Noam Chomsky Explains Where Artificial Intelligence Went Wrong

While popularly known for his piercing and relentless critiques of U.S. foreign policy and economic neoliberalism, Noam Chomsky made his career as a researcher and professor of linguistics and cognitive science. In his 50 years at MIT he earned the appellation “the father of modern linguistics” and—after overturning B.F. Skinner’s behaviorist paradigm—founder of the “cognitive revolution.” But these are labels the self-effacing Chomsky rejects, in his characteristically understated way, as he rejects all triumphalist narratives that seem to promise more than they deliver.

Such is the case with Artificial Intelligence. The term, coined in 1956 by computer scientist John McCarthy, once described the optimism with which the scientific community pursued the secrets of human cognition in order to map those features onto machines. Optimism has turned to puzzlement, ambivalence, or in Chomsky’s case outright skepticism about the models and methodologies embraced by the field of AI.

Never particularly sanguine about the prospects of unlocking the “black box” of human cognition through so-called “associationist” theories, Chomsky has recently become even more critical of the statistical models that have come to dominate so many of the sciences, though he is not without his critics. At an MIT symposium in May of last year, Chomsky expressed his doubts of a methodology Nobel-winning biologist Sydney Brenner has called “low input, high throughput, no output science.”

Recently Yarden Katz, an MIT graduate student in Cognitive Sciences, sat down with Chomsky to discuss the problems with AI as Chomsky sees them. Katz’s complete interview appeared this month in The Atlantic. He also videotaped the interview and posted clips to his Youtube channel. In the clip above, Katz asks Chomsky about “forgotten methodologies in artificial intelligence.” Chomsky discusses the shift toward practical application in engineering and computing technology, which “directed people away from the original questions.” He also expresses the opinion that the original work was “way too optimistic” and assumed too much from the little data available, and he describes how “throwing a sophisticated machine” at the problem leads to a “self-reinforcing” definition of success that is at odds with scientific discovery.

In the clip below, Chomsky discusses a new field in systems biology called “Connectomics,” an attempt to map the wiring of all the neurons in the brain—an endeavor prickly biologist Sydney Brenner calls “a form of insanity.” Katz asks if the “wiring diagram” of the brain would provide “the right level of abstraction” for understanding its workings.

The interview is worth reading, or watching, in full, especially for students of neuroscience or psychology. Chomsky discusses the work of his onetime colleague David Marr, whose posthumously published book Vision has had an enormous influence on the field of cognitive science. Chomsky also praises the work of Randy Gallistel, who argues that developments in cognitive and information science will transform the field of neuroscience and overturn the paradigms embraced by early researchers in AI. While this is an exciting time to be a cognitive scientist, it seems, perhaps, a difficult time to be a proponent of Artificial Intelligence, given the complexities and challenges the field has yet to meet successfully.

Related Content:

Noam Chomsky Spells Out the Purpose of Education

Noam Chomsky & Michel Foucault Debate Human Nature & Power (1971)

Manufacturing Consent: Noam Chomsky and the Media (1992)

Josh Jones is a doctoral candidate in English at Fordham University and a co-founder and former managing editor of Guernica / A Magazine of Arts and Politics.

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  • Gary Salter says:

    The advances in the power of the big supercomputers, take for instance, the recent anouncment of IBM’s big project to simulate really big neural networks of neurons is making progress in understanding what we can do in running very accurate simulations of billions of neurons.
    link to kurzweilai’s article:
    IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer

    The thing is, with systems like IBM’s watson the use the older AI methods is still a step in the right direction because you want to expore all methods of how to build an AI. Also, IBM is developing AI neural network chips (with HP) that are neurons on a chip that are made of transistors and can run faster than a computer simulations. There are also experiments in growing real neurons on a intergrated circuit chip (growing a brain from scratch) on a chip so that you could easily interface to such neurons…with advanced biotech/nanotech we could custom grow any type of cell, cure all diseases, make our cells younger..all it takes is the will to fund such projects with just a small fraction of the money wasted on the world militaries very bloated budgets (lets steal the money from the welfare waste that is the worlds defense/war budgets and fund these very cool technologies that will benefit us more in the long run, then if you wnat, go back to funding the latest H-bomb/popular war!!)

  • Josh Jones says:

    Very cool stuff. Thanks, Gary. I’m with you. Let’s divert military spending to r&d.

  • Christine Kroll says:

    I recommend this movie for the Open Culture collection because of its presentation of many arguments and prognostications of which are coming to fruition. Movie is free courtesy of at IMDb, Hybrid World: The Plan to Modify and Control the Human Race (2012).

    There’s a review here touching on current events 2015

  • Johann Popper says:

    Noam Chomsky’s thinking and speech is measurably slower now compared to 50 years ago. Why?

    Neuronal function and synaptic connections are exactly the right place to look. Brenner is out of his mind if he thinks understanding neuromorphic structure is a waste of time. The problem is the outdated egos involved in these projects are not very exceptional intellects, and they don’t really have a personal interest in curing neurological diseases by replicating neuro-functions as a path to understanding, and most even have personal anti-materialist ontological biases, such as Chomsky’s well documented Cartesianism, that utterly derail any and all attempts to make sense out of the data, from the roots. I don’t think it’s even possible for a brain raised before the wide proliferation of personal computers to have an instinct for recognizing requisite patterns in the data. Or, in other words, Cartesianism is a clear dead end, and only a new hylomorphism or some other such ontology can allow biased intellects to get around common categorical conflations. To figure out how anything works, look at when it doesn’t work first! Obviously! Yet, to my knowledge, almost none of the celebrity intellectuals of cognitive science or artificial intelligence have spent even one month studying Schizophrenia in their entire careers. This is really the limit case of academic absurdity. Stagnation is simply the result of the gatekeepers of a field not working the relevant problems. I say, get out of the way. Non-establiahment intellectuals needs to close in on these problems from outside of current academia until its obsolesence becomes clear, exactly as the scientific revolution displaced scholasticism.

  • Johann Popper says:

    *”not very exceptional intellects, and they don’t really have a personal interest”

    I want to be clearer in English that I’m not saying a light like Noam Chomsky is not exceptional. That sounds awful and is not the case. I mean to say without a personal interest, most work typically cannot be exceptional. Where there is no life or death motivation to paradigm shift or to explore longshot patterns, epochal discoveries or project instaurations simply will not occur.

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