The implications of AI are largely speculative. However, this American Enterprise Institute post outlines how AI could aid originalist analysis. It would be an interesting paradox if futuristic technology allows for a more accurate interpretation of history. aei.org/technology-and…

Hot off the presses - Just posted a new draft article on "Artificial Intelligence and Constitutional Interpretation" Written with the incredible Prof. Andrew Coan papers.ssrn.com/sol3/papers.cf… Comments and suggestions are welcome! cc:Colorado Law

Download of the Week: "Artificial Intelligence and Constitutional Interpretation" by Coan & Surden, lsolum.typepad.com/legaltheory/20…

Artificial Intelligence and Constitutional Interpretation by Andrew Coan, Harry Surden :: SSRN papers.ssrn.com/sol3/papers.cf…

My work is discussed in an article out today in the Coloradan

I’m Shawn, founder of Memories.ai, former researcher at Meta and CS PhD at University of Cambridge. Today we’re launching Memories.ai: we built the world’s first Large Visual Memory Model - to give AI human-like visual memories. Why visual memory? AI to

Visit voicingboulder.com to submit a photo or voice note that captures the Boulder you want to see. Your contribution will shape Boulder’s future by blending art, community voice, and city planning through the 2025 Comprehensive Plan update. #boulderlife #youthvoice

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Right now, AI adoption in organizations is constrained by the need to figure out how to integrate AI with the complex & often poorly-understood processes inside companies But ChatGPT agent suggests that The Bitter Lesson of AI may come for real work, too. open.substack.com/pub/oneusefult…

This is an interesting paper - our universe could be inside of a black hole, fostering theories of Smolin and others. arxiv.org/pdf/2505.23877

Having trouble grasping the EU AI Act? Here's An American's Guide to the EU AI Act with Andrew Selbst papers.ssrn.com/sol3/papers.cf…. @berkeleytech

The relative failure of Llama 4 turned out to be very consequential to the AI landscape. It led to the shifting the locus of open weights development to China, a move towards closed models as companies running local Llama couldn’t continue to upgrade, & big talent wars in the US.

This is a really nicely done paper, and a good example of how experts can not only design good benchmarks but also better diagnose what the AI is doing wrong. In this case, not turning to lookup tables and not doing math with coding tools (both of these seem solvable in time)

Your midweek pick-me-up 🌻 Photo shared by arvenkit_ on IG

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This is true of any major benchmark (ARC-AGI, humanity’s last exam, time for task completion). If you compare to last month, things seem relatively stable. If you compare to last year, things seem to moving extremely fast.

I'm breaking my silence. For years, I was quiet about how Eric Weinstein, Sabine Hossenfelder, Prof. Brian Keating & Curt Jaimungal suppressed my scientific critique. They preach free inquiry but practice censorship. This is the story of their hypocrisy. 🧵 timothynguyen.org/2025/08/21/phy…

This is illustrative: 1) When you get an instant AI answer, it is from a small model, which are weak models, especially at math. 2) Non-reasoning models, like the one powering AI overview, only “think” as they write, they make mistakes & then back justify them as they write more.