How to Move When the Machine Stops Pulling
How to... use AI to unstick yourself
We often lose momentum because of something small. AI can help.
Tool Making Follow-Up: What I Mean by Friction
Hopefully clearing up some of my vagueness to reduce misunderstanding.
Navier-Stokes and Lean
I was working on this week's post on Lean after reading Kevin Hartnett's book The Proof in the Code: How a Truth Machine Is Transforming Ma...
The Accelerationist Case for Frontier Pacing
Slow AI is smooth AI, smooth AI is fast AI
Manifold Tool - D&D 5e (2024)
This tool takes the form of a wrench, a screwdriver, or another basic tool. As a Magic action, you can touch the item and transform it into a type of Artisan’s Tools of your choice. Whatever form the tool takes, you have proficiency with it when you use it.
What If Sliding Could Be Controlled?
GitHub - openai/NavierStokesAndEuler at 8937a8f4cbc7abaab5e9e97d1cc7f5d2319d9538
Lean certificates accompanying Navier-Stokes and Euler results - openai/NavierStokesAndEuler
The Friction I Don't Want to Lose
I love learning. And while the friction of solving new problems is increasingly traded for speed, the experience gained by overcoming that friction is as important as ever.
Navier-Stokes lost in translation: Why Lean verification of AI...
Autoformalisation is increasingly used to verify mathematical texts, including those generated by AI, as in OpenAI's announced proof of blow-up of solutions to the Navier-Stokes equations. In this...
FRICTIONAL AI WORKSHOP
Topics of the Workshop In its third edition, this workshop builds on and expands its exploration of friction-in-design in AI systems, hosting a full-day event that challenges the pursuit of seamless, rapid interactions. In contrast to the conventional narrative that human over-reliance on AI stems
The machine didn't take your craft. You gave it up. - David Abram
The machine didn't take your craft. You gave it up. by David Abram
We should be more tired than the model
Adding deliberate friction back into development
OpenAI solves Navier–Stokes, but research misconduct concerns remain | Olga Holtz posted on the topic | LinkedIn
OpenAI says its AI has solved a Millennium Prize Problem: Navier–Stokes. A human mathematician asks whether OpenAI committed research misconduct. As a mathematician working at the intersection of mathematics and AI, I find this truly messy while also potentially historic. Three things deserve attention. 1. The fine print IS the mathematics. The claimed construction starts with a three-dimensional incompressible fluid at rest. Under a smooth external force, its velocity becomes unbounded in finite time, even while its kinetic energy stays bounded. Crucially, the force itself remains smooth through the singularity. Forcing is explicitly permitted in Clay’s official breakdown alternatives C and D. Its presence does not disqualify the result. But this construction does not establish blow-up for unforced Navier–Stokes. That distinction matters. 2. This was a research system, not one clever prompt. OpenAI reports roughly 10,000 concurrent agents in the successful group and a result after about 88 hours. The Navier–Stokes effort used approximately 130 billion output tokens; Lean formalization and verification took another 17 hours. Human researchers redirected the effort and used tools to consolidate intermediate insights. The salient part here, as I already wrote earlier, is a new organization of mathematical discovery using an enormous amount of compute, not a faster chatbot. The published Lean formalization is important: there is something concrete for others to check. Let‘s see independent scrutiny of both the proof and whether the formal statement matches the intended theorem. 3. Proof verification is not provenance verification. The result follows the line of attack designed by Spanish mathematicians Diego Córdoba and Luis Martínez Zoroa, who appeared to be close to solving the problem themselves. Tristan Buckmaster has raised concerns about research priority and possible use of private Codex material (his statement is linked to the comment by Guillermo Núñez Ponasso below). Those concerns are not automatic proof of misconduct, yet: OpenAI recognizes Buckmaster and Levent Alpöge’s priority on forced Euler, a related but different problem. It denies accessing their unpublished work. But it also cannot rule out de-identified data from their OpenAI product usage helping improve its models, though it calls that unlikely. Is it though? That ‚leakage’ is quite possible, in my view. If correct, this is an extraordinary result. Let’s get independent verification. And let’s protect unpublished research — but exactly how to protect it from an AI lab if using its tools for this research? Recall that a verified theorem can settle a mathematical question. It cannot, by itself, settle scientific credit or establish that the discovery process was fair. Are you comfortable using an AI lab’s tools when that lab could also become your research competitor? https://lnkd.in/g4HzzUNp #AI #Mathematics #AIforScience #MillenniumProblems #NavierStokes | 54 comments on LinkedIn
The Viscous Frontier
open.substack.com