IEEE Signal Processing - July 2018 - 6
their "intelligence" because of their high
We need to be prudent about how we
degree of specialization. For instance,
name our technologies because names
the powerful AlphaGo game machine
matter, and they may end up conveying
that defeated the world champion in the
exaggerated expectations. The "intelli-
game of GO will perform miserably at
gent" devices of today, and the accom-
another game, no matter how simple
panying "deep" learning structures, are far
it is. This is because the machine was
from competing with the cognitive abili-
designed for the GO game. Likewise, an
ties and abstract thinking of the human
AI system designed to win chess games
mind. Humans can learn and create
will perform poorly if we change some
abstract concepts as well as apply them
simple rules of the game such as limit-
across different tasks whether in solving a
ing the movements of the queen piece or
puzzle, playing a game of chess, writing a
perhaps reversing the colors of the black
piece of poetry, or proving a mathematical
and white squares. "Intelligent" machines
theorem. Real abstract reasoning is beyond
are only good for the specific tasks they
the reach of current AI systems. HAL
have been designed for, and they will
9000, the fictional artificial intelligent
achieve that level of "excellence" only if
character in the 1968 movie 2001: A Space
they have been trained well enough with a
Odyssey, was capable of multitasking and
massive amount of data. Many would per-
able to perform speech recognition, facial
ceive them as "intel-
recognition, lip read-
ing, and reasoning and
many of the methodologies ligent" only because
they can perform their
could even understand
and techniques driving
tasks much faster than
emotions. But that
machine learning and
normal human abili-
was fiction then and
deep learning today have
ties, which is a fallacy.
continues to be fiction
been part of the signal
Certainly, a car travel-
today. We regularly
processing repertoire
ing at 100 km/h is not
witness failures that
"intelligent" because it
are characteristic of
for decades.
is faster than a human.
what could go wrong
It is simply much more efficient in this
with current technologies, with some seri-
particular task. Extrapolating from "being
ous outcomes such as self-driving cars
good at something" to "being intelligent
being involved in fatal accidents in 2017,
about everything" is the problem we are
Microsoft's chatbot being easily manipu-
facing today with the media frenzy around
lated to posting racist comments in 2016,
AI and the learning methods powering
and even the ID facial recognition system
them. Fortunately, scholars and scientists
on the new iPhone X being fooled by a
driving the field are far more reasoned in
three-dimensional-printed face mask in
their approaches and expectations. They
2017. These are relatively recent examples,
build their understanding one step at a
which raise questions about the reliability
time and benefit from a fertilization of
of even the most up-to-date technologies.
ideas across fields.
What limits the "intelligence" of the
Consider, for example, the widely
machines we are designing today is the
successful convolutional neural networks
fact that they can only be as good as the
(CNNs), which constitute one of the
data used to train them. These devices
main drivers in the ongoing data revo-
spot and react to patterns they have
lution since their use in 1989 by LeCun
learned from the training data. If, for
and his collaborators in the recognition
example, a self-driving car was never
of handwritten zIP codes [1]. CNNs are
trained to recognize an individual cross-
an outgrowth of an architecture known
ing the street in the dark, it will most
as "neocognitron," which was developed
likely not learn how to respond to such
a decade earlier by Fukushima (1980) [2].
an event or may respond in some errat-
This structure was, in turn, motivated
ic manner. An "intelligent" machine
by the discoveries in the 1960s of the
should at least have the ability to rea-
corecipients of the 1981 Noble Prize in
son and make logical deductions based
Medicine David H. Hubel (1926-2013)
on the circumstances. But even then,
and Torsten N. Wiesel (1924-present) for
machines would continue to be limited in
6
IEEE Signal Processing Magazine
|
July 2018
|
their work on understanding the visual
cortex of cats [3]. Since their launch,
CNNs have revolutionized image pro-
cessing and computer vision applica-
tions as well as speech processing and
other fields. What I find most interesting
about this story is not the many wonder-
ful applications that have emerged since
then, such as automatic photo tagging,
scene understanding, and face recogni-
tion. For example, police in China have
started using sunglasses connected to the
Internet and equipped with facial recogni-
tion technology to spot criminal suspects
in crowds. All of these applications do
not impress me because signal process-
ing experts like you and me understand
well the algorithms that drive the systems
and how these systems can be trained
given sufficient data. What amazes me
the most about the origins of the CNN
approach is something completely dif-
ferent. I stand in awe at seeing the prog-
ress we have achieved today from simply
studying the visual cortex system of the
cat! Just imagine how much more prog-
ress we can achieve by studying the
immense biodiversity that lives around
us, including tiny flies!
Signal processing to the rescue
There are immense opportunities for sig-
nal processing to empower AI systems,
simply because we are the discipline that
specializes in extracting information from
data and in understanding data, represent-
ing data, and projecting into the future.
We are masters of information process-
ing. Actually, many of the methodologies
and techniques driving machine learning
and deep learning today have been part
of the signal processing repertoire for
decades, including stochastic algorithms,
the backpropagation algorithm, neural
networks, Markov models, mixture mod-
els, Bayesian inference, classification, etc.
The fundamental science driving most AI
innovations is largely the same we have
had from years past. What is pushing AI
further today is the availability of mas-
sive data to train these machines, with the
accompanying computing power neces-
sary to carry out the processing. Much
of the progress is data and computing. It
is no wonder that the most successful AI
applications today are in domains where
Table of Contents for the Digital Edition of IEEE Signal Processing - July 2018
Contents
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