The obsession with creating bigger datasets and bigger neural networks has side-lined some of the important questions and areas of research regarding AI. We need new models to advance AI.
Artificial neural networks rely on the point model, treating neurons as nodes that tally inputs and pass the sum through an activity function. Neuroscientists have discovered that dendrite compartments which are part of a neuron can also perform computations that mathematicians had categorized as unsolvable.
Lack of causality is one of the shortcomings of current machine learning systems. Systems that compose and manipulate named objects and semantic variables with causal structures will overcome these limits of AI.
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