Phil Steitz

@psteitz.bsky.social

Animal lover, trail runner, mathematician, product and tech leader, open source developer

Talking to a UX designer today I realized that AI app design is different. instead of focusing on discerning user intent and minimizing cognitive load in business apps, now we let users give vague signals and then count on “the AI” to do something intelligent that effectively becomes their intent.

Troubling, but not at all surprising. I am reminded of Socrates’ warning to Hipppcrates in Protagoras 314b that you can’t “return” cognitive experience to “the vendor” like bad food because you have already been changed by it.

Prof Sam Illingworth@samillingworth.com · 2w ago

🧠 AI is reshaping the people who use it A peer-reviewed paper warns AI in healthcare and the military changes the humans using it: their empathy, responsibility and critical thinking. The tool changes the user. 🔗 doi.org/10.1007/s43681-026-01197-x #SlowAI #AIethics #Deskilling 🧪

A coworker gave me a compliment today. She said I was a “great storyteller.” I cringed. If a child told me that I would be happy, but in the workplace…not so much. The whole “story-telling” push among the b-school gang today reeks to me of epistemic decay.

As scientists we should remember that when we talk science with an AI model we teach private companies how to do science and not a new generation of scientists that will contribute to society. Time spent doing faster research with AI agents is not spent mentoring students. Better CV but worth it?

50% of the time that comparativists come to my office for methods help, they are actually just looking for this paper. What do you do if your "treatment" affected all units, but you expect it to affect some units *more* based on covariates? This. You do this. www.tandfonline.com/doi/abs/10.1...

Factorial Difference-in-Differences*

We formulate factorial difference-in-differences (FDID), a research design that extends canonical difference-in-differences (DID) to settings in which an event affects all units. In many panel data...

tandfonline.com

🧵Feeling safe against data poisoning in post-training? Think again! Individual components of LLM post-training pipelines are surprisingly robust to data poisoning attacks. In work led by Jack Sanderson (co-advised w Yiwei Lu), we show they crumble when attacked together. 1/n

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What are the real problems to be solved in continual learning? In my latest post, I tackle this question — reviewing where I think the field went astray in the past, how language models changed things, and where the real challenges remain. infinitefaculty.substack.com/p/what-are-t...

What are the real problems of continual learning?

Reflections on catastrophic interference, plasticity, and learning for the future in the era of large language models

infinitefaculty.substack.com

So honored to receive the Glushko prize, and just grateful that @cogscisociety.bsky.social is willing to recognize whatever I’m doing as the study of the “mind” :)

Santa Fe Institute @sfiscience.bsky.social · 2mo ago

SFI Complexity Postdoctoral Fellow Marina Dubova (@mdubova.bsky.social) has received a 2026 Glushko Dissertation Prize from the Cognitive Science Society and the Glushko-Samuelson Foundation. The prize recognizes recent Ph.D. dissertations for groundbreaking work in cognitive science.