Statistical AI, arising from machine learning,
tends to be more concerned with "inductive" thought: given a set of
patterns, induce the trend. Classical AI, on the other hand, is more concerned
with "deductive" thought: given a set of constraints, deduce a
conclusion. Another difference, as mentioned in the previous question, is that
C++ tends to be a favourite language for statistical AI while LISP dominates in
classical AI.
Also , Machine learning is based on a number of earlier building blocks,
starting with classical statistics. Statistics is just about the numbers
and quantifying the data. There are many tools for finding relevant properties
of the data but this is pretty close to pure mathematics.
A
system can't be more intelligent without displaying properties of both
inductive and deductive thought. This lends many to believe that in the end,
there will be some kind of synthesis of statistical and classical AI.
Resources :
1- https://ai.stackexchange.com/questions/8791/difference-in-scope-of-statistical-ai-and-classical-ai
2- https://stackoverflow.com/questions/4353715/classical-ai-ontology-machine-learning-bayesian
Again formatting is wrong - be careful with copy and paste. Also context to the project theme - what has this told you about your major project theme? Where will it lead you?
ReplyDeleteWell defined difference between statistical AI and Classical AI.
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