What Can We Learn From The Histories of AI: A Conversation With Stephanie Dick
From Harvard Data Science Review Podcast by Harvard Data Science Review
April 30, 2026 · 45 min · Episode 64
About this episode
Stephanie Dick discusses how historical perspectives on knowledge and intelligence inform contemporary AI systems.
What can history teach us about today’s AI revolution? In this month’s episode of the Harvard Data Science Review Podcast, we are joined by Stephanie Dick, a historian of science and technology, to explore how past ideas about knowledge and intelligence shape today’s AI systems. Drawing on examples from early AI, including facial recognition and police databanks, Dick shows that technical decisions are never purely technical—they reflect assumptions about knowledge, people, and power. Tracing AI through three historical “acts,” she challenges the idea that contemporary AI systems represent a clean break from the past. Dick also questions the pursuit of artificial general intelligence, emphasizing instead that intelligence is plural, embodied, and fundamentally relational. This conversation offers a fresh perspective for anyone building, studying, or thinking about AI today. Our guest: Stephanie Dick is an historian, speaker, and writer who works at the intersections of mathematics, computing, and artificial Intelligence. She is also an assistant professor in the School of Communication at Simon Fraser University and the co-editor of HDSR’s Mining the Past column.
People in this episode
Host: Harvard Data Science Review
Guest: Stephanie Dick
Topics covered
- AI history
- intelligence
- technology
- knowledge
- power
- artificial general intelligence
Keywords
- AI revolution
- historian
- facial recognition
- police databanks
- intelligence
- relational
Mentioned in this episode
Organizations: Harvard Data Science Review, Simon Fraser University
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