chine be given the intelligence to make
such a determination on its own?
Health-care institutions increasingly must be able to identify authentically
human patients’ EHRs. In light of this
trend, hackers might want to generate
synthetic patient identities to exploit
the data of real patients, hospitals,
and insurance companies. For example, a hacker might want to falsely increase a particular hospital’s congestive heart failure (CHF) 30-day
readmission rate, a measure of quality.
Medicare also uses it to evaluate U.S.
hospitals—by posting into the hospital’s
database synthetic-patient identities associated with CHF readmissions.
Before synthetic patient identities become a public health problem, the legitimate EHR market might benefit from
applying Turing Test-like techniques to
ensure greater data reliability and diagnostic value. Any new techniques must
thus consider patients’ heterogeneity
and are likely to have greater complexity
than the Allen eighth-grade-science-test
is able to grade.
References
1. Baytas, I., Xiao, C., Zhang, X., Wang, F., Jain, A., and
Zhou, J. Patient subtyping via time-aware LSTM
networks. In Proceedings of the 23rd SIGKDD
Conference on Knowledge Discovery and Data Mining
(Halifax, NS, Canada, Aug. 13–17). ACM Press, New
York, 2017, 65–74.
2. Kartoun, U. A methodology to generate virtual
patient repositories. arXiv Computing Research
Repository, 2016; https://arxiv.org/ftp/arxiv/
papers/1608/1608.00570.pdf
Uri Kartoun, Cambridge MA
I Am Not a Number
Solon Barocas’s and danah boyd’s Viewpoint “Engaging the Ethics of Data Science in Practice” (Nov. 2017) did well to
focus on ethics in data science, as data
science is an increasingly important
part of everyone’s life. Data, ethics, and
data scientists are not new, but today’s
computational power magnifies their
scale and resulting social and economic
influence. However, by focusing narrowly on data scientists, Barocas and boyd
missed several important sides of the
ethics story.
The disjoint they identified between
CARISSA SCHOENICK ET AL.’S article “Moving Beyond the Turing Test with the Allen AI Science Challenge” (Sept. 2017) got me thinking … less
about the article itself than about the
many articles on artificial intelligence
we see today. I am astonished that
people who know what computers
can do, and, especially, how they do
it, still think we (humankind) will ever
create a rational being, much less that
the day is near.
I see no such sign. A program that
can play winning chess or Go is not one.
We all knew it would happen sooner or
later. We are talking about a large but finite set of paths through a well-defined
set. Clever algorithms? Sure. But such
things are the work of engineers, not of
the computer or its programs.
Siri is no more intelligent than a
chess program. I was indeed surprised,
the first time I tried it, by how my
iPhone seemed to understand what I
was saying, but it was illusory. Readers
of Communications will have some notion of its basic components—
something that parses sound waves from
the microphone and something that
looks up the resulting tokens—and if
a sound token is within, say, 5% of the
English word … you know what must
be happening. The code is clever, that
is, cleverly designed, but just code.
Neither the chess program nor Siri
has awareness or understanding. A
game-playing program does not know
what a “game” is, nor does it care if it
wins or loses. Siri has no notion of what
a “place” is or why anyone would want
to go there.
By contrast, what we are doing—
reading these words, asking maybe,
“Hmm, what is intelligence?” is some-
thing no machine can do. We are con-
sidering ideas, asking, say, “Is this
true?” and “Do I care?”
That which actually knows, cares,
and chooses is the spirit, something
every human being has. It is what dis-
tinguishes us from animals, and from
computers. What makes us think we
can create a being like ourselves? The
leap from artificial to intelligence
could indeed be infinite.
Arthur Gardner, Scotts Summit, PA
Carissa Schoenick et al. (Sept. 2017)
described an AI vs. eighth-grade science test competition run in 2015 by
the Allen Institute for Artificial Intelligence. The top solution, as devised by
Chaim Linhart, predicted the correctness of most answers through a combination of gradient-boosting models
applied to a corpus. Schoenick et al.
suggested that answering eighth-grade-level science-test questions would be
more effective than Alan Turing’s own
historic approach as a test of machine
intelligence. But could there be ways to
extend the Turing Test even further, to,
say, real-life scenarios, as in medicine?
Consider that electronic health records (EHRs) collected during a typical clinical-care scenario represent a
largely untapped resource for studying
diseases and associated co-occurring
diseases at a national scale. EHRs are a
rich data source, with thousands of data
elements per patient, including structured elements and clinical notes, often
spanning years. Because EHRs include
sensitive personal information and are
subject to confidentiality requirements,
accessing such databases is a privilege
research institutions grant only a few
well-qualified medical experts.
All this motivated me to develop a
simple computer program I call EM-RBots to generate a synthetic patient
population of any size, including
demographics, admissions, comor-bidities, and laboratory values. 2 A synthetic patient has no confidentiality
restrictions and thus can be used by
anyone to practice machine learning
algorithms. I was delighted to find one
of my synthetic cohorts being used by
other researchers to develop a novel
neural network that performs better than the popular long short-term
memory neural network. 1
In the EHR context, though a human physician can readily distinguish
between synthetically generated and
real live human patients, could a ma-
A Leap from Artificial to Intelligence
DOI:10.1145/3168260