Artificial Intelligence: Tom Dietterich on the Promise and Pitfalls of Machines that Learn

About this episode

Artificial intelligence has evolved from an abstract concept into one of the most transformative forces of our time. When Tom Dietterich graduated from Oberlin in 1977 with a degree in mathematics, AI was still largely theoretical. Over the decades that followed, his pioneering research helped turn theory into reality. 

A distinguished computer scientist and one of the early architects of machine learning, Dietterich’s work laid the groundwork for the algorithms that now drive everything from voice assistants and climate models to medical diagnostics and drug development. Tom’s work has made him a sought-after authority. He advises the U.S. government on AI technologies and has earned some of the field’s top honors, including the Award for Research Excellence from the International Joint Conference on Artificial Intelligence — a career achievement shared by just 25 scientists since 1985.

In this episode of Running to the Noise, Oberlin College President Carmen Twillie Ambar sits down with Tom Dietterich, Distinguished Professor Emeritus at Oregon State University, to explore both the promise and the pitfalls of artificial intelligence. Together, they trace the evolution of AI from its beginnings to its current influence across nearly every industry, and discuss how a liberal arts education uniquely prepares us to ask not just what can AI do, but what should we do with it?

From the environmental impact of large-scale computing to the creative and ethical questions facing artists and educators, Dietterich offers a nuanced, hopeful, and deeply human vision for how we can shape the future of intelligent machines.

This isn’t just a conversation about technology; it is a reflection on curiosity, ethics, and what it means to stay human in an age of algorithms.

What We Cover in This Episode

• The origins of machine learning and how early innovators taught computers to “learn.”
• The environmental and ethical implications of AI and how efficiency and innovation can coexist.
• Why AI’s biggest challenge is not what it can do, but what humans choose to do with it.
• How a liberal arts foundation fosters critical thinking, ethics, and responsible innovation.
• The promise of “computational sustainability” and AI’s role in addressing global challenges.
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Episode Links

• The Dartmouth Workshop (1956): The Founding of Artificial Intelligence
• Reinforcement Learning: An Introduction by Sutton & Barto
• Sony AIBO Robot Dog
• eBird – Cornell Lab of Ornithology
• TAHMO: The Trans-African Hydro-Meteorological Observatory