Claim OwnershipMachines & Meaning
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© 2024 Angel Evan
Description
Machines & Meaning examines artificial intelligence through the lens of different philosophers to understand how AI technology shapes human experience. Created for curious, thoughtful people who want to move beyond simplistic "AI is good" or "AI is bad" narratives, each episode takes a key concept from a philosopher and uses it to examine a specific aspect of AI technology and its impact on human life. While the show assumes listeners are familiar with current AI developments, it doesn't require technical knowledge. The series aims to help listeners develop a deeper understanding of how these technologies are changing how we think, behave, and relate to one another by bringing philosophical insights into conversation with modern AI developments.
9 Episodes
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Exploring how Hannah Arendt’s concept of “thoughtlessness” reveals why AI systems create the perfect conditions for systematic harm that emerge from widespread non-engagement with consequences.
Exploring how Aristotle’s concept of practical wisdom reveals the meta-cognitive skills professionals will need to remain valuable in an age when AI can perform most technical tasks.
Exploring how artificial intelligence systematically undermines the conditions necessary for developing human expertise, creating what we might call “permanent intermediates,” people who achieve functional competence but never develop true mastery.
Exploring how Thomas Aquinas’ Doctrine of Double Effect helps us understand our complex relationship with AI’s unintended consequences.
Exploring how Immanuel Kant’s concept of the categorical imperative parallels our current challenge of creating immutable ethical rules for artificial intelligence.
Using Simone de Beauvoir’s philosophical framework on categorization, we examine how rigid binary thinking and over-compartmentalization limit our ability to understand and govern A.I.
Using Descartes’ framework for how we acquire knowledge, we examine what happens when AI reasoning models confront problems where mathematical certainty isn’t enough.
We explore Lewis Mumford’s concept of ‘technics’ to answer an essential question in AI: are we creating technologies that adapt to serve human needs, or are we increasingly adapting ourselves to serve theirs?
We explore Alisdair MacIntyre’s concept of narrative fragmentation and whether large language models (LLMs) contribute to it through their underlying architecture.
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