It does not take a genius to recognize that artificial
intelligence is going to be one of the hot topics for 2017. AI is suddenly everywhere from phones that
answer your questions to self-driving cars. Once a technology achieves prominence in the consumer
space, it moves into the mainstream of applied fields, even for fields that are
slow to adopt technology.
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| Predictions 2017. Source: http://www.psychics.com/blog/a-brief-history-of-the-crystal-ball/ |
AI has also caught the imagination of people who like to
worry about the future. Are we going to
achieve some kind of singularity where we can upload human consciousness to a
computer or is Skynet going to determine that people are simply superfluous
and work to destroy them?
The prospects for either of these worrisome events seems
extraordinarily remote. I think that
these concerns rest largely on a deep misunderstanding of just what intelligence
is. Intelligence is often used to
describe something akin to cognitive processing power. And processing power of a certain kind that
represents the cultural achievements of Western Civilization (e.g., school, business
success).
Intelligent people/things generally are those that are
better able to think. The implication is
that some people are better than others at thinking—they are generally more
intelligent. This is the kind of idea
that underlies the concept of IQ (intelligence quotient). IQ was originally invented to predict how
well children would do in school.
This notion of general intelligence, one that is intended to
measure how well people think overall, has proven to be elusive. Although there is some correlation between
performance on one cognitive test and another, that correlation is not particularly high. Moreover, the correlation may be more indicative
of the similarity between cognitive tests and tasks than of shared cognitive
abilities. The correlation may be a
result of the kind of situations where we attribute intelligence (for example,
multiple classroom activities or business) and not be general at all. Even among so-called intellectual activities,
the correlation may be absent.
The same applies to artificial intelligence. We don’t have any generally intelligent
machines. So far, artificial
intelligence machines are rather narrowly specialized. Building a great chess playing machine is
unlikely to be any use to winning at Jeopardy. Intelligence, both human and
natural, seems to be largely domain specific.
If the evidence were stronger for general human intelligence, I might be
more willing to predict that kind of success in general artificial intelligence, but so
far, the evidence seems strongly to contrary.
Further, the problems that seem to rely most on intellectual
capacity, such as chess playing or Jeopardy answering, turn out to be the
easier problems to solve with computers.
Problems that people find natural, such as recognizing a voice or a face
turn out to be more difficult for computers.
It is only recently, that we have made progress on addressing such
problems with computers.
Chess playing and Jeopardy answering by computers uses
approaches that are different from those used by humans. The differences are often revealed in the
kind of mistakes people and machines make.
IBM’s Watson beat the human Jeopardy players handily,
but it made certain mistakes that humans would not (for example, asserting that
Toronto was a US city). The difference
in mistakes (artificial vs. natural stupidity?) is not a sign of AI’s failure,
just that the computer is doing things in a different way than a smart person
would.
Similarly, the kinds of mistakes people make tell us
something about how they form their intelligence. For example, people will give different
answers to the same question, depending on how precisely it is asked. In a seminal study by Kahneman and Tversky,
participants were asked to choose between two treatments for 600 people
infected with a deadly disease.
If the
people were given a positively framed choice, 72% chose Treatment A:
- Positive Frame: With Treatment A 200 people will be saved. With Treatment B, there is a 33% chance of saving all 600 and a 66% chance of saving no one.
On the other hand, if the situation was described more
negatively, only 22% chose Treatment A:
- Negative Frame: With Treatment A, 400 people will die. With Treatment B, there is a 33% chance that no one will die and 66% chance that all 600 people will die.
With both sets of alternatives, 200 people are predicted to
live and 400 people will die under treatment A, and under Treatment B there is
a 33% chance that everyone will survive and a 66% chance that no one will
survive. Logically, people should give the
same answer to both, but instead they are affected by the pattern of how the
question was asked. The first pair match
a positive pattern and the second pair match a negative pattern, and thus lead
to different choices.
People have a tendency to jump to conclusions based on first
impressions or other patterns that they analyze. In fact, the root factor underlying human
cognition seems to be pattern recognition.
People see patterns in everything.
The gambler’s fallacy, for example, relies on the fact that people see
patterns in random events. If heads come
up six times in a row, people are much more likely to think that the next flip
will result in tails, but in reality heads and tails are equally likely.
Humans evolved the ability to exploit patterns over millions
of years. Artificial intelligence, on
the other hand, has seen dramatic progress over the last few decades because it
is only recently that computer software has been designed to take advantage of
patterns.
People are naturally impressionistic intuitive
reasoners. Computers are naturally
logical and consistent. Computers
recognize patterns, and thereby become more intelligent, to the extent that
these patterns can be accommodated in a logical, mathematical framework. Humans have a difficult time with logic. They can use logic to the extent that it is
consistent with the patterns that are perceived or are “emulated” by external
devices. But logic is difficult to learn
and difficult to employ consistently.
Every increase in human intelligence over the last several
thousand years, I would argue, has been caused by the introduction of some
artifact that helped people to think more effectively. These artifacts range from language itself,
which makes a number of thinking processes more accessible to things like checklists
and pro/con lists, which help make decisions more systematic, to mathematics.
In contrast, the kinds of tasks that people find easy (and
challenge computers), such as recognizing faces are apparently a property of
specific brain structures, which have evolved over millions of years. Other aspects of what we usually think of as
intelligence are much more recent developments, evolutionarily speaking, over a
time frame of, at most, a few thousand years.
Our species, Homo sapiens, has only been around for about 150,000
years. There have been quite a few
changes to our intellectual capacity over that time, particularly over the last
few thousand years. The cave paintings at Lascaux in France, among the earliest known artifacts of human
intelligence, are only about 20,000 years old.
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| An example of Face recognition by humans. Although upside down, this face is easily recognizable, but there is something strange about it. See below. |
Computer face-recognition systems do not yet have the same
capacity as human face recognition,
but the progress in computerized face recognition has come largely from
algorithms that exploit invariant measurements of the faces (such as the ratio
of the distance between the eyes relative to the length of the nose).
The birth of self-driving cars can be attributed to the
DARPA Grand Challenge. DARPA offered a million dollar prize for a
self-driving car that could negotiate, unrehearsed, a 142 mile off-road course through
the Mojave desert. The 2014 competition
was a complete failure. None of the
vehicles managed more than 5% of course.
In 2015, on the other hand, things were dramatically different. Stanley, the Stanford team’s car negotiated
the full course in just under 7 hours. A
major source of its success, I believe, lay in the sensors that were deployed
and in the way information from those sensors was processed and pattern analyzed. Core to the system were machine learning
algorithms that learned to avoid obstacles from example incidents in which
human drivers avoided obstacles.
Enhancement in computer intelligence similarly has come from
hacks and artifacts. Face recognition
algorithms, navigational algorithms, sensors, parallel distributed computing,
and pattern recognition (often called machine learning) have all contributed to
the enhancement of machine intelligence. But, like the elusiveness of human general intelligence, it is dubious
that we will see anything resembling general intelligence in computers.
Winning a Noble prize in physics is no
guarantee that one can come to sensible conclusions about race and intelligence, for example. Being able to answer Jeopardy questions is
arguably more challenging than winning chess games, but it is not the same
thing as being able to navigate a vehicle from Barstow, California to Primm,
Nevada. Computers are getting better at
what they do, but the functions on which each one is successful are still narrowly
defined. Computers may increasingly take
over jobs that used to require humans, but they are unlikely, I think, to
replace them altogether. Ultimately, computers, even those endowed with
artificial intelligence, are tools for raising human capabilities. They are not a substitute for those
capabilities.
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| The same face turned right-side up. In the view above, the mouth and eyes were upright while the rest of the face was upside down. The effect is readily seen when the face is right-side up, but was glossed over when upside down. We recognize the pattern inherent in the parts and in the whole. Source: http://thatchereffect.com/ |
