26-05-2012, 02:06 PM
ARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE.ppt (Size: 463.5 KB / Downloads: 47)
Introduction
Artificial Intelligence, a term coined by John McCarthy defined it as “the science and engineering of making intelligent machines”
Intelligence is ability to acquire, understand and apply knowledge, or the ability to exercise thought or reason.
The goal of AI is to develop working computer systems that are truly capable of performing tasks that require high levels of intelligence.
AI research aims to create systems that can replicate human intelligence completely.
Strong AI refers to a machine that approaches or supersedes human intelligence.
Weak AI refers to the use of software to study or accomplish specific tasks that do not encompass the full range of human cognitive abilities.
Knowledge- An Introduction
Knowledge can be defined as the body of facts and principles accumulated by humankind or the act, fact or state of knowing.
It is having familiarity with language, concepts, procedure, rules coupled with ability to use these notions effectively in modeling systems.
Knowledge should not be confused with data.
Knowledge is closely related to Intelligence. Intelligence requires the possession of and access to knowledge
The AI Technique
The AI technique is based on Physical Symbol System Hypothesis which states 'A Physical Symbol System has the necessary and sufficient means for general intelligent action.
The AI technique should possess the following properties-
- The knowledge captures generalizations - It must be understood by people who provide it - It can be easily modified to correct errors and reflect changes - It can be used in great many situations
Future Developments
Nanodevices – for very large scale components analysis and convolution. These could usher in a new era of neural computing that is step beyond digital computing, because it depends on learning rather than programming and it is fundamentally analog.
Research is ongoing in understanding the computational algorithms used in the brain, with some recent biological evidence for radial basis networks and neural back-propagation as mechanisms for processing data
ARTIFICIAL INTELLIGENCE.ppt (Size: 463.5 KB / Downloads: 47)
Introduction
Artificial Intelligence, a term coined by John McCarthy defined it as “the science and engineering of making intelligent machines”
Intelligence is ability to acquire, understand and apply knowledge, or the ability to exercise thought or reason.
The goal of AI is to develop working computer systems that are truly capable of performing tasks that require high levels of intelligence.
AI research aims to create systems that can replicate human intelligence completely.
Strong AI refers to a machine that approaches or supersedes human intelligence.
Weak AI refers to the use of software to study or accomplish specific tasks that do not encompass the full range of human cognitive abilities.
Knowledge- An Introduction
Knowledge can be defined as the body of facts and principles accumulated by humankind or the act, fact or state of knowing.
It is having familiarity with language, concepts, procedure, rules coupled with ability to use these notions effectively in modeling systems.
Knowledge should not be confused with data.
Knowledge is closely related to Intelligence. Intelligence requires the possession of and access to knowledge
The AI Technique
The AI technique is based on Physical Symbol System Hypothesis which states 'A Physical Symbol System has the necessary and sufficient means for general intelligent action.
The AI technique should possess the following properties-
- The knowledge captures generalizations - It must be understood by people who provide it - It can be easily modified to correct errors and reflect changes - It can be used in great many situations
Future Developments
Nanodevices – for very large scale components analysis and convolution. These could usher in a new era of neural computing that is step beyond digital computing, because it depends on learning rather than programming and it is fundamentally analog.
Research is ongoing in understanding the computational algorithms used in the brain, with some recent biological evidence for radial basis networks and neural back-propagation as mechanisms for processing data