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?
by Yarden Katz, The Atlantic | Read more:
Photo: Graham Gordon Ramsay
