“Humans manage to do so much with surprisingly little,” says Douglas Guilbeault, an assistant professor of organizational behavior at Stanford Graduate School of Business. “Whereas AI, by comparison, is doing relatively little, but with so much power, so much compute, so many resources, and by comparison, relatively fewer constraints.”

On a bonus episode of the If/Then podcast, Guilbeault describes the implications of his recent work. Although he readily acknowledges that AI is “increasingly able to do quite a lot,” Guilbeault and his colleagues believe they have identified a key principle that distinguishes human intelligence from machine intelligence — and one which illuminates the limitations of machine thinking.

Although some researchers and AI boosters believe both humans and AI learn via optimization, Guilbeault and his colleagues have shown that another process more accurately captures how people distill the seemingly infinite complexity of the world and act based on limited information.

“You encounter a lot of noise, a lot of chaos, a lot of randomness,” Guilbeault says. “We somehow figure out how to make meaning and establish strong understandings from within that.”

What limitations have you encountered in your work with AI? Share your story with us at ifthenpod@stanford.edu.

Related Content:

Chapters:

00:00:00 Introduction

00:01:40 Why human learning matters for AI

00:05:03 Satisficing and the limits of optimization

00:06:41 Why LLMs learn differently from humans

00:09:58 The stakes of AI hype

00:13:11 “Humanity has had a good run”

00:15:19 Intuition, insight, & conceptual leaps

00:17:38 Beyond statistics: metaphor, vibes, & reasoning

00:19:39 A simple rule for social learning

00:21:18 Is there a ceiling for AI?

00:23:00 Randomness, disorder, & the path to insight

00:25:00 What an optimization mindset leaves out

00:27:54 Conclusion

If/Then, from Stanford GSB, features conversations with faculty that explore how their research deepens our understanding of business and leadership.

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