Image recognition and interpretation. Many medical images can now be automatically interpreted, from plane X-rays through to more complex images like angiograms, CT and MRI scans.
They have no image recognition ability, no physical vision with ocular organs simulations or accurate line of sight paradigms, they do not and cannot process raw sensations like sound, touch, and have no ideal of orientation (g's, earth gravity, CG, ...
Fuzzy cluster analysis: methods for classification, data analysis and image recognition. New York: John Wiley. ISBN 0-471-98864-2.
Ibrahim, Ahmad M. (1997). Introduction to Applied Fuzzy Electronics. Englewood Cliffs, N.J: Prentice Hall.
Image recognition is one application of neural nets that is used at the moment, as is face and voice recognition. However, neural nets are unpredictable.
A good example of this functionality is the capability for image recognition, since human brain is capable to distinguish an object from an image even if is blur, meanwhile linear computing is able to read just pixels as set of colors.
device, which [Rana] El Kaliouby is constructing along with MIT colleagues Rosalind Picard and Alea Teeters, consists of a camera small enough to be pinned to the side of a pair of glasses, connected to a hand-held computer running image recognition ...
Despite these limitations, neural nets have reproduced interesting behaviors in areas such as speech or image recognition, natural-language processing, and learning.
This could be automated though a combination of image recognition and signal processing algorithms. Such automation is vital if a significant amount of neural tissue is to be scanned in a reasonable amount of time.
Image recognition is another task that a human can easily do but which stymies even the biggest of computers. A person can recognize a plane as it turns, flies overhead, and disappears into a dot.
 See also: Artificial intelligence, AI, Neural network, Knowledge, Robotics
  
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