Scaling is only a multiplier; we need to work on developing new approaches

Data scaling and compute scaling are *multipliers* for the generalization power of an approach. Trillions of dollars in investment can make very inefficient, low-generalization approaches look adequate, but that doesn't make them the ultimate end-all-be-all of AI. You should work on methods that *can* scale, because if you don't benefit from this multiplier you simply can't compete. But you should keep in mind scaling is only a multiplier: a cleverer approach will scale *better*.

— François Chollet (@fchollet) November 7, 2026

Some (celebratory) wildflowers for November

AI and Rank 5 mathematics

From Sabine Hossenfelder's Nov. 4 Newsletter:

The past months have proved beyond doubt that today’s AI models are capable of converting their reasoning into symbolic, computer-based maths. The idea of building maths entirely on such computer-readable format is decades old, but now it has come into reach because frontier models can both read and understand the published literature as well as converting it into symbolic proofs.

Mathematician Jared Duker Lichtman at Stanford University in the US has launched the Mathematics Autoformalization Project (MAP) and estimates that the project will cover all known mathematics optimistically by next year, conservatively in 2 to 3 years. It is not the only such project currently underway. A group at META has a similar initiative called ATLAS and Mathlib calls theirs the Formal Frontier.

It sounds like the objective is to make existing mathematics an object of investigation rather, transforming it from a conceptual mechanism into a object of thought in the matter of Piaget's reflective abstraction, which I discuss in this blog post from 2025. That would give us a Rank 5 mathematics. (Other remarks on Rank 5 thought).

Here's what Hossenfelder thinks:

What could such a formalization be good for? For one thing, it would make it dramatically easier to build on the existing corpus of knowledge. But maybe more importantly, it would allow entirely new forms of meta-mathematical studies that could reveal so-far unknown relations between different areas of maths.

Color me sympathetic.

Sardonic commentary on Anthropic's S-1

From YouTube:

Anthropic — the maker of the Claude AI models — has filed its S-1 prospectus for what could be the largest IPO in history, at a valuation somewhere between one and a half and two trillion dollars. In this video Patrick Boyle goes through the filing in detail: the eighty-billion-dollar “loss” that isn’t really a loss, the genuine operating profit nobody expected, the growth rate that quietly halved in the third quarter, the eight hundred billion dollars of compute commitments buried in the footnotes, and a corporate governance structure that leaves ordinary shareholders with almost no say at all. Is Anthropic a two-trillion-dollar bargain, an AI bubble about to pop, or something nobody — including Anthropic — can actually price? We look at what the numbers say, what they don’t, and why the honest answer is that both the bulls and the bears are guessing.

With the right architecture, 109 million parameters beats 72 billion.

Yann LeCun was right & nobody in this industry wants to say it out loud. 109 million parameters beat 72 billion. He has spent years saying scale is not the thing that makes a model understand the world. Meta bet the other way. He left in November and started his own lab in Paris. Last Thursday he published the proof. The theory everyone has been running on since 2024 is that if you train models big enough, on text, on images, on sound, they all end up with the same picture of reality inside them. Size does it. Nobody ever proved that part. Randall Balestriero and LeCun proved it. And the proof says size was never doing the work. What does the work is which two things you show a model together. A photo and the caption of that photo are two views of one thing. Models trained on pairs like that get there fast. Two paragraphs pulled from the same document are not two views of one thing. Models trained on those get there slowly and expensively. Next word prediction is the second kind. That is what every chatbot in the world runs on. In their tests the first method reached the same understanding after 21.6 million examples. Next word prediction needed 900 million. And a 109 million parameter text model ended up closer to an image model's view of the world than a 72 billion parameter language model was. 660 times smaller.

— Shruti (@heyshrutimishra) November 1, 2026

Hoboken, Pier 13 and Stevens

Is Elon living in simulation theory?

Ezra Klein, Elon Musk’s Endgame, NYTimes Oct. 31, 2026.

Ezra Klein has a wide-ranging discussion of Elon Musck with Quinn Slobodian, a professor at Boston University who has just coauthored a book about Musk, Muskism, with Ben Tarnoff. The discussion ends with Musk's apparent belief in the idea that we're living in a computer simulation.

Klein:

I just think that for a person with as much power as he has, to have that kind of psychology to suspect that this is a game where your role in it is making big moves — I don’t really know how to describe that situation. But it is a distinctive one that we all now have a lot of exposure to.

Slobodian:

There are a couple of varieties of the simulation theory, too. One is we are all simply just artifacts of this future civilization’s computer video game.

But the other is that most people are shadow people and a small number of people are actually player characters. And it seems like that’s the one that orients him most.

In the libertarian community that reflected a lot on these things, one of the people is Robin Hanson, the George Mason University economist, who said: What kind of ways would we conduct ourselves if this were true?

And he says: If this were true, then it would make sense to first care nothing about other people — so things like foreign aid and the welfare state would be frivolous things to spend time on — to be as entertaining as possible, to accumulate as much wealth as possible and to try to simply organize the world around you in as successful a way as you can.

There’s this radical reneging on anything like social responsibility that is only possible if you embrace this incredibly nihilistic reaffirmation of your megalomania. And the question is how to remove the props and struts that hold him up in that delusion, because it is not true, and he needs to be somehow persuaded from the outside that it’s not true.

If he continues to be incentivized to indulge in those ways of thinking, then he will never be required to be divested of that.

There's much more in the discussion.

OpenAI's Navier-Stokes result not possible in the real world

My deep analysis of the OpenAI Navier-Stokes result: the conditions proposed by OpenAI can never be created by natural physics. If the dimension of the core of the vortex induced by their unrealistic forcing function were equal to the spacing between water molecules, then the length of the vortex would have to be more that 10^12 times the radius of the observable universe to induce blowup. OpenAI did in fact satisfy what amounts to a huge loophole in the Clay Mathematics Institute's conditions to win the prize; however, OpenAI's solution is so wildly unrealistic that we in fact learned absolutely nothing from their result about how well-conditioned Navier-Stokes is under the actual physics of the universe -- which was the whole point of this problem being chosen as a Millenium Prize Challenge in the first place. https://t.co/77PWqeJCqg

— Luke Hutchison (@LH) October 29, 2026

Renting a bike in Hoboken

Virtuoso jazz recorder: Samba de Orfeu

"Samba de Orfeu" by Luiz Bonfá

Emmet Cohen - Piano
Gunhild Carling - Recorder
Yasushi Nakamura - Bass
Joe Farnsworth - Drums

Keith Jarrett, "Over the Rainbow," Tokyo 1984

Keith Jarrett’s solo performance of “Over the Rainbow” in Tokyo in 1984 is widely regarded as one of the most outstanding and moving piano solo covers of all time. Rather than simply restating the familiar melody, the legendary pianist infused the piece with new life through his brilliant improvisation, refined harmonic structure, and profound emotional depth.

— Timeless Classics (@Moonlighhy) October 22, 2026

On our place in the universe: the Anthropocene, Gaia, and the noosphere

Robert Wright and Boris Shoshitaishvili discuss Boris’s new book, Our Planetary Mirror, which draws on three big ideas—Gaia, the noosphere, and the Anthropocene—along with philosophy and myth to reassess who we are and the problems we face as a species. Overtime: Are humans AI’s fossil fuel?

0:00 Overtime teaser
0:43 Boris’s background and new book, Our Planetary Mirror
3:43 Three ways of seeing the planet—and us
12:47 Resurveying the Gaia “hypothesis”
20:31 Dawkins’s case against Gaia
27:16 Is humanity outgrowing its planetary parent?
32:57 The noosphere: Teilhard vs Vernadsky
48:46 Can we reason our way to global solidarity?*
53:58 Heading to Overtime

*This section has tantalizing hints about the need for a symbolic and mythological dimension to our conception of our place in the universe.

Network geometry and task representation in the human cortex

The brain uses dimensionality reduction as a built-in feature of its wiring. Network geometry shapes the transformation of task representations across the human cortex https://t.co/8ztNt5yHSG #neuroscience

— Earl K. Miller (@MillerLabMIT) October 22, 2026

Charting human thought with LLMs

https://t.co/wWtIKiwxz4

— How To Prompt (@HowToPrompt__) October 21, 2026

On 14th St. in Hoboken looking at the Empire State Building