Artificial Intelligence Different from Neural Networks
So starting off, what is Artificial General Intelligence ?
The goal of Artificial General Intelligence is to build thinking machines; that is general purpose intelligent systems comparable to that of the human mind and beyond. While this was the original aim of Artificial Intelligence, the mainstream research has veered towards domain-specific and problem specific solutions. This field combines study in theoretical computer science, mathematics and philosophy. The work in this field is very nascent today and is mostly on the theoretical side aimed towards development of AI systems mimicking the human brain.
Interesting, so what are the different approaches to creating an AGI system ?
Most of the approach to AI may be divided into broad categories such as -
- Symbolic (also known as traditional AI)
Most of the ambitious AGI-oriented projects undertaken have been symbolic in nature. One such famous project was the Genera Problem Solver by …. , which used heuristics to solve problems. The GPS did succeed in solving some simple problems by breaking down the problem into smaller sub-problems until it was addressable by simple heuristics.
Other approaches to this problem are - attempting to create true AI by encoding common sense knowledge (Doug Lenat’s CYC project), logic style knowledge representation, and a problem solving approach carried out by an assemblage of heuristics (Alan Newell’s SOAR project)
Source: Experiments in AI
Intrusion Detection with Artificial Neural Networks: Anomaly based Intrusion Detection using Backpropagation Neural Networks
Book (VDM Verlag)
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