Bamshad (Bob) Lotfabadi's Website

Would it not be a good idea to devise Artificial Intelligence Language systems that rely on the responses from masses of people, rather than trying to generate something meaningful by themselves? Even collecting a few words from hundreds of thousands of people create more intelligence than a system relying on itself to process and give accurate response through different sorts of independent algorithms!

If we are trying to get machines to understand, the question is, do we ourselves understand anyway? Or are there other patterns that make up our brain system, what we call “understanding” while we ourselves don’t understand anyway?

Great picture!

Source: http://nltk.googlecode.com/svn/trunk/doc/book/ch01.html#ex-set-comprehension-python

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Figure 1.5: Simple Pipeline Architecture for a Spoken Dialogue System: Spoken input (top left) is analyzed, words are recognized, sentences are parsed and interpreted in context, application-specific actions take place (top right); a response is planned, realized as a syntactic structure, then to suitably inflected words, and finally to spoken output; different types of linguistic knowledge inform each stage of the process.

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Perhaps, if a machine can go successfully through finding user-goal in a dialog, the machine could do neat things. The question is, what can the computer do when user goals are broad or too general rather than structured or having a narrow field of ideas

from:
http://nltk.googlecode.com/svn/trunk/doc/book/ch01.html#ex-set-comprehension-python

At a purely practical level, we all need help to navigate the universe of information locked up in text on the Web. Search engines have been crucial to the growth and popularity of the Web, but have some shortcomings. It takes skill, knowledge, and some luck, to extract answers to such questions as: What tourist sites can I visit between Philadelphia and Pittsburgh on a limited budget? What do experts say about digital SLR cameras? What predictions about the steel market were made by credible commentators in the past week? Getting a computer to answer them automatically involves a range of language processing tasks, including information extraction, inference, and summarization, and would need to be carried out on a scale and with a level of robustness that is still beyond our current capabilities.

On a more philosophical level, a long-standing challenge within artificial intelligence has been to build intelligent machines, and a major part of intelligent behaviour is understanding language. For many years this goal has been seen as too difficult. However, as NLP technologies become more mature, and robust methods for analyzing unrestricted text become more widespread, the prospect of natural language understanding has re-emerged as a plausible goal.