🚀 go-pugleaf

I enjoyed reading Clifford Adams' essay, "Artificial Intelligence
and Learning--A Changing Perspective."

I find myself increasingly amused by the debate over "True Understanding."
After reading Feynman's anecdotes about Brazilian physics students
(in _Surely You're Joking, Mr. Feynman_), I now differentiate between
shallow understanding and deep understanding.  I believe there is no
theoretical limit to the depth to which one can achieve understanding.

I find the word "understanding" a bit problematical, because I don't
fully understand what the word means.  I prefer the word "comprehension"
because its etymology is clearer.  "Comprehend" means "to capture with".
I capture knowledge by constructing a mental model that resembles
(in both structure and behavior) the object of my contemplation.
Often, I need to construct a physical model to play with, or a computer
model to interact with before I can get a good mental picture.

Using Lisp as a metaphor, I measure the depth of my understanding
by the number of levels of "chunking" or decomposition between my
overall model, and its atomic constituents.  I also measure my
depth of understanding by the number and richness of concrete
instances of an abstract model.  Here is where computers and AI
are a bit behind humans.  When it comes to models, analogies,
metaphors, and parables, computers are just getting started.

Symbolic representation is in its infancy.  For me to understand
something well, I need multiple interchangeable models.  I need
a verbal or mathematical representation, which I can manipulate
formally, and I also need a visual or geometric representation
which I can manipulate like a cartoon in my head (or on my computer
graphics display).  To my mind, the next great advance in computer
understanding comes with the computer's ability to project an
animated color image that graphically represents (re-presents)
the structure and behavior of a system by transforming a symbolic
(i.e. ASCII) representation into a visual (or audible) form.

Bi-directional information-preserving transformations are the
central tool of modeling.  We see examples in Fourier Transforms,
Analytical Geometry, Digital Signal Processing, Holograms, Analog
Computers, and various Duality Theories.

--Barry Kort