “the left mouse button.” Because, Microsoft’s
documentation said and says so. Memories of working on a OpenLook windowing
system by SUN Microsystems, during ’90 – ’91, suggest that their documentation
followed the more natural communicative standard, right on left and left on
right.
We, humanity, are quite capable of writing algorithms who
can perform an adaptable linear boundary analysis of medical datasets,
partitioning healthy from borderline and ill groups. And thereby, doing the
work of a sagacious human physicians in diagnosing all manner of disease,
offering treatment regimens, and giving accurate prognoses. But, the same
cohort of computing geniuses cannot properly identify the actual right and left
of the smart phones whom they use.
One of the best college courses ever personally taken was
one in artificial intelligence at a state school in the Heartland. It was a
pattern recognition course. Whom was taught in the old-fashioned way, zero
calculators or computers. All of the necessary matrix manipulations for the
problem sets were done by hand. Which, by the way, evinced the power of the
algorithms used in machine learning. Because, very elementary operations with
simple numbers made some very powerful statements about the complex problems
that were laid before our class. It does not always take a CRAY, Deep Blue, or
a quantum computer. And, that seasoned professor did a phenomenal job at
teaching the subject. The same course was enjoyed again online at Stanford SEE
with professor Ng. The material was exactly the same, except the numbers used
for the problem sets. So, we could complete the exercises on the assessments
using pencil and paper and not tools like Octave or Mathematica. Thus, we could
gain a greater understanding of the material and confidence in our own
reasoning ability. And, that is truly hands on learning.
