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About Deep Learning, Intuition and Thinking

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Abstract

“Intuition is nothing more and nothing less than recognition”, is a famous quote by Herbert Simon, who received the Turing Award in 1975 and the Nobel Prize in 1978. As explained by Daniel Kahneman, another Nobel Prize winner, in his book Thinking, Fast and Slow [2], and during his talk at Google in 2011: “There is really no difference between the physician recognising a particular disease from a facial expression and a little child learning, pointing to something and saying doggie. The little child has no idea what the clues are but he just said, he just knows this is a dog without knowing why he knows”. These milestones should be used as a guideline to help understanding decision making in recent AI algorithms and thus their transparency. [CONTINUE]
“Intuition is nothing more and nothing
less than recognition” [1], is a famous
quote by Herbert Simon, who received
the Turing Award in 1975 and the Nobel
Prize in 1978. As explained by Daniel
Kahneman, another Nobel Prize winner,
in his book Thinking, Fast and Slow [2],
and during his talk at Google in 2011
[L1]
: There is really no differ enc e
between the physician recognising a
particular disease from a facial expres-
sion and a little child learning, pointing
to something and saying doggie. The
little child has no idea what the clues are
but he just said, he just knows this is a
dog without knowing why he knows”.
These milestones should be used as a
guideline to help understanding deci-
sion making in recent AI algorithms and
thus their transparency.
Most of the recent progress in artificial
intelligence (AI) has been on recognition
tasks, and this progress has been
achieved through the adoption of deep
learning (DL) methods. The AI renais-
sance started in 2012 when a deep neural
network, built by Hinton’s team, won the
ImageNet Large Scale Visual
Recognition Challenge. Deep learning
methods have been, and still are, the driv-
ing force behind this renaissance. Like
the little child mentioned by Kahneman,
a state-of-the-art deep neural network is
able to look at something and say “dog-
gie”, without knowing why it knows. In
other words, the task of recognition,
especially in computer vision, has been
solved by DL methods with a form of
artificial intuition. And this is not a sur-
prise given that important researchers
such as Simon have accepted the equiva-
lence between intuition and recognition.
Even if many people feel a sense of
magic talking about DL, the research
conducted in recent years has proven
that there is no magic at all in intuition
and the same holds for DL.
Within the discipline of psychology and
decision making, expert intuition has
been discussed a lot in recent years,
dividing researchers into believers and
skeptics. However, after six years of
discussion, a believer, Gary Klein, and a
skeptic, Daniel Kahneman, wrote an
important paper in 2009 whose subtitle
was “A failure to disagree”. Trying to
answer the question When can we trust
intuition? they agreed on a set of condi-
tions for trustable intuitive expertise.
Am ong t hese condi tions , the mos t
important ones are:
an environment that is sufficiently
regular to be predictable;
an opportunity to learn these regular-
ities through prolonged practice.
I bel ieve mos t of the res earche rs
working on DL would agree that those
are also good conditions for the ques-
tion: When can we trust deep learning?
In fact, in order for a DL method to
learn, we need a large training set (pro-
longed practice) and this set must be
representative of the application sce-
nario in which the environment must be
sufficiently regular to be predictable.
What degree of transparency can we ask
for from DL methods? Following the
metaphor between DL and intuition, we
ca n l ook at what Simon said ab out
human recognition capabilities: “we do
not have access to the processes that
allow us to recognise a familiar object
or person”. I believe the same is true for
DL. Even if we can monitor the flow of
information in a deep neural network,
we don’t understand the “process”.
Ne verthe less, DL me thods can be
transparent in some terms: knowledge
about the used training set and an in-
depth analysis of the statistical out-
co mes can help in mak ing them
trustable for a specific task, in a specific
context at a specific time.
As humans should not rely on intuition
for all decisions, DL methods should be
used as part of more complex AI sys-
tems that also involve non-intuitive
pr oces ses. Kah nema n has u sed the
metaphor of two systems in his research
about human thinking:
System 1: fast, automatic frequent,
emotional, stereotypic, unconscious.
System 2: slow, effortful, infrequent,
logical, calculating, conscious.
It is not by c hance that Deep Mind
AlphaGo, the program that in 2016
defeated South Korean professional Go
player Lee Sedol, combines DL with
Monte Carlo tree search. As Michael
Wooldridge, chair of the IJCAI Awards
Committee said, “AlphaGo achieves
what it does through a brilliant combi-
nation of classic AI techniques as well
as the state-of-the-art machine learning
techniques that DeepMind is so closely
as sociat ed with . Fol lowing our
metaphor, AlphaGo is a good example
of collaboration between System 1 and
System 2. AlphaGo uses DL to provide
an intuitive estimation of the likelihood
that the next stone will be placed in a
specific place and of the final outcome
of the game given the current status.
Ho wever, th e fina l deci sion about
where to put a stone is made using the
Monte Carlo tree searc h ( Alph aGo
System 2). In other words, AlphaGo
uses the outcome of artificial intuition
implemented using DL methods (its
System 1) but takes decisions with log-
ical reasoning (its System 2).
The few examples discussed here show
that psycho logy c an help i n under-
standing AI. When Simon was con-
ducting his research, psychology and AI
were closely linked. This is a link that
we need to revisit.
Link: [L1] https://kwz.me/hdj
References:
[1] H. A. Simon: “What is an ‘explana-
tion’ of behavior?” Psychological
science 3.3, 150-161, 1992.
[2] D. Kahnemann: “Thinking, Fast
and Slow”, Farrar, Straus and
Giroux, 2011.
Please contact:
Fabrizio Falchi, ISTI-CNR, Italy
+39 050 315 29 11
Fabrizio.falchi@cnr.it
ERCIM NEWS 116 January 201914
Special Theme: Transparency in Algorithmic Decision Making
About Deep Learning, Intuition and Thinking
by Fabrizio Falchi, (ISTI-CNR)
In recent years, expert intuition has been a hot topic within the discipline of psychology and decision
making. The results of this research can help in understanding deep learning; the driving force behind
the AI renaissance, which started in 2012.
... Specifically, we encode all the visual and textual descriptors extracted from the videos into (surrogate) textual representations that are then efficiently indexed and searched using an off-the-shelf text search engine." [74]. Abstract: ...
Technical Report
Full-text available
The Artificial Intelligence for Multimedia Information Retrieval (AIMIR) research group is part of the NeMIS laboratory of the Information Science and Technologies Institute ``A. Faedo'' (ISTI) of the Italian National Research Council (CNR). The AIMIR group has a long experience in topics related to: Artificial Intelligence, Multimedia Information Retrieval, Computer Vision and Similarity search on a large scale. We aim at investigating the use of Artificial Intelligence and Deep Learning, for Multimedia Information Retrieval, addressing both effectiveness and efficiency. Multimedia information retrieval techniques should be able to provide users with pertinent results, fast, on huge amount of multimedia data. Application areas of our research results range from cultural heritage to smart tourism, from security to smart cities, from mobile visual search to augmented reality. This report summarize the 2019 activities of the research group.
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