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The never ending language learner (NELL) is able to build ontologies largely unsupervised with 87% accuracy from a base set of seed facts and access to the inte

The never ending language learner (NELL) is able to build ontologies largely unsupervised with 87% accuracy from a base set of seed facts and access to the internet, indefinitely. [1] Whilst it is unable to assign one symbol with multiple nouns, I think these are more engineering issues than anything. The overall architecture of NELL can be made smarter with horizontally scalable knowledge inference additions. I think articles like this are going to be out of date fairly soon (if not already out of date privately). [1] http://en.wikipedia.org/wiki/Never-Ending_Language_Learning http://en.wikipedia.org/wiki/Never-Ending_Language_Learning

3rd3 13y ago
> Accumulated errors, such as the deduction that Internet cookies were a kind of baked good, led NELL to deduce from the phrases "I deleted my Internet cookies" and "I deleted my files" that "computer files" also belonged in the baked goods category. That is hilarious! By the way, there is another intersting never ending lerner which is based on random images from the internet: http://www.neil-kb.com/ http://www.neil-kb.com/
- blueblob 13y ago
  Thanks both of you for these, very interesting. I am looking at this problem on my own at the moment and didn't find either on my own.
- adwf 13y ago
  Yes 87% is quite impressive, but that remaining 13% is all important. The errors sadly propagate (as above); more importantly, the errors tend to strike randomly also. eg. A human might not be expected to know too much medical terminology, hence could easily get names/locations/functions of muscles and organs mixed up. Whereas a computer might be brilliant at that, but then suddenly not know what a door knob is (door handle? door knob of butter? etc...) So ultimately to make a computer seem intelligent the problem is two-fold: Getting a high percentage of right answers, whilst also getting the "right" wrong answers. Afterall, we don't criticize a human too much for not knowing something like the exact geographic location of a small African nation. But we definitely would if they thought that it was on Mars! It's exactly the same with computers, they're just far more likely to make wildly incorrect mistakes than understandably incorrect ones. So finding a way to mitigate computer mistakes, to make them milder, is every bit as important as eliminating them entirely - a potentially impossible task.
  - evunveot 13y ago
    So finding a way to mitigate computer mistakes, to make them milder, is every bit as important as eliminating them entirely - a potentially impossible task. Seems like they should run multiple instances against different but overlapping corpuses and see what they agree on. For all we know, any single person would forget what a door knob is if they had all the information in the world in their brain. But maybe it already works that way internally, I don't know.
  - blueblob 13y ago
    If you think about the way that humans learn, we get formal education that the computer doesn't get. We are getting corrected for our mistakes by other people. This work did not originally do that. The newer versions of it get corrected by people but that doesn't mean that it couldn't be corrected by more instances of itself that operate over different datasets via a consensus algorithm. The algorithm will also probably get flack that it is not unsupervised when human learning is actually partially supervised. One could argue that humans have a similar problem of errors propagating. Areas that we feel strongly about can bias us against learning in fields such as religion, politics, and, of course, programming language design. I think that it is possible that humans would make some similarly poor responses in areas that we don't talk about often.
    - cortesoft 13y ago
      I agree. In fact, errors like thinking an internet cookie is a baked good sounds EXACTLY like the type or mistake a small child would make.
      - 3rd3 13y ago
        That makes me remember that as a small kid there were certain spots in my environment beyond which, I was conviced, infinite wilderness would adjoin or the world would just end (for example behind forests or hedges). Growing older I was often sobered when I discrovered there was just bog-standard urban landscape. Yet, the machine seems to come from an entirely differernt direction. It has much more facts accumulated than a child and can articulate and process them to absolute precision.
xzephyr 13y ago
>Whilst it is unable to assign one symbol with multiple nouns, I think these are more engineering issues than anything. The overall architecture of NELL can be made smarter with horizontally scalable knowledge inference additions. It is not a trivial problem.[1] In fact representing knowledge as some ontology of human language strings with fixed schematics has its fundamental limitations. In many situations it simply fails, disambiguation is just one of them. For example, trustworthiness, temporal information and newly invented phrases etc. [1]http://en.wikipedia.org/wiki/Word-sense_disambiguation http://en.wikipedia.org/wiki/Word-sense_disambiguation

3rd3 13y ago

> Accumulated errors, such as the deduction that Internet cookies were a kind of baked good, led NELL to deduce from the phrases "I deleted my Internet cookies" and "I deleted my files" that "computer files" also belonged in the baked goods category. That is hilarious! By the way, there is another intersting never ending lerner which is based on random images from the internet: http://www.neil-kb.com/ http://www.neil-kb.com/

blueblob 13y ago
Thanks both of you for these, very interesting. I am looking at this problem on my own at the moment and didn't find either on my own.
adwf 13y ago
Yes 87% is quite impressive, but that remaining 13% is all important. The errors sadly propagate (as above); more importantly, the errors tend to strike randomly also. eg. A human might not be expected to know too much medical terminology, hence could easily get names/locations/functions of muscles and organs mixed up. Whereas a computer might be brilliant at that, but then suddenly not know what a door knob is (door handle? door knob of butter? etc...) So ultimately to make a computer seem intelligent the problem is two-fold: Getting a high percentage of right answers, whilst also getting the "right" wrong answers. Afterall, we don't criticize a human too much for not knowing something like the exact geographic location of a small African nation. But we definitely would if they thought that it was on Mars! It's exactly the same with computers, they're just far more likely to make wildly incorrect mistakes than understandably incorrect ones. So finding a way to mitigate computer mistakes, to make them milder, is every bit as important as eliminating them entirely - a potentially impossible task.
- evunveot 13y ago
  So finding a way to mitigate computer mistakes, to make them milder, is every bit as important as eliminating them entirely - a potentially impossible task. Seems like they should run multiple instances against different but overlapping corpuses and see what they agree on. For all we know, any single person would forget what a door knob is if they had all the information in the world in their brain. But maybe it already works that way internally, I don't know.
- blueblob 13y ago
  If you think about the way that humans learn, we get formal education that the computer doesn't get. We are getting corrected for our mistakes by other people. This work did not originally do that. The newer versions of it get corrected by people but that doesn't mean that it couldn't be corrected by more instances of itself that operate over different datasets via a consensus algorithm. The algorithm will also probably get flack that it is not unsupervised when human learning is actually partially supervised. One could argue that humans have a similar problem of errors propagating. Areas that we feel strongly about can bias us against learning in fields such as religion, politics, and, of course, programming language design. I think that it is possible that humans would make some similarly poor responses in areas that we don't talk about often.
  - cortesoft 13y ago
    I agree. In fact, errors like thinking an internet cookie is a baked good sounds EXACTLY like the type or mistake a small child would make.
    - 3rd3 13y ago
      That makes me remember that as a small kid there were certain spots in my environment beyond which, I was conviced, infinite wilderness would adjoin or the world would just end (for example behind forests or hedges). Growing older I was often sobered when I discrovered there was just bog-standard urban landscape. Yet, the machine seems to come from an entirely differernt direction. It has much more facts accumulated than a child and can articulate and process them to absolute precision.

xzephyr 13y ago

>Whilst it is unable to assign one symbol with multiple nouns, I think these are more engineering issues than anything. The overall architecture of NELL can be made smarter with horizontally scalable knowledge inference additions. It is not a trivial problem.[1] In fact representing knowledge as some ontology of human language strings with fixed schematics has its fundamental limitations. In many situations it simply fails, disambiguation is just one of them. For example, trustworthiness, temporal information and newly invented phrases etc. [1]http://en.wikipedia.org/wiki/Word-sense_disambiguation http://en.wikipedia.org/wiki/Word-sense_disambiguation