There are other ways of thinking about AI
AI is not superhuman
What metaphor should drive the field of AI research?
Many Minds: Seven metaphors for AI
If you wanted a petri dish for understanding metaphors—how they emerge and evolve and jostle with each other—it would be hard to do better than the world of AI. We talk about AI systems variously as coaches or co-pilots, little genies or alien intelligences. Some researchers claim that AIs "grow," that they're entering their phase of "adolescence." Critics deride AI products as slop and dismiss LLMs as a kind of autocomplete on steroids. What's behind these different characterizations? Which ones are accurate and which are unfair? And are our metaphors mostly colorful rhetoric or do they matter? Are they shaping how we understand, adopt, and ultimately regulate these new technologies? My guest today is . Melanie is a computer scientist and Professor at the Santa Fe Institute. She is the author of the book, and she writes a by the same name. This episode is a bit of a companion to with Steve Flusberg. In that episode, Steve and I attempted a kind of crash course on metaphor and the human mind. Here, Melanie and I sit down for more of an extended case study: how metaphors are guiding, galvanizing, and maybe deceiving us in the contested realm of AI discourse. We unpack seven of the most widely used metaphors in this space. We consider how these metaphors are shaping not only our everyday understandings of AI, but also law and policy. We also talk about the metaphor and analogy capabilities of AI itself. Can these systems reason abstractly in the way that humans can? Along the way, Melanie and I touch on: AI-generated poetry, anthropomorphism, the original sin of AI research, the myth of Narcissus, psychometric testing and its pitfalls, metaphors for AI that are a bit hard to spot, and the question of whether an AI has ever come up with a decent analogy for itself. Longtime fans of the show will know that we've had Melanie on the show . We invited her back, not only because she's thought about metaphor and analogy in AI discourse for decades, but because she's a voice of calm insight in an area that’s increasingly awash in hype and polemic. Longtime fans of the show may also note that we are now celebrating our 6th birthday at Many Minds. That's right, the show launched in February 2020. If you'd like to support us as we recognize this milestone, you can leave us a rating or a review, recommend us to a friend, or give us a shout out on social media. Your support is always appreciated. Without further ado, on to my conversation with Dr. Melanie Mitchell. Enjoy! Notes 3:30 – For an overview of Douglas Hofstadter’s work on analogy, see . 8:00 – Much of our discussion in this interview draws on Dr. Mitchell’s piece on the in Science magazine. 13:30 – For earlier discussions of anthropomorphism on the show, see our earlier episodes and . 16:00 – See for the original discussion of LLMs as “stochastic parrots.” 17:00 – See for the original discussion of ChatGPT as a “blurry jpeg.” 18:30 – See for the original discussion of LLMs as role players. 22:00 – See for one use of the “LLMs as crowds” metaphor. See also a discussion of this metaphor (and other metaphors for AI) . 25:00 – For one discussion of AI as a “cultural technology” by Alison Gopnik and colleagues, see . For a more recent discussion of the same metaphor by Henry Farrell, Alison Gopnik and others, see . 27:00 – For the podcast series on intelligence that Dr. Mitchell co-hosted for the Santa Fe Institute, see . 28:00 – See for an influential formulation of the idea that AI is an “alien intelligence.” 29:00 – For philosopher Shannon Vallor’s book about AI as “mirror,” see . 31:00 – For the recent study on users’ metaphors for AI systems, see . 33:00 – For more on the rise of social AI, see our earlier episode . 38:00 – For more on what AI researchers might learn from developmental and comparative psychologists, see Dr. Mitchell’s (summarizing her keynote at NeurIPs). 42:00 – For more on the ARC (Abstraction and Reasoning Corpus) and the research that Dr. Mitchell and colleagues have been doing with it, see and . 48:30 – For the study on humans' preference for AI-generated poetry, see . 50:30 – For Brigitte Nerlich’s documentation and discussion of various metaphors for AI (including AI’s metaphors for itself), see . Recommendations , by Shannon Vallor ‘,’ by Murray Shanahan (!) et al. ‘,’ by Henry Farrell et al. Many Minds is a project of the , which is made possible by a generous grant from the John Templeton Foundation to Indiana University. The show is hosted and produced by , with help from Assistant Producer and with creative support from DISI Directors Erica Cartmill and Jacob Foster. Our artwork is by . Subscribe to Many Minds on Apple, Stitcher, Spotify, Pocket Casts, Google Play, or wherever you listen to podcasts. You can also now subscribe to the Many Minds newsletter ! We welcome your comments, questions, and suggestions. Feel free to email us at: manymindspodcast@gmail.com. For updates about the show, visit or follow us on Bluesky ().
Our minds aren’t equipped to handle AI
AI is junk food for the mind: easy, tempting and ultimately very bad for you.
Don't treat surface-level metaphors about AI as if they describe fundamental reality
Debates about whether AI is a “tool” or whether it’s wrong to “anthropomorphize” it often seem confused about what reality is made of
My Thoughts on AI, Part 1: Fears, Opinions, and Mental Journey
My own personal thoughts and opinions on AI effects and usage, and how those have evolved over time
Why I believe current AI models can really think and understand - Part 1 of 2
How the human mind is different from our conception of it
We Need to Talk About How We Talk About 'AI'
We share a responsibility to create and use empowering metaphors rather than misleading language, write Emily M. Bender and Nanna Inie.
The Thoughts The Civilized Keep
The hype around a new AI language generator reveals the sterility of mainstream thinking on AI today — and indeed on how we think about thinking itself.
On working machines
In part one, on thinking machines, I explored two facets of the philosophy of artificial intelligence: “intelligence”, and consciousness. That left an important topic to consider for this post: the...
Our minds did not evolve for AI
AI is mental junk food: tasty and tempting, but ultimately unhealthy — a cognitive hot dog
Why I believe current AI models can really think and understand - Part 2 of 3
Why computers can in principle produce thinking and understanding
Ideas from philosophy I use to think about AI
Physicalism, functionalism, and lots of Quine
AI is An Ideology, Not A Technology
At its core, "artificial intelligence" is a perilous belief that fails to recognize the agency of humans.
Are We Thinking Correctly About AI Intelligence? | Quanta Magazine
Computer scientist Melanie Mitchell discusses why artificial intelligence doesn’t “think” or “reason” like humans, and how we can create better methods for measuring machine cognition.
The brain is a computer is a brain: neuroscience's internal debate and the social significance of the Computational Metaphor
The Computational Metaphor, comparing the brain to the computer and vice versa, is the most prominent metaphor in neuroscience and artificial intelligence (AI). Its appropriateness is highly debated in both fields, particularly with regards to whether it is useful for the advancement of science and technology. Considerably less attention, however, has been devoted to how the Computational Metaphor is used outside of the lab, and particularly how it may shape society's interactions with AI. As such, recently publicized concerns over AI's role in perpetuating racism, genderism, and ableism suggest that the term "artificial intelligence" is misplaced, and that a new lexicon is needed to describe these computational systems. Thus, there is an essential question about the Computational Metaphor that is rarely asked by neuroscientists: whom does it help and whom does it harm? This essay invites the neuroscience community to consider the social implications of the field's most controversial metaphor.
AI That Evolves in the Wild | Edge.org
I’m interested not in domesticated AI—the stuff that people are trying to sell. I'm interested in wild AI—AI that evolves in the wild. I’m a naturalist, so that’s the interesting thing to me. Thirty-four years ago there was a meeting just like this in which Stanislaw Ulam said to everybody in the room—they’re all mathematicians—"What makes you so sure that mathematical logic corresponds to the way we think?" It’s a higher-level symptom. It’s not how the brain works. All those guys knew fully well that the brain was not fundamentally logical.