This article was written by Carlos E. Perez.
In this post I will explore further the characteristics of Artificial Intuition with the goal of describing a set of patterns that can aid us in formulating novel architectures for Deep Learning. In a previous post, I introduced the idea that there are two distinct cognitive mechanisms, one based on logical inference and another based on intuition. At least 6 decades have been spent exploring cognitive mechanisms based on logical inference without making much progress towards AGI. Deep Learning, a breakthrough discovered in 2012, revealed an alternative promising research approach based on the a different cognitive paradigm.
In the field of Psychology, Kahneman and Tversky researches the interplay of these two kinds of cognitive function in a book “Thinking, Fast and Slow”.
Kahneman’s book explores human cognitive biases and employs the dual cognitive processes as a root cause of these biases. In this post however, I will be exploring system 1 (i.e. intuition), more specifically artificial intuition and the mechanisms that give rise to it.
The origins of Deep Learning of course has had a long history. The approach originates from the Connectionist approach and derives much of its philosophy from ideas found in the Complexity sciences. In a nutshell, the idea is that emergent complex behavior can arise from simple mechanisms. Chaos and complexity are the two driving forces that exist in complex systems.
Our goal then is to either explain or better understand how emergent features arise through chaos and complexity. Here are some key features and some questions that require good answers.
To read the whole article, with questions and some of big conceptual leaps explained, click here.
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