In the last decade or so we have seen tremendous progress in Artificial Intelligence (AI). AI is now in the real world, powering applications that have a large practical impact. Most of it is based on modeling, i.e. machine learning of statistical models that make it possible to predict what the right decision might be in future situations. The next step for AI is machine creativity, i.e. tasks where the correct, or even good, solutions are not known, but need to be discovered. Methods for machine creativity have existed for decades. I believe we are now in a similar situation as deep learning was a few years ago: with the million-fold increase in computational power, those methods can now be used to scale up to creativity in real-world tasks. In particular, Evolutionary Computation is in a unique position to take advantage of that power, and become the next deep learning.

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To Appear In Banzhaf et al., editors, Evolution in Action: Past, Present and Future, New York, 2020. Springer.

@incollection{miikkulainen:action20, title={Creative AI Through Evolutionary Computation}, author={Risto Miikkulainen}, booktitle={Evolution in Action: Past, Present and Future}, editor={Banzhaf et al.}, address={New York}, publisher={Springer}, url="http://nn.cs.utexas.edu/?miikkulainen:action18", year={2020} }