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Noam Chomsky: ...Throughout history it’s been mostly the property holders or the educated classes who’ve tended to support power systems. And that’s a large part of what I think education is—it’s a form of indoctrination. You have to reconstruct a picture of the world in order to be conducive to the interests and concerns of the educated classes, and this involves a lot of self-deceit.
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Robert Trivers: So you’re talking about self-deception in at least two contexts. One is intellectuals who, in a sense, go through a process of education which results in a self-deceived organism who is really working to serve the interests of the privileged few without necessarily being conscious of it at all. The other thing is these massive industries of persuasion and deception, which, one can conceptualize, are also inducing a form of either ignorance or self-deception in listeners, where they come to believe that they know the truth when in fact they’re just being manipulated.
At the Brains, Minds, and Machines symposium held during MIT's 150th birthday party, Technology Review reports that Prof. Noam Chomsky
MIT: 150
derided researchers in machine learning who use purely statistical methods to produce behavior that mimics something in the world, but who don't try to understand the meaning of that behavior. Chomsky compared such researchers to scientists who might study the dance made by a bee returning to the hive, and who could produce a statistically based simulation of such a dance without attempting to understand why the bee behaved that way. "That's a notion of [scientific] success that's very novel. I don't know of anything like it in the history of science," said Chomsky.
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Chomsky's remarks were in response to an audience member who asked about the success of statistical models for language tasks such as speech recognition and machine translation. Chomsky's reply (not recorded by Technology Review) was something along the lines that such models had some limited success in some application areas, but not the kind of success that counted.
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- A statistical model is a mathematical model which is modified or trained by the input of data points. Statistical models are often but not always probabilistic. Where the distinction is important we will be careful not to just say "statistical" but to use the following component terms:
- A mathematical model specifies a relation among variables, either in functional form that maps inputs to outputs (e.g. y = m x + b) or in relation form (e.g. the following (x, y) pairs are part of the relation).
- A probabilistic model specifies a probability distribution over possible values of random variables, e.g., P(x, y), rather than a strict deterministic relationship, e.g., y = f(x).
- A trained model uses some training/learning algorithm to take as input a collection of possible models and a collection of data points (e.g. (x, y) pairs) and select the best model. Often this is in the form of choosing the values of parameters (such as m and b above) through a process of statistical inference.
young children can easily learn to master more than one language in an astonishingly short period of time. This has led a number of linguists, most notably Noam Chomsky, to suggest that there might be language universals, common features of all languages that the human brain is attuned to, making learning easier; others have looked for statistical correlations between languages. Now, a team of cognitive scientists has teamed up with an evolutionary biologist to perform a phylogenetic analysis of language families, and the results suggest that when it comes to the way languages order key sentence components, there are no rules.
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The authors of the new paper point out just how hard it is to study languages. We're aware of over 7,000 of them, and they vary significantly in complexity. There are a number of large language families that are likely derived from a single root, but a large number of languages don't slot easily into one of the major groups. Against that backdrop, even a set of simple structural decisions—does the noun or verb come first? where does the preposition go?—become dizzyingly complex, with different patterns apparent even within a single language tree.
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Linguists, however, have been attempting to find order within the chaos. Noam Chomsky helped establish the Generative school of thought, which suggests that there must be some constraints to this madness, some rules that help make a language easier for children to pick up, and hence more likely to persist. Others have approached this issue via a statistical approach (the authors credit those inspired by Joseph Greenberg for this), looking for word-order rules that consistently correlate across language families. This approach has identified a handful of what may be language universals, but our uncertainty about language relationships can make it challenging to know when some of these are correlations are simply derived from a common inheritance.
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Languages vary widely but not without limit. The central goal of linguistics is to describe the diversity of human languages and explain the constraints on that diversity. Generative linguists following Chomsky have claimed that linguistic diversity must be constrained by innate parameters that are set as a child learns a language1, 2. In contrast, other linguists following Greenberg have claimed that there are statistical tendencies for co-occurrence of traits reflecting universal systems biases3, 4, 5, rather than absolute constraints or parametric variation. Here we use computational phylogenetic methods to address the nature of constraints on linguistic diversity in an evolutionary framework6. First, contrary to the generative account of parameter setting, we show that the evolution of only a few word-order features of languages are strongly correlated. Second, contrary to the Greenbergian generalizations, we show that most observed functional dependencies between traits are lineage-specific rather than universal tendencies. These findings support the view that—at least with respect to word order—cultural evolution is the primary factor that determines linguistic structure, with the current state of a linguistic system shaping and constraining future states.
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