Tootfinder

Subtoot 'cause the post I saw had enough replies already, but this is a bad article (though it has a wonderful description of how Von Neumann machines work):
https://aeon.co/essays/your-brain-does-not-process-information-and-it-is-not-a-computer
For context, I'm against most uses of modern LLMs for several good ethical reasons, and I think the current state of AI research funding is both unsustainable and harmful to knowledge development. However, I've done a tiny bit of deep learning research myself, and I think the tech has a lot of cool potential, even if on balance it might have even more terrifying-potential.
The central problem with this article is that while it accurately describes ways that most human brains differ fundamentally from one way computers can be set up, it completely ignores how (computer) neutral networks work, including the fact that they'd perform very similar to the humans on the dollar bill task, because they encode a representation of their training inputs as distributed tweaks to the connection weights of many simulated neurons. (Also, people with photographic memory do exist...)
I think that being challenged in one's metaphors is a great idea (read Paul Agre on AI) and this is a useful article to have read for that reason, but I think the more useful stance is a principled agnosticism towards whether the human brain works like a computer, along with a broader imagination for "what a computer works like." More specifically, I'm quite convinced the brain doesn't work like a modern operating system (effectively the central straw man in this article), but I reserve judgement on whether it works like a neutral network.

This https://arxiv.org/abs/2406.00857 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Modeling the refractive index profile n(z) of polar ice for ultra-high energy neutrino experiments
We develop an in-situ index of refraction profile using the transit time of radio signals broadcast from an englacial transmitter to 2-5 km distant radio-frequency receivers, deployed at depths up to 200 m. Maxwell's equations generally admit two ray propagation solutions from a given transmitter, corresponding to a direct path (D) and a refracted path (R); the measured D vs. R (dt(D,R)) timing differences provide constraints on the index of refraction profile near South Pole, where the Askarya…

China's Rising Leadership in Global Science
Renli Wu, Christopher Esposito, James Evans
https://arxiv.org/abs/2406.05917 https://…

China's Rising Leadership in Global Science
Major shifts in the global system of science and technology are destabilizing the global status order and demonstrating the capacity for emerging countries like China and India to exert greater influence. In order to measure changes in the global scientific system, we develop a framework to assess the hierarchical position of countries in the international scientific collaboration network. Using a machine-learning model to identify the leaders of 5,966,623 scientific teams that collaborated acr…

The Japanese Vision for the Black Hole Explorer Mission
Kazunori Akiyama, Kotaro Niinuma, Kazuhiro Hada, Akihiro Doi, Yoshiaki Hagiwara, Aya E. Higuchi, Mareki Honma, Tomohisa Kawashima, Dimitar Kolev, Shoko Koyama, Sho Masui, Ken Ohsuga, Hidetoshi Sano, Hideki Takami, Yuh Tsunetoe, Yoshinori Uzawa, Takuya Akahori, Yuto Akiyama, Peter Galison, Takayuki J. Hayashi, Tomoya Hirota, Makoto Inoue, Yuhei Iwata, Michael D. Johnson, Motoki Kino, Yutaro Kofuji, Yosuke Mizuno, Kotaro Moriyama, H…

The Japanese Vision for the Black Hole Explorer Mission
The Black Hole Explorer (BHEX) is a next-generation space very long baseline interferometry (VLBI) mission concept that will extend the ground-based millimeter/submillimeter arrays into space. The mission, closely aligned with the science priorities of the Japanese VLBI community, involves an active engagement of this community in the development of the mission, resulting in the formation of the Black Hole Explorer Japan Consortium. Here we present the current Japanese vision for the mission, r…

Fundamental operating regimes, hyper-parameter fine-tuning and glassiness: towards an interpretable replica-theory for trained restricted Boltzmann machines
Alberto Fachechi, Elena Agliari, Miriam Aquaro, Anthony Coolen, Menno Mulder
https://arxiv.org/abs/2406.09924

Fundamental operating regimes, hyper-parameter fine-tuning and glassiness: towards an interpretable replica-theory for trained restricted Boltzmann machines
We consider restricted Boltzmann machines with a binary visible layer and a Gaussian hidden layer trained by an unlabelled dataset composed of noisy realizations of a single ground pattern. We develop a statistical mechanics framework to describe the network generative capabilities, by exploiting the replica trick and assuming self-averaging of the underlying order parameters (i.e., replica symmetry). In particular, we outline the effective control parameters (e.g., the relative number of weigh…