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It must be clear that although neural networks are inspired on the functioning of our brain, people who create these networks are well aware that they are not an accurate model of our brain. Nevertheless, these networks and our brain have in common:

Ofcourse, a list of characteristics that they don't have in common with our brain, would be much longer, but, nevertheless, they come one step closer to being a model for our brain than the traditional artificial intelligence paradigm which does not possess the characteristics mentioned above. The classic AI works with explicit procedures to imitate human reasoning, and they focus on more sophisticated aspects of it. Until now it has delivered poor results. Neural networks focus more on low level functions of our reasoning, and, although it's early to make judgements, it seems a more fertile research area. The general idea is that higher levels of our reasoning are based on low level aspects of it, and therefore, if we start simulating these low level aspects, eventually we will arrive to a theory which explains our reasoning in general.


Subsymbolism means that concepts, instead of being represented with a symbol (number, letter, word, phrase, memory adress, etc.), are represented with quantitive properties of, in this case, a network. The single quantitive property is just a sub-part of the representation of the concept, a subsymbol. Traditional AI assumes that our reasoning consists in handling symbols representing concepts, and identifies these symbols with our natural language. Subsymbolism is a valid alternative to this assumption. Concerning neural networks, the subsymbolism paradigm stresses the importance of distributing representations on multiple units. This means for example that, if a concept has a certain property, this property is not represented by the activation of one unit, but by the activation of multiple units.

Logic or intuitivity?

Most people assume that our reasoning is mainly guided by deciding based on some kind of logic and planning our actions in a conscious way. But is it really like that? How many activities do we perform without any conscious deciding and planning involved? Obvious examples are walking, avoiding obstacles that we find on our path and observing our everyday reality with our eyes and ears. Maybe we could even say that most of our activities are guided by unconscious processes in the brain. Then, if our goal is simulating human intelligence, wouldn't it be more smart to start simulating unconscious processes of our brain, and then simulate a conscious process of extraction of relations between concepts from these unconscious processes? I think that neural networks can play an important role in bringing us closer to this goal.

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Thomas Riga, University of Genoa, Italy