ABOUT THIS PODCAST 🔗

Update frequency:
weekly
Average audio length:
52 minutes
Guest interviews
English
United States
101 episodes
since Oct. 24, 2024
episodic

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Together with Dr. Joanna Bryson, we explore what AI really is and discuss how humans can regulate this new technology in a way that benefits all.

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This week, Dr. Michael Graziano explores whether AI could ever become truly alive, why humans project consciousness onto machines, and how artificial intelligence is already learning to manipulate us emotionally and psychologically.
Well-known robot ethicist Professor Alan Winfield joins us to explore the real risks of robots and AI and what they mean for humanity’s future.
Dr. Alan Winfield explores how robots evolve through error, imitation, and iteration, revealing surprising parallels with human learning, culture, and creativity, while questioning what “learning” really means for machines.
Dr. Julie Carpenter explores how soldiers formed emotional bonds with bomb-disposal robots and what those attachments reveal about our future relationships with humanoid robots as they become more than machines.
We speak with Dr. Julian Posada about AI’s hidden workforce, where workers in poorer countries label data, identify content, and build the essential foundations powering today’s artificial intelligence revolution.
What happens when AI becomes superintelligent? Lincoln Cannon joins us to explore transhumanism, humanity’s technological evolution, and a future where artificial intelligence could transform what it means to be human.
We speak with Dr. Sandra Wachter about how our data fuels AI, search, and algorithms, often without our knowledge. We discuss how the law must evolve to protect users’ data and to regulate how it is collected and analyzed.
In part two of our interview with Dr. Guy Hoffman, we explore why artists and engineers both shape robotics and why meaningful robot evolution may arrive much more slowly than expected.
Dr. Guy Hoffman joins us to explore the future of robotics, separating hype from reality. We discuss which approaches are likely to succeed, which may fail, and why truly capable robots could take much longer than many expect.
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