IEEE Signal Processing - July 2018 - 7

understanding of the subject matter, and,
we have access to large amounts of data,
therefore, one needs to try "this trick" or
such as speech, imaging, search engines,
"that one" to get the system to work.
and games. We can complement this
These words, and the speaker's honesty,
progress with a more fundamental sci-
are a testament to the aforementioned
ence-based understanding of the process-
fact, namely, that we still lack a full under-
ing abilities and limitations of data-driven
standing of how AI systems work. Experts
designs. That is where signal processing
in signal processing will not hesitate to
approaches can contribute.
"use tricks" as well. However, they will
What is missing in much of the
still strive to understand "why the trick is
ongoing work on AI methods and deep-
needed," and the choice of "which trick to
learning methods is an unequivocal
use" is often guided by some underlying
understanding of the science behind how
theory. It is not uncommon for signal
these systems operate and how they reach
processing papers
their decisions. Why
are certain decisions
Just like signal processing to be turned down if
preferred and in what
is the "science behind our they lack sufficient
theoretical justifica-
ways are they opti-
digital life," we also have
tion. Signal process-
mal or reasonable?
a role to play in developing ing experts can play
There is overreliance
the "science behind the
an important role in
on learning by train-
solidifying the foun-
ing, which, by default,
data-driven revolution."
dational basis for the
biases the machines
ongoing data-driven revolution. Let me
to operate within the boundaries dictated
give you one example from the past.
by the training data. That is one reason,
Consider the nearest-neighbor (NN)
for example, why there are concerns
rule, which assigns feature vectors to the
about using AI assistants in courtrooms
label of their closest neighbors. This is a
to decide the sentences for convicted
simple classification rule, with an intui-
individuals. Most leading scholars and
tive appeal and construction. However,
researchers in the AI field are aware of
a scholar with a signal processing mind
the limitations; they are thoughtful indi-
would want to know more. We would
viduals with strong interest in building up
like to understand why the rule works
their theories on strong foundations. But
and what performance guarantees it has.
many of them do not control the outlets
The beautiful seminal result by Cover
that propagate the "fake news" and the
and Hart (1967) essentially showed that
inflated expectations. Moderation, along
the probability of error of the NN rule
with prudent and proven science, should
is bounded by twice the probability of
be the norm.
error of the Bayes classifier regardless of
For example, in a recent talk I attended
the underlying distribution [4]! In other
on deep learning, I was not surprised by
words, as stated in their paper, "any
how many times the speaker repeated
other decision rule... can cut the prob-
the words "doing this plus a few other
ability of error by at most one half." A
tricks, you get this or that result." When-
remarkable conclusion!
ever I hear the words "a few other tricks"
While the topic of AI fascinates us all,
in a scientific presentation, it raises a red
some of its progress is driven by contribu-
flag in my mind because they convey
tions from real signal processing experts
to me that we still do not have sufficient

IEEE Signal Processing Magazine

|

July 2018

|

like you and me, whether you are working
on learning algorithms, natural language
processing, image processing, computer
vision, or data science. Just like signal
processing is the "science behind our
digital life," we also have a role to play in
developing the "science behind the data-
driven revolution." I agree that there is so
much potential ahead of us, with superla-
tive applications that will make our lives
easier. At the same time, as science often
does, we need to progress with reason
and confidence to develop working sys-
tems away from unrealistic expectations
or even catastrophic consequences. These
ramifications were already foreseen by
the two forward-looking movies Planet
of the Apes and 2001: A Space Odyssey,
which were both released on the same
day in April 1968! One movie looked at
humans and the other at machines. In the
first movie, the human intelligence led
to a catastrophic end, while the second
movie showed what could go wrong with
AI with the "intelligent" HAL machine
having to be turned down before it was
too late!

References

[1] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R.
E. Howard, W. Hubbard, and L. D. Jacke, "Back -
propagation applied to handwritten zip code recogni-
tion," Neural Comput., vol. 1, no. 4, pp. 541-551, 1989.
[2] K. Fukushima, "Neocognitron: A self-organizing
neural network model for a mechanism of pattern recog-
nition unaffected by shift in position," Biol. Cybern.,
vol. 36, no. 4, pp. 193-202, 1980.
[3] D. H. Hubel and T. N. Wiesel, "Receptive fields,
binocular interaction, and functional architecture in the
cat's visual cortex," J. Physiol., vol. 160, no. 1, pp. 106-
154, 1962.
[4] T. M. Cover and P. E. Hart, "Nearest-neighbor pat-
tern classification," IEEE Trans. Inf. Theory, vol. 13,
no. 1, pp. 21-27, 1967.

sp

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Table of Contents for the Digital Edition of IEEE Signal Processing - July 2018

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