Communications of the ACM

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