er hand, I’d be happy to discuss, say,
romantic poetry, the implications of
China’s one-child policy, or the cur-
rent financial crisis, and so on. I can’t
pass a Turing Test, but so what? I can
still be a very interesting conversa-
tionalist. I don’t need to have actually
experienced the pain of hitting my
hand with a hammer to talk about it.
Just like priests don’t need to be mar-
ried to counsel about-to-be-married
young people about married life. They
know about the trials and tribulations
of married life secondhand by having
talked about it all their lives to people
who are married. So, while their mod-
el of marriage might not be as perfect
as the model married people have, it
is a good enough approximation to
provide real insight about marriage.
Think of me in those terms.”
Computers of the future, even if
they never pass a Turing Test, will
potentially be able to see patterns
and relationships between patterns
that we, with all our experience in
the world, might simply have missed.
The phenomenal computing capacity
of computers, along with ever-better
data capture, storage, retrieval, and
processing algorithms, has given
rise to computer programs that play
chess, backgammon, Go, and many
other highly “cognitive” games as
well, or better, than most humans.
They compose music and recognize
speech, faces, music, smells, and
emotions. Not as well as the best hu-
mans—not yet—but this is only the
beginning. And just as the early fail-
ures of AI contributed to our deeper
understanding of the true complexity
of human cognition, these programs
force us to rethink our anthropocen-
tric ideas on the uniqueness of our
cognitive skills. But this rethinking
should not be a cause for a concern.
That a mass of 100 billion slow and
imprecise neurons could organize
themselves over the course of many
millions of years in such a way as to
produce human cognition is an amaz-
ing outcome of evolution. However,
there is no reason to believe this is
the only way to achieve cognition.
Understanding human cognition and
achieving artificial cognition are two
separate endeavors, and, even if each
can inform the other, they should not
The goal of building a machine
able to pass a Turing Test will long
remain elusive and probably never be
achieved. But many other great challenges lie ahead, greater even than
flawlessly imitating human cognitive
behavior down to the last typing mistake. The degree to which progress
can be made in AI will be in direct relation to the degree to which the problems to be solved can be represented
cleanly and unambiguously. One of
the next great challenges of AI will
be the development of computer programs designed to discover and prove
elegant new mathematical theorems
worthy of publication in mathematics journals, not because they were
done by a computer but because the
mathematics itself will be worthy of
publication. Other challenges will be
development of programs that make
use of the oceans of data now available to find new relationships between diseases and human behavior
or the environment. Yet others will be
programs that can look at two different pictures and find their analogous
It is time for the Turing Test to take a
bow and leave the stage. The way for-
ward in AI does not lie in an attempt
to flawlessly simulate human cogni-
tion but in trying to design comput-
ers capable of developing their own
abilities to understand the world and
in interacting with these machines in
a meaningful manner. Researchers
should be clearer about the distinc-
tion between using computers to un-
derstand human cognition and using
them to achieve artificial cognition,
meaning we need to revise our long-
held notions of “understanding.”
Understanding is not something only
humans are capable of and, as com-
puters get better at representing and
contextualizing patterns, making
links to other patterns and analyzing
these relationships, we will be forced
to concede that they, too, are capable
of understanding, even if that under-
standing is not isomorphic to our
own. Few people would argue that
interacting with people of other cul-
tures does not enrich our own lives
and way of looking at the world. In
a similar, if not identical way, in the
not-too-distant future, the same will
be true of our interactions with com-
This work was funded in part by a
grant from the French National Research Agency (ANR-10-065-GETPI-
MA). Thanks to Dan Dennett and, especially, to Melanie Mitchell for their
insightful comments on an earlier
draft of this article.
1. Bell, g. and gemmell, J. Total Recall: How the
E-Memory Revolution Will Change Everything. dutton,
New York, 2009.
2. dreyfus, H.L. What Computers Can’t Do. Harper &
Row, New York, 1979.
3. Ferrucci, d., Brown, E., Chu-Carroll, J., Fan, J., gondek,
d., Kalyanpur, A., Lally, A., murdock, J., Nyberg, E.,
Prager, J., Schlaefer, N., and Welty, C. Building Watson:
An overview of the deepqA project. AI Magazine 31, 3
4. French, R.m. dusting off the Turing Test. Science 336,
6078 (Apr. 13, 2012), 164–165.
5. French, R.m. Subcognition and the limits of the Turing
Test. Mind 99, 393 (1990), 53–65.
6. French, R.m. Subcognitive probing: Hard questions
for the Turing Test. In Proceedings of the 10th Annual
Cognitive Science Society Conference (montréal, July).
LEA, Hillsdale, NJ, 1988, 361–367.
7. French, R.m. The Turing Test: The first 50 years.
Trends in Cognitive Sciences 4, 3 (mar. 2000), 115–121.
8. Haugeland, J. Artificial Intelligence, The Very Idea.
mI T Press, Cambridge, mA, 1985.
9. Hayes, P. and Ford, K. Turing Test considered harmful.
In Proceedings of the 14th International Joint
Conference on Artificial Intelligence (montréal).
morgan Kauffman Publishers, San Francisco, 1995,
10. Hofstadter, d.R. Metamagical Themas. Basic Books,
New York, 1985, 631–665.
11. Holland, J. H. Adaptation in Natural and Artificial
Systems. University of michigan Press, Ann Arbor, mI,
12. Kolata, g. How can computers get common sense?
Science 217, 4566 (Sept. 1982), 1237-1238.
13. minsky, m. Computation: Finite and Infinite Machines.
Prentice-Hall, Englewood Cliffs, NJ, 1967.
14. mitchell, m. An Introduction to Genetic Algorithms.
mI T Press, Cambridge, mA, 1996.
15. Roy, d. Human Speechome Project, 2011; http://www.
16. Roy, d., Patel, R., deCamp, P., Kubat, R., Fleischman,
m., Roy, B., mavridis, N., Tellex, S., Salata, A., guinness,
J., Levit, m., and gorniak, P. The Human Speechome
Project. In Proceedings of the 28th Annual Cognitive
Science Society Conference, R. Sun and N. miyake, Eds.
(Vancouver, B. C., Canada). LEA, Hillsdale, NJ, 2006,
17. Simon, H. and Newell, A. Heuristic problem solving:
The next advance in operations research. Operations
Research 6, 1 (Jan.–Feb., 1958), 1–10.
18. Talbot, d. A social-media decoder. Technology Review
(Nov.–dec. 2011); http://www.technologyreview.com/
19. Turing, A.m. Computing machinery and intelligence.
Mind 59, 236 (1950), 433–460.
20. Whitby, B. The Turing Test: AI’s biggest blind alley?
In Machines and Thought: The Legacy of Alan Turing,
P. millican and A. Clark, Eds. Oxford University Press,
Oxford, U.K., 1996, 53–63.
Robert M. French ( firstname.lastname@example.org) is
a Research director of the French National Center for
Scientific Research (CNRS) working at the Laboratoire
d’Etude de l’Apprentissage et du développement at the
University of Burgundy, dijon, France.