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Chomsky is agreeing that it's making a map. He just doesn't think that map is very useful on a scientific level, but is useful on an engineering level. You're

Chomsky is agreeing that it's making a map. He just doesn't think that map is very useful on a scientific level, but is useful on an engineering level. You're responding that he's wrong, because it's useful on an engineering level. Right? I'm reading many comments here and they seem to keep boiling down to this notion. Am I wrong?

mturmon 14y ago
At the top level, I think this captures it. If, like Chomsky, you value having a model of the underlying cognition process rather than a set of black-box predictors for aspects of that problem (e.g., various corpus-driven translators), then you might be really annoyed that the black-box people are so satisfied with their results.
- Evbn 14y ago
  The Church was angry that the sun didn't revolve around the Earth. We don't get to pick the prettiest models. Right makes right.
  - mturmon 14y ago
    I object to your glibness. Probably both methods (first-principles cognitive modeling vs. high-degree-of-freedom black box learning) will prove informative, just in different ways. Or in your terms, we may not get to pick the prettiest models, but we owe it to ourselves to explore the space of models to see if we can find the structure in it. The engineer in me is pleased by the undoubted success the data-driven learning culture has had on problems of real importance. But this work is highly empirical, with a tendency to point solutions, and someone is likely to come in later on and generalize these methods (e.g., why do some families of black-box predictor or features outperform others for language learning). There's room for both approaches. Norvig's reply to Chomsky's original remark contains a reference to Leo Breiman's well-informed remarks on this question (http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.ss/1009213726 http://projecteuclid.org/DPubS?service=UI&version=1.0&#3...). Breiman, as author of basic books on measure theory as well as on classification trees, was able to walk both sides of this line ("make a first-principles model" vs. "use lots of data"). He spent considerable energy over the years trying to introduce the data-intensive approach to conventional statistics. For instance, he was one of the handful of bona fide statisticians who would attend and contribute to neural net and machine learning conferences. Probably this strategy is more productive than Chomsky's grumpy-old-man warnings (or sagacious warnings, depending on how you look at it).
rdtsc 14y ago
I think by default you'll find a disproportionate number of critics of Chomsky here. Some who understand what this about are more like to be engineers and like the engineering approach. Others who don't, saw Norvig's name and by default jumped to that side of the argument.

mturmon 14y ago

At the top level, I think this captures it. If, like Chomsky, you value having a model of the underlying cognition process rather than a set of black-box predictors for aspects of that problem (e.g., various corpus-driven translators), then you might be really annoyed that the black-box people are so satisfied with their results.

Evbn 14y ago
The Church was angry that the sun didn't revolve around the Earth. We don't get to pick the prettiest models. Right makes right.
- mturmon 14y ago
  I object to your glibness. Probably both methods (first-principles cognitive modeling vs. high-degree-of-freedom black box learning) will prove informative, just in different ways. Or in your terms, we may not get to pick the prettiest models, but we owe it to ourselves to explore the space of models to see if we can find the structure in it. The engineer in me is pleased by the undoubted success the data-driven learning culture has had on problems of real importance. But this work is highly empirical, with a tendency to point solutions, and someone is likely to come in later on and generalize these methods (e.g., why do some families of black-box predictor or features outperform others for language learning). There's room for both approaches. Norvig's reply to Chomsky's original remark contains a reference to Leo Breiman's well-informed remarks on this question (http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.ss/1009213726 http://projecteuclid.org/DPubS?service=UI&version=1.0&#3...). Breiman, as author of basic books on measure theory as well as on classification trees, was able to walk both sides of this line ("make a first-principles model" vs. "use lots of data"). He spent considerable energy over the years trying to introduce the data-intensive approach to conventional statistics. For instance, he was one of the handful of bona fide statisticians who would attend and contribute to neural net and machine learning conferences. Probably this strategy is more productive than Chomsky's grumpy-old-man warnings (or sagacious warnings, depending on how you look at it).

rdtsc 14y ago

I think by default you'll find a disproportionate number of critics of Chomsky here. Some who understand what this about are more like to be engineers and like the engineering approach. Others who don't, saw Norvig's name and by default jumped to that side of the argument.