Preface

Subfields of artificial intelligence often diversify from a core idea. For example, deep learning networks, models in computational neuroscience, and neuroevolution all take inspiration from biological neural networks as a potential pathway to AI. Most researchers choose to pursue the subfield (and by extension, abstraction) they see as most promising for leading to AI, which naturally results in significant debate and disagreement among researchers as to what abstraction is best. A better understanding and less polarized debate may result from a clear presentation and discussion of abstractions by their most knowledgeable proponents. These insights motivated bringing together researchers from fields that abstract AI at different levels or in different ways to disperse knowledge, and to critically examining the value and promise of different abstractions. Thus this AAAI symposium, How Intelligence Should be Abstracted in AI, consisted of a diverse and multidisciplinary group of AI researchers interested in discussing and comparing different abstractions of both intelligence and processes that might create it.