by Brandon Keim
April 12, 2012
Alan Turing memorial at Bletchley
the site of Turing’s codebreaking accomplishments during World War II.
One hundred years after
Alan Turing was
born, his eponymous test remains an elusive benchmark for artificial
intelligence (AI). Now, for the first time in decades, it’s possible to
imagine a machine making the grade.
Turing was one of the 20th century’s great mathematicians, a
conceptual architect of modern computing whose code-breaking
decisive part in World War II. His test,
described in a seminal
dawn-of-the-computer-age paper, was deceptively simple: If a machine
could pass for human in conversation, the machine could be
Artificial intelligences are now ubiquitous, from GPS navigation
systems and Google algorithms to automated customer service and
Apple’s Siri, to say nothing of Deep Blue and Watson - but no
machine has met Turing’s standard.
The quest to do so, however, and the
lines of research inspired by the general challenge of modeling
human thought, have profoundly influenced both computer and
There is reason to believe that code kernels for the first
Turing-intelligent machine have already been written.
“Two revolutionary advances in
information technology may bring the Turing test out of
retirement,” wrote Robert French, a cognitive scientist at the
French National Center for Scientific Research, in an Apr. 12
“The first is the ready availability
of vast amounts of raw data - from video feeds to complete sound
environments, and from casual conversations to technical
documents on every conceivable subject. The second is the advent
of sophisticated techniques for collecting, organizing, and
processing this rich collection of data.”
“Is it possible to recreate something similar to the
subcognitive low-level association network that we have? That’s
experiencing largely what we’re experiencing? Would that be so
impossible?” French said.
When Turing first proposed the test -
poignantly modeled on a party game in which participants tried to
fool judges about their gender; Turing was cruelly persecuted for
his homosexuality - the idea of “a subcognitive low-level
association network” didn’t exist.
The idea of replicating human thought,
however, seemed quite possible, even relatively easy.
The human mind was thought to be logical. Computers run logical
commands. Therefore our brains should be computable. Computer
scientists thought that within a decade, maybe two, a person engaged
in dialogue with two hidden conversants, one computer and one human,
would be unable to reliably tell them apart.
That simplistic idea proved ill-founded.
Cognition is far more
complicated than mid-20th century computer scientists or
psychologists had imagined, and logic was woefully insufficient in
describing our thoughts.
Appearing human turned out to be an
insurmountably difficult task, drawing on previously unappreciated
human abilities to integrate disparate pieces of information in a
“Symbolic logic by itself is too
brittle to account for uncertainty,” said Noah Goodman, a
computer scientist at Stanford University who models
intelligence in machines.
“the failure of what we now call
old-fashioned AI was very instructive. It led to changes in how
we think about the human mind. Many of the most important things
that have happened in cognitive science” emerged from these
struggles, he said.
By the mid-1980s, the Turing test had
been largely abandoned as a research goal (though it survives today
the annual Loebner prize for realistic chatbots, and momentarily
realistic advertising bots are a regular feature of online life.)
However, it helped spawn the two
dominant themes of
modern cognition and artificial intelligence:
calculating probabilities and producing complex behavior from the
interaction of many small, simple processes.
Unlike the so-called brute force computational approaches seen in
Deep Blue, the computer that famously defeated chess
champion Garry Kasparov, these are considered accurate reflections
of at least some of what occurs in human thought.
As of now, so-called probabilistic and connectionist approaches
inform many real-world artificial intelligences: autonomous cars,
Google searches, automated language translation,
the IBM-developed Watson program that so thoroughly dominated at Jeopardy.
They remain limited in scope.
“If you say, ‘Watson, make me
dinner,’ or ‘Watson, write a sonnet,’ it explodes,” said Goodman
- but raise the alluring possibility of applying them to
unprecedentedly large, detailed datasets.
“Suppose, for a moment, that all the
words you have ever spoken, heard, written, or read, as well as
all the visual scenes and all the sounds you have ever
experienced, were recorded and accessible, along with similar
data for hundreds of thousands, even millions, of other people.
Ultimately, tactile, and olfactory sensors could also be added
to complete this record of sensory experience over time,” wrote
French in Science, with a nod to MIT researcher Deb Roy’s
recordings of 200,000 hours of his infant son’s waking
“Assume also that the software
exists to catalog, analyze, correlate, and cross-link everything
in this sea of data. These data and the capacity to analyze them
appropriately could allow a machine to answer heretofore
computer-unanswerable questions” and even pass a Turing test.
Artificial intelligence expert Satinder
Singh of the University of Michigan was cautiously optimistic about
the prospects offered by data.
“Are large volumes of data going to
be the source of building a flexibly competent intelligence?
Maybe they will be,” he said.
“But all kinds of questions that haven’t been studied much
become important at this point. What is useful to remember? What
is useful to predict? If you put a kid in a room, and let him
wander without any task, why does he do what he does?” Singh
continued. “All these sorts of questions become really
“In order to be broadly and flexibly competent, one needs to
have motivations and curiosities and drives, and figure out what
is important,” he said.
“These are huge challenges.”
computer defeating Ken Jennings, the highest-ranking human
Jeopardy! player. (IBM)
Video: Two chatbots
hold a conversation in the Cornell Creative Machines Lab.
Should a machine pass
the Turing test,
it would fulfill a human desire that predates the computer age,
dating back to Mary Shelley’s Frankenstein or even
the golems of
Middle Age folklore, said computer scientist Carlos Gershenson of
the National Autonomous University of Mexico.
But it won’t answer a more fundamental
“It will be difficult to do - but
what is the purpose?” he said.
Citation: “Dusting Off the Turing Test.”
By Robert M. French. Science, Vol. 336 No. 6088, April 13, 2012.
“Beyond Turing’s Machines.”
By Andrew Hodges. Science, Vol. 336 No.
6088, April 13, 2012.