Generative AI of Decision Making
Abstract:
Artificial Intelligence (AI) has recently expanded from imitation to creativity. Instead of imitating human behavior, it can now be used to create previously unseen solutions. In addition to images and language, decision-making has emerged as a third such domain. The idea is to discover new behaviors, strategies, and designs that satisfy both performance and cost objectives. The approach is based on search, such as reinforcement learning and evolutionary computation. Surrogate models are essential in making the approach safe and practical, and rules can be evolved to make it explainable. Applications span a variety of domains, such as those in web design, agriculture, pandemic response, climate change, diabetes treatment, and even evolution of intelligence.
Bio:
Risto Miikkulainen is a Professor of Computer Science at the University of Texas at Austin and a VP of AI Research Cognizant AI Labs. He received an M.S. in Engineering from Helsinki University of Technology (now Aalto University) in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His current research focuses on methods and applications of AI in decision-making, particularly those based on neuroevolution and generative AI, as well as neural network models of natural language processing and vision. He is an author of over 500 articles in these research areas. At Cognizant, and previously as a CTO of Sentient Technologies, he is scaling up these approaches to real-world problems. Risto is an AAAI, IEEE, and INNS Fellow. His work on neuroevolution has recently been recognized with the IEEE CIS Evolutionary Computation Pioneer Award, the Gabor Award of the International Neural Network Society, and Outstanding Paper of the Decade Award of the International Society for Artificial Life.