19 ms·

This is neat. Shlomib, you might be interested in SHRDLU [1][2] if you are not aware of it. It was developed by Terry Winograd [3] for his dissertation [4] at

by Arjuna 13y ago

This is neat. Shlomib, you might be interested in SHRDLU [1][2] if you are not aware of it. It was developed by Terry Winograd [3] for his dissertation [4] at MIT. It is a natural language understanding [5] parser that allows you to interact with a small world of 3D solids. I think you will find the paper interesting, because it goes into detail on sentence structure and associated parsing. Here is a sample dialogue with the program, taken from Wikipedia: Person: Pick up a big red block. Computer: OK. Person: Grasp the pyramid. Computer: I don't understand which pyramid you mean. Person (changing their mind): Find a block which is taller than the one you are holding and put it into the box. Computer: By "it", I assume you mean the block which is taller than the one I am holding. Computer: OK. Person: What does the box contain? Computer: The blue pyramid and the blue block. Person: What is the pyramid supported by? Computer: The box. Person: How many blocks are not in the box? Computer: Four of them. Person: Is at least one of them narrower than the one which I told you to pick up? Computer: Yes, the red cube. [1] https://en.wikipedia.org/wiki/SHRDLU https://en.wikipedia.org/wiki/SHRDLU [2] http://hci.stanford.edu/winograd/shrdlu/ http://hci.stanford.edu/winograd/shrdlu/ [3] https://en.wikipedia.org/wiki/Terry_Winograd https://en.wikipedia.org/wiki/Terry_Winograd [4] http://hci.stanford.edu/winograd/shrdlu/AITR-235.pdf http://hci.stanford.edu/winograd/shrdlu/AITR-235.pdf [5] https://en.wikipedia.org/wiki/Natural_language_understanding https://en.wikipedia.org/wiki/Natural_language_understanding

tannerc 13y ago
Why does this seem so scary/amazing? It's basically taking what computers already do really well today and adding a language "mask" onto it. Yet it still blows me away.
- mindcrime 13y ago
  What's really amazing is how old SHRDLU is. It was developed back in the late 60's, early 70's. Although... I guess you could look at it two ways: Be amazed at what SHRDLU could do in 1970, or be disappointed that, given that we had that in 1970, we don't have the "Star Trek Computer" yet in 2013.
  - shmageggy 13y ago
    SHRDLU is definitely amazing, especially given its age, but one's amazement is tempered a little bit (or maybe enhanced, depending on perspective) when you realize that it achieved what it did primarily through really great engineering rather that some fundamental insight about language. 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.
    - mindcrime 13y ago
      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 13y ago
        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/ http://www2.lti.cs.cmu.edu/Research/Kant/
Shorel 13y ago
Woah, that's eliza for CAD in steroids.

by Arjuna 13y ago

tannerc 13y ago

Why does this seem so scary/amazing? It's basically taking what computers already do really well today and adding a language "mask" onto it. Yet it still blows me away.

mindcrime 13y ago
What's really amazing is how old SHRDLU is. It was developed back in the late 60's, early 70's. Although... I guess you could look at it two ways: Be amazed at what SHRDLU could do in 1970, or be disappointed that, given that we had that in 1970, we don't have the "Star Trek Computer" yet in 2013.
- shmageggy 13y ago
  SHRDLU is definitely amazing, especially given its age, but one's amazement is tempered a little bit (or maybe enhanced, depending on perspective) when you realize that it achieved what it did primarily through really great engineering rather that some fundamental insight about language. 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.
  - mindcrime 13y ago
    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 13y ago
      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/ http://www2.lti.cs.cmu.edu/Research/Kant/

Shorel 13y ago

Woah, that's eliza for CAD in steroids.