Page 19 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Unit 1: Overview of Artificial Intelligence

(c) Artificial Intelligence – Image Processing: We can apply artificial intelligence techniques Notes
in digital image fundamentals, image enhancement, image restoration, morphological
image processing, image segmentation and edge detection, object recognition, image
representation and description, colour image processing, wavelets and multi resolution
processing, image compression.
(d) Artificial Intelligence – Voice Recognition: Voice recognition has existed in some form or
another since the 1950s, Since then, many other companies have toyed with voice
recognition, including Dragon Dictation, which launched the first speech recognition
software for the PC. Soon after, the telecom industry began creating voice portals, which
were intended to replace customer service representatives by giving information through
voice-activated menus. Instead, they became a nuisance to cell phone users looking for
answers about their overage charges.
Despite these advances, the ability to truly communicate with machines through
conversation remained confined to the realm of science fiction.
The latest advances in voice recognition software have found a niche in the automotive
department. Ford’s SYNC feature debuted in 2007 and has now become a popular offering
across its product line. In last year’s Super Bowl, Chevrolet advertised On Star’s ability to
read live Facebook feeds aloud.

AI for speech recognition involves two basic ideas. Firstly, it involves studying the thought
processes of human beings. Secondly, it deals with representing those processes via
machines (like computers, robots, etc.). AI is behavior of a machine, which, if performed
by a human being, would be called intelligence. It makes machines smarter and more
useful, and is less expensive than natural intelligence. NLP, refers to artificial intelligence
methods of communicating with a computer in a natural language like English. The main
objective of a NLP program is to understand input and initiate action. The input words are
scanned and matched against internally stored known words. Identification of a keyword
causes some action to be taken. In this way, one can communicate with the computer in
one’s language.
(e) Artificial Intelligence – Neural Network: A neural network is, in essence, an attempt to
simulate the brain. Neural network theory revolves around the idea that certain key
properties of biological neurons can be extracted and applied to simulations, thus creating
a simulated (and very much simplified) brain. The first important thing to understand
then is that the components of an artificial neural network are an attempt to recreate the
computing potential of the brain. The second important thing to understand, however, is
that no one has ever claimed to simulate anything as complex as an actual brain. Whereas
the human brain is estimated to have something on the order of ten to a hundred billion
neurons, a typical Artificial Neural Network (ANN) is not likely to have more than 1,000
artificial neurons. While many types of artificial neural nets exist, most are organized
according to the same basic structure. There are three components to this organization: a
set of input nodes, one or more layers of ‘hidden’ nodes, and a set of output nodes. The
input nodes take in information, and are akin to sensory organs. Whether the information
is in the form of a digitized picture, or a series of stock values, or just about any other form
that can be numerically expressed, this is where the net gets its initial data. The information
is supplied as activation values, that is, each node is given a number, higher numbers
representing greater activation. This is just like human neurons except that rather than
conveying their activation level by firing more frequently, as biological neurons do,
artificial neurons indicate activation by passing this activation value to connected nodes.
After receiving this initial activation, information is then passed through the network.

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