I&#x27;d love to hear Chomsky&#x27;s reaction to this stuff (or someone in his camp on the Chomsky vs. Norvig debate [0]).<p>My understanding is that Chomsky was against statistical approaches to AI, as being scientifically un-useful - eventual dead ends, which would reach a certain accuracy, and plateau - as opposed to the purer logic&#x2F;grammar approaches, which reductionistically&#x2F;generatively decompose things into constituent parts, in some <i>interpretable</i> way, which is hence more scientifically valuable, and composable - easier to build on.<p>But now we&#x27;re seeing these very successful blended approaches, where you&#x27;ve got a grammatical search, which is reductionist, and produces an interpretable factoring of the sentence - but its guided by a massive (comparatively uninterpretable) neural net.<p>It&#x27;s like AlphaGo - which is still doing search, in a very structured, rule based, reductionist way - but leveraging the more black-box statistical neural network to make the search actually efficient, and qualitatively more useful.  Is this an emerging paradigm?<p>I used to have a lot of sympathy for the Compsky argument, and thought Norvig et al. [the machine learning community] could be accused of talking up a more prosaic &#x27;applied ML&#x27; agenda into being more scientifically worthwhile than it actually was.<p>But I think systems like this are evidence that gradual, incremental, improvement of working statistical systems, can eventually yield more powerful reductionist&#x2F;logical systems overall.
I&#x27;d love to hear an opposing perspective from someone in the Chomsky camp, in the context of systems like this.
(Which I am hopefully not strawmanning here.)<p>[0]Norvig&#x27;s article: <a href="http:&#x2F;&#x2F;norvig.com&#x2F;chomsky.html" rel="nofollow">http:&#x2F;&#x2F;norvig.com&#x2F;chomsky.html</a>