AOPA Pilot Magazine - October 2020 - 89

group of guys with clubs emerging from
the dark behind your car.
Well, it turns out that you're not the ace
programmer you thought you were.
This is the point in the trip where all
of the things that your computer doesn't
know, can't know, may just get you killed.
Chief among those things is the most likely
motives of a group of guys who have placed
highway cones in front of your car and are
now approaching armed with clubs.
Thank goodness, you didn't remove
the steering wheel or the gas pedal.
That's because you're about to do something your computer will never be able to
do-you're going to ad-lib yourself out of
trouble as you jump in the front seat, roll
over the flimsy cones, and leave the highwaymen behind.
There are problems that computers
cannot solve. Even if you work with the
cleverest programmers, even if you have
infinite time and energy and more computing power than could possibly ever exist
in the universe, some things will always
be impossible for a computer to work out.
The explanation of that would take two or
three more articles, so you'll just have to
trust me. But, if you don't trust me, look
up the Halt/Not Halt paradox posited by
Alan Turing.
Without getting too far into the weeds,
it's important to remember that there are
structural differences between human and
artificial intelligence (AI). What we call AI
today is designed around a neural network,
a computer algorithm that imitates certain
functions of the human brain. It contains
virtual neurons in layers, connected to each
other, that more or less pass on information
based on some predefined rules about that
information's importance to the next layer.
The AI "learns," so to speak, by making
mistakes and modifying the rules to come
up with more accurate answers. In short,
neural networks are solving an optimization problem. No one knows in detail how
humans learn, but that's definitely not how
it works. AI needs a lot of training with a lot
of carefully prepared rules and data, which
is very unlike the way human intelligence

works. Neural nets do not build models of
the world; instead they classify patterns,
and pattern recognition can fail with only
small changes made to the pattern.
Most important, unlike your brain, AI
is not good at generalizing what it learns
from one situation and applying that past
experience to formulate solutions to the
current problem.
Now, think of a time you've called on
your experience and creativity to get you
out of a "situation," and then try to write a
set of instructions for a computer to analyze that situation and do what you did.
Without even realizing it you are ad-libbing every time you drive your car, every
time you fly your airplane. Ad-libbing is the
process of real-time creativity and problem
solving and like art, intuition, and imagination is inextricably tied to consciousness,
the most mysterious aspect of our lives.
Where do ideas really come from? Until we
know that, we will never be able to define
its computational rules.
Now that I've buried AI, let's resurrect it to do the work that it's really good
at, which is its ability to think fast. Neural
networks operating on a standard computer perform around 10 billion operations
per second while real human neurons fire
at a rate of about one thousand times per
second. That speed advantage means the
computer excels at extrapolating data
that doesn't have well-understood trends.
Perhaps the point of artificial intelligence is not to make it all that similar to
human intelligence. After all, the most useful machines we have-like airplanes and
cars-are useful exactly because they do
not mimic humans. Instead, we should concentrate on building systems and machines
that can do tasks we are not good at.
Every year that goes by brings remarkable new technologies, made possible
with the help of AI, making our airplanes
safer to fly and, by extension, more useful.
I recently began flying an airplane with
synthetic vision. While on an approach to
minimums, a representation of the runway
appears on the primary flight display long
before we are low enough to see it out the
aopa.org/pilot AOPA PILOT | 89
http://ap-aerospace.com/rent http://www.aopa.org/pilot
AOPA Pilot Magazine - October 2020

Table of Contents for the Digital Edition of AOPA Pilot Magazine - October 2020
