the problem of "decoding" ourselves -- understanding the inner workings of our minds and our brains, and how the architecture of these elements is encoded in our genome -- would surely be at the top. Yet the diverse fields that took on this challenge, from philosophy and psychology to computer science and neuroscience, have been fraught with disagreement about the right approach.
In 1956, the computer scientist John McCarthy coined the
term "Artificial Intelligence" (AI) to describe the study of
intelligence by implementing its essential features on a computer.
Instantiating an intelligent system using man-made hardware, rather than our
own "biological hardware" of cells and tissues, would show ultimate
understanding, and have obvious practical applications in the creation of
intelligent devices or even robots.
Some of McCarthy's colleagues in neighboring departments,
however, were more interested in how intelligence is implemented in humans (and
other animals) first.
Noam Chomsky and others worked on what became cognitive
science, a field aimed at uncovering the mental representations and rules that
underlie our perceptual and cognitive abilities. Chomsky and his colleagues had
to overthrow the then-dominant paradigm of behaviorism, championed by Harvard
psychologist B.F. Skinner, where animal behavior was reduced to a simple set of
associations between an action and its subsequent reward or punishment. The
undoing of Skinner's grip on psychology is commonly marked by Chomsky's 1967
critical review of Skinner's book Verbal Behavior, a book in which Skinner
attempted to explain linguistic ability using behaviorist principles.
Skinner's approach stressed the historical associations
between a stimulus and the animal's response -- an approach easily framed as a
kind of empirical statistical analysis, predicting the future as a function of
the past. Chomsky's conception of language, on the other hand, stressed the
complexity of internal representations, encoded in the genome, and their
maturation in light of the right data into a sophisticated computational system,
one that cannot be usefully broken down into a set of associations.
Behaviorist
principles of associations could not explain the richness of linguistic
knowledge, our endlessly creative use of it, or how quickly children acquire it
with only minimal and imperfect exposure to language presented by their
environment. The "language faculty," as Chomsky referred to it, was
part of the organism's genetic endowment, much like the visual system, the
immune system and the circulatory system, and we ought to approach it just as
we approach these other more down-to-earth biological systems.
David Marr, a neuroscientist colleague of Chomsky's at MIT,
defined a general framework for studying complex biological systems (like the
brain) in his influential book Vision, one that Chomsky's analysis of the
language capacity more or less fits into. According to Marr, a complex
biological system can be understood at three distinct levels. The first level
("computational level") describes the input and output to the system,
which define the task the system is performing.
In the case of the visual
system, the input might be the image projected on our retina and the output
might our brain's identification of the objects present in the image we had
observed. The second level ("algorithmic level") describes the
procedure by which an input is converted to an output, i.e. how the image on
our retina can be processed to achieve the task described by the computational
level. Finally, the third level ("implementation level") describes
how our own biological hardware of cells implements the procedure described by
the algorithmic level.
The approach taken by Chomsky and Marr toward understanding
how our minds achieve what they do is as different as can be from behaviorism.
The emphasis here is on the internal structure of the system that enables it to
perform a task, rather than on external association between past behavior of
the system and the environment. The goal is to dig into the "black
box" that drives the system and describe its inner workings, much like how
a computer scientist would explain how a cleverly designed piece of software
works and how it can be executed on a desktop computer.
As written today, the history of cognitive science is a
story of the unequivocal triumph of an essentially Chomskyian approach over
Skinner's behaviorist paradigm -- an achievement commonly referred to as the
"cognitive revolution," though Chomsky himself rejects this term.
While this may be a relatively accurate depiction in cognitive science and
psychology, behaviorist thinking is far from dead in related disciplines.
Behaviorist experimental paradigms and associationist explanations for animal
behavior are used routinely by neuroscientists who aim to study the
neurobiology of behavior in laboratory animals such as rodents, where the
systematic three-level framework advocated by Marr is not applied.
In May of last year, during the 150th anniversary of the
Massachusetts Institute of Technology, a symposium on "Brains, Minds and
Machines" took place, where leading computer scientists, psychologists and
neuroscientists gathered to discuss the past and future of artificial
intelligence and its connection to the neurosciences.
The gathering was meant to inspire multidisciplinary
enthusiasm for the revival of the scientific question from which the field of
artificial intelligence originated: how does intelligence work? How does our
brain give rise to our cognitive abilities, and could this ever be implemented
in a machine?
Noam Chomsky, speaking in the symposium, wasn't so enthused.
Chomsky critiqued the field of AI for adopting an approach reminiscent of
behaviorism, except in more modern, computationally sophisticated form. Chomsky
argued that the field's heavy use of statistical techniques to pick regularities
in masses of data is unlikely to yield the explanatory insight that science
ought to offer.
For Chomsky, the "new AI" -- focused on using
statistical learning techniques to better mine and predict data -- is unlikely
to yield general principles about the nature of intelligent beings or about
cognition.
This critique sparked an elaborate reply to Chomsky from
Google's director of research and noted AI researcher, Peter Norvig, who
defended the use of statistical models and argued that AI's new methods and
definition of progress is not far off from what happens in the other sciences.
Chomsky acknowledged that the statistical approach might
have practical value, just as in the example of a useful search engine, and is
enabled by the advent of fast computers capable of processing massive data. But
as far as a science goes, Chomsky would argue it is inadequate, or more
harshly, kind of shallow. We wouldn't have taught the computer much about what
the phrase "physicist Sir Isaac Newton" really means, even if we can
build a search engine that returns sensible hits to users who type the phrase
in.
It turns out that related disagreements have been pressing
biologists who try to understand more traditional biological systems of the
sort Chomsky likened to the language faculty. Just as the computing revolution
enabled the massive data analysis that fuels the "new AI", so has the
sequencing revolution in modern biology given rise to the blooming fields of
genomics and systems biology.
High-throughput sequencing, a technique by which
millions of DNA molecules can be read quickly and cheaply, turned the sequencing
of a genome from a decade-long expensive venture to an affordable, commonplace
laboratory procedure. Rather than painstakingly studying genes in isolation, we
can now observe the behavior of a system of genes acting in cells as a whole,
in hundreds or thousands of different conditions.
The sequencing revolution has just begun and a staggering
amount of data has already been obtained, bringing with it much promise and
hype for new therapeutics and diagnoses for human disease. For example, when a
conventional cancer drug fails to work for a group of patients, the answer
might lie in the genome of the patients, which might have a special property
that prevents the drug from acting.
With enough data comparing the relevant
features of genomes from these cancer patients and the right control groups,
custom-made drugs might be discovered, leading to a kind of "personalized
medicine." Implicit in this endeavor is the assumption that with enough
sophisticated statistical tools and a large enough collection of data, signals
of interest can be weeded it out from the noise in large and poorly understood
biological systems.
The success of fields like personalized medicine and other
offshoots of the sequencing revolution and the systems-biology approach hinge
upon our ability to deal with what Chomsky called "masses of unanalyzed
data" -- placing biology in the center of a debate similar to the one
taking place in psychology and artificial intelligence since the 1960s.
Systems biology did not rise without skepticism. The great
geneticist and Nobel-prize winning biologist Sydney Brenner once defined the
field as "low input, high throughput, no output science."
Brenner, a
contemporary of Chomsky who also participated in the same symposium on AI, was
equally skeptical about new systems approaches to understanding the brain. When
describing an up-and-coming systems approach to mapping brain circuits called
Connectomics, which seeks to map the wiring of all neurons in the brain (i.e.
diagramming which nerve cells are connected to others), Brenner called it a
"form of insanity."
Brenner's catch-phrase bite at systems biology and related
techniques in neuroscience is not far off from Chomsky's criticism of AI. An
unlikely pair, systems biology and artificial intelligence both face the same
fundamental task of reverse-engineering a highly complex system whose inner
workings are largely a mystery.
Yet, ever-improving technologies yield massive
data related to the system, only a fraction of which might be relevant. Do we
rely on powerful computing and statistical approaches to tease apart signal
from noise, or do we look for the more basic principles that underlie the
system and explain its essence?
The urge to gather more data is irresistible,
though it's not always clear what theoretical framework these data might fit
into. These debates raise an old and general question in the philosophy of
science: What makes a satisfying scientific theory or explanation, and how
ought success be defined for science?
I sat with Noam Chomsky on an April afternoon in a somewhat
disheveled conference room, tucked in a hidden corner of Frank Gehry's dazzling
Stata Center at MIT. I wanted to better understand Chomsky's critique of
artificial intelligence and why it may be headed in the wrong direction.
I also
wanted to explore the implications of this critique for other branches of
science, such neuroscience and systems biology, which all face the challenge of
reverse-engineering complex systems -- and where researchers often find
themselves in an ever-expanding sea of massive data. The motivation for the
interview was in part that Chomsky is rarely asked about scientific topics
nowadays.
Journalists are too occupied with getting his views on U.S. foreign
policy, the Middle East, the Obama administration and other standard topics.
Another reason was that Chomsky belongs to a rare and special breed of
intellectuals, one that is quickly becoming extinct. Ever since Isaiah Berlin's
famous essay, it has become a favorite pastime of academics to place various
thinkers and scientists on the "Hedgehog-Fox" continuum: the
Hedgehog, a meticulous and specialized worker, driven by incremental progress
in a clearly defined field versus the Fox, a flashier, ideas-driven thinker who
jumps from question to question, ignoring field boundaries and applying his or
her skills where they seem applicable. Chomsky is special because he makes this
distinction seem like a tired old cliche.
Chomsky's depth doesn't come at the
expense of versatility or breadth, yet for the most part, he devoted his entire
scientific career to the study of defined topics in linguistics and cognitive
science. Chomsky's work has had tremendous influence on a variety of fields
outside his own, including computer science and philosophy, and he has not
shied away from discussing and critiquing the influence of these ideas, making
him a particularly interesting person to interview. Videos of the interview can
be found here.
I want to start with a very basic question. At the beginning
of AI, people were extremely optimistic about the field's progress, but it
hasn't turned out that way. Why has it been so difficult? If you ask
neuroscientists why understanding the brain is so difficult, they give you very
intellectually unsatisfying answers, like that the brain has billions of cells,
and we can't record from all of them, and so on.
Chomsky: There's something to that. If you take a look at
the progress of science, the sciences are kind of a continuum, but they're
broken up into fields. The greatest progress is in the sciences that study the
simplest systems. So take, say physics -- greatest progress there. But one of
the reasons is that the physicists have an advantage that no other branch of
sciences has. If something gets too complicated, they hand it to someone else.
Like the chemists?
Chomsky: If a molecule is too big, you give it to the
chemists. The chemists, for them, if the molecule is too big or the system gets
too big, you give it to the biologists. And if it gets too big for them, they
give it to the psychologists, and finally it ends up in the hands of the
literary critic, and so on. So what the neuroscientists are saying is not
completely false.
However, it could be -- and it has been argued in my view
rather plausibly, though neuroscientists don't like it -- that neuroscience for
the last couple hundred years has been on the wrong track. There's a fairly
recent book by a very good cognitive neuroscientist, Randy Gallistel and King,
arguing -- in my view, plausibly -- that neuroscience developed kind of
enthralled to associationism and related views of the way humans and animals
work. And as a result they've been looking for things that have the properties
of associationist psychology.
"It could be -- and it has been argued, in my view
rather plausibly, though neuroscientists don't like it -- that neuroscience for
the last couple hundred years has been on the wrong track."
Like Hebbian plasticity? [Editor's note: A theory,
attributed to Donald Hebb, that associations between an environmental stimulus
and a response to the stimulus can be encoded by strengthening of synaptic
connections between neurons.]
Chomsky: Well, like strengthening synaptic connections.
Gallistel has been arguing for years that if you want to study the brain
properly you should begin, kind of like Marr, by asking what tasks is it
performing. So he's mostly interested in insects. So if you want to study, say,
the neurology of an ant, you ask what does the ant do? It turns out the ants do
pretty complicated things, like path integration, for example. If you look at
bees, bee navigation involves quite complicated computations, involving
position of the sun, and so on and so forth. But in general what he argues is
that if you take a look at animal cognition, human too, it's computational
systems. Therefore, you want to look the units of computation. Think about a
Turing machine, say, which is the simplest form of computation, you have to
find units that have properties like "read", "write" and
"address." That's the minimal computational unit, so you got to look
in the brain for those. You're never going to find them if you look for
strengthening of synaptic connections or field properties, and so on. You've
got to start by looking for what's there and what's working and you see that
from Marr's highest level.
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