From Open-Endedness to AI
While much of AI is focused on the ability to solve problems, open-ended processes are arguably far more powerful even though they do not solve any specific problem. Instead, an open-ended process continues to produce increasingly complex yet unpredictable new inventions and innovations forever. We know that open-endedness is possible because humans ourselves are the product of such a process –- evolution –- but also because we have exhibited open-endedness ourselves over the creative explosion of millennia of civilization. Thus to truly achieve intelligence at the human level we must ultimately account for open-endedness.
Takeaways: 1) Open-endedness is a deep and fundamental facet of our intelligence, yet often neglected in the pursuit of AI. 2) Open-endedness is a growing field within AI and machine learning with great potential to leverage the power of modern approaches such as deep learning. 3) Open-endedness reveals the importance of a number of unconventional topics within ML, such as divergence, populations, diversity preservation, stepping stone collection, generating problems and solutions at the same time, and environment design.
Kenneth O. Stanley is Charles Millican Professor of Computer Science at the University of Central Florida and director there of the Evolutionary Complexity Research Group. He was also a co-founder of Geometric Intelligence Inc., which was acquired by Uber to create Uber AI Labs, where he is now also a senior research science manager and head of Core AI research. He received a B.S.E. from the University of Pennsylvania in 1997 and received a Ph.D. in 2004 from the University of Texas at Austin. He is an inventor of the Neuroevolution of Augmenting Topologies (NEAT), HyperNEAT, and novelty search neuroevolution algorithms for evolving complex artificial neural networks. His main research contributions are in neuroevolution (i.e. evolving neural networks), generative and developmental systems, coevolution, machine learning for video games, interactive evolution, and open-ended evolution. He has won best paper awards for his work on NEAT, NERO, NEAT Drummer, FSMC, HyperNEAT, novelty search, Galactic Arms Race, and POET. His original 2002 paper on NEAT also received the 2017 ISAL Award for Outstanding Paper of the Decade 2002 - 2012 from the International Society for Artificial Life. He is a coauthor of the popular science book, "Why Greatness Cannot Be Planned: The Myth of the Objective" (published by Springer), and has spoken widely on its subject.