611: Open-Ended A.I.: Practical Applications for Humans and Machines

Dr. Ken Stanley, a world-leading expert on Open-Ended AI and author of the genre-bending book "Why Greatness Cannot be Planned," joins Jon Krohn for a discussion that has the potential to shift your entire view on life. Tune in now to learn more about the complex topics of genetic ML algorithms, the Objective Paradox, Novelty Search, and so much more. This episode is brought to you by Zencastr (zen.ai/sds), the easiest way to make high-quality podcasts. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • Ken on his book 'Why Greatness Cannot Be Planned" and the Objective Paradox [4:15] • The Novelty Search approach [24:14] • How open-ended algorithms like Novelty Search can be stopped from doing something potentially dangerous [1:00:00] • The future of open-ended AI and its intimate relationship with Artificial General Intelligence [1:07:34] • Ken's new company [1:13:34] • How AI could transform life for humans in the coming decades [1:18:29] Additional materials: www.superdatascience.com/611

Om Podcasten

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.