[mindcrime]

Since SHRDLU's world is so limited, Winograd was able to explicitly program every facet of its language understanding. Unsurprisingly, this approach is totally not scalable and this reveals a little about why we don't have fully human-like language programs.

That's a good point. It does lead one to wonder, however, if techniques inspired to SHRDLU could (or do) have application in domain-specific applications where the world is likewise restricted. Given the increases in raw horsepower available since SHURDLU was first developed, I find myself wondering if we couldn't do some pretty useful things today, using this approach.

[jweese]

Yes. For example, consider interlingual machine translation. Most systems today (like Google) use statistical MT that learns patterns from millions of examples. In interlingua, by contrast, you analyze the input sentence to form a language-independent representation of the sentence's meaning. Then you use that representation to generate a sentence in a new language.

As you might expect, this is basically impossible for wide-domain MT because we don't have unambiguous representations of the meaning of every sentence, and we don't necessarily know how to combine them, and there's a lot of non-compositional phrases, and on and on.

However, if we restrict ourselves to one small domain, interlingua can work. For example, the KANT system [1] is an interlingua that is built for translating technical manuals for Caterpillar products (bulldozers and so on). The input has to be written in a restricted subset of English (Caterpillar Technical English), but then you can analyze it exactly with hand-written rules, and produce exact output in the target language.

[1] http://www2.lti.cs.cmu.edu/Research/Kant/

[Someone]

Firstly, we have done similar things. For example, we have/had http://en.wikipedia.org/wiki/METEO_System for weather reports (use "machine translation weather reports" to google Scientific literature. Among others, that finds information that work is being done on a Croatian version of this). I think there have been successes in the medical field, too, but cannot find them.

However, this 'knowledge engineering' approach to AI has fallen somewhat out of fashion a bit in favourite of statistical methods (however, I don't think anybody does statistics 'from scratch'. For example, in NLP, you could try to statistically learn the definite articles in English, but hard-coding that 'the' is the only one will get you results faster.