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AI Vs Human Brain
“AI Is No Match for the Quirks of Human Intelligence”. “We may sometimes
behave like computers, but more often, we are creative, irrational, and not
always too bright”.
The source is the MIT Reader Press, and the author is Herbert L. Roitblat.
He is Principal Data Scientist at Mimecast. He is the author of “Algorithms
Are Not Enough: Creating General Artificial Intelligence” from which this
article is adapted. I provide a clinical biopsychosocial framework.
“We’ve succeeded in creating machines that can solve specific fairly
narrow problems — “smart” machines that can diagnose disease, drive
cars, understand speech, and beat us at chess — but general intelligence
remains elusive”. I believe that there are multiple subtypes of IQ. It is the
human brain that creates these and other instruments and tools!
AI does not reveal insight. I refer to this IQ subtype as Intrapersonal. He
offers the following example of Archimedes. Legend has it that he runs
naked thru the streets of Syracuse and solves involving the contents of a
crown adorning a statue. He solves the puzzle via insight while running.
AI operates via algorithms created by humans. While heuristics involve
creative short cuts. “Relatively little is known about how we solve insight
problems”. He offers that studies conducted in the laboratory do not
represent the natural world.
“Unlike computers, we are relatively limited in what we can keep in active
memory at one time” which we also refer to as working. Most of us only
retain about 7 to 9 units. Although, we believe that the capacity of long-term
is unlimited. Some can utilize chunking to enhance retrieval with greater
accuracy.
“Computational intelligence has focused on the kind of work done by a
deliberate system, but the automatic system may be just as or more
important. And it may be more challenging to emulate in a computer”.
“Hardly a day goes by without a call for some kind of regulation of artificial
intelligence, either because it is too stupid (for example, face recognition)
or imminently too intelligent to be trusted. But good policy requires a
realistic view of what the actual capabilities of computers are and what they
have the potential to become”.
“If all that is necessary for a machine learning system is to engage its
analytic capabilities, then the machine is likely to exceed the capabilities of
humans solving similar problems. Analytic problem solving is directly
applicable to systems that gain their capabilities through optimization of a
set of parameters. On the other hand, if the problem requires divergent
thinking, commonsense knowledge, or creativity, then computers will
continue to lag behind humans for some time”.
More importantly for me, I remain curious as to the motivations and aims of
the creators when creating various tools and instruments. The prime
example for me is the creation of social platforms such as the ones
managed by Facebook. Do the benefits far outweigh the liabilities? I refer
to this as executive and moral reasoning. These debates are necessary.
Richard G Kensinger, MSW