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Subsymbolic AI (SSAI) aims to model intelligence empirically.
SSAI was inspired by biological systems: A model which imitates neural nets in the brain is the basis for the creation of artificial intelligence.
Neural nets consist of a network of
neurons which have weighted connections
with each other.
Early work by Rosenblatt (1962):
the “Perceptron” 
Advantages of artificial neuronal nets:
Representation and processing of fuzziness
Highly parallel and distributed action
Speed and fault-tolerance
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