Think First, AI Second

Everybody needs a personal AI policy. Just ask Hank Green.

How can we reap AI’s benefits without melting our brains in the process?

When AI builds itself

Our progress toward recursive self-improvement, and its implications.

How AI assistance impacts the formation of coding skills

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

I work, I think? - Annotated

How AI may quietly dismantle the feedback loop that turns inexperienced people into competent ones, and why my work matters to me.

identifAI - Discover Origin. Reveal Insight

identifAI pioneers discerning human-AI creations, bolstering authenticity with advanced models. Determine if an image come from AI or from human!

39 principles for designing human-AI interaction

An applied framework for designing AI interfaces that support appropriate reliance, user control, transparency, and responsible autonomy.

The 80% Problem in Agentic Coding

Managing comprehension debt when leaning on AI to code

Do not give up your brain

It's tempting to just let tools think for you, but you still need to be able to think for yourself and stay sharp.

I am often wrong

[I shared this note with my team earlier this week, and am posting it here as well. I hope it is interesting or helpful for others working on building product in the age of AI.]

AI should help us produce better code - Agentic Engineering Patterns

AI should help us produce better code - Agentic Engineering Patterns

More Than a Tool

What We Learned from a Year of Conversations About AI

Labor market impacts of AI: A new measure and early evidence

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

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 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 system 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 formulations 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 here 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 ().

Good Taste the Only Real Moat Left

AI makes competent output cheap. That makes taste more valuable, but also more incomplete. The real edge comes from pairing judgment with context, stakes, and the willingness to build.

In Search of Vibe Coding Nirvana - Day 1 - Wesley's notes

Deliberate practice in exploring and experimenting with AI tooling