Tootfinder

Just read this post by @… on an optimistic AGI future, and while it had some interesting and worthwhile ideas, it's also in my opinion dangerously misguided, and plays into the current AGI hype in a harmful way.
https://social.coop/@eloquence/114940607434005478
My criticisms include:
- Current LLM technology has many layers, but the biggest most capable models are all tied to corporate datacenters and require inordinate amounts of every and water use to run. Trying to use these tools to bring about a post-scarcity economy will burn up the planet. We urgently need more-capable but also vastly more efficient AI technologies if we want to use AI for a post-scarcity economy, and we are *not* nearly on the verge of this despite what the big companies pushing LLMs want us to think.
- I can see that permacommons.org claims a small level of expenses on AI equates to low climate impact. However, given current deep subsidies on place by the big companies to attract users, that isn't a great assumption. The fact that their FAQ dodges the question about which AI systems they use isn't a great look.
- These systems are not free in the same way that Wikipedia or open-source software is. To run your own model you need a data harvesting & cleaning operation that costs millions of dollars minimum, and then you need millions of dollars worth of storage & compute to train & host the models. Right now, big corporations are trying to compete for market share by heavily subsidizing these things, but it you go along with that, you become dependent on them, and you'll be screwed when they jack up the price to a profitable level later. I'd love to see open dataset initiatives SBD the like, and there are some of these things, but not enough yet, and many of the initiatives focus on one problem while ignoring others (fine for research but not the basis for a society yet).
- Between the environmental impacts, the horrible labor conditions and undercompensation of data workers who filter the big datasets, and the impacts of both AI scrapers and AI commons pollution, the developers of the most popular & effective LLMs have a lot of answer for. This project only really mentions environmental impacts, which makes me think that they're not serious about ethics, which in turn makes me distrustful of the whole enterprise.
- Their language also ends up encouraging AI use broadly while totally ignoring several entire classes of harm, so they're effectively contributing to AI hype, especially with such casual talk of AGI and robotics as if embodied AGI were just around the corner. To be clear about this point: we are several breakthroughs away from AGI under the most optimistic assumptions, and giving the impression that those will happen soon plays directly into the hands of the Sam Altmans of the world who are trying to make money off the impression of impending huge advances in AI capabilities. Adding to the AI hype is irresponsible.
- I've got a more philosophical criticism that I'll post about separately.
I do think that the idea of using AI & other software tools, possibly along with robotics and funded by many local cooperatives, in order to make businesses obsolete before they can do the same to all workers, is a good one. Get your local library to buy a knitting machine alongside their 3D printer.
Lately I've felt too busy criticizing AI to really sit down and think about what I do want the future to look like, even though I'm a big proponent of positive visions for the future as a force multiplier for criticism, and this article is inspiring to me in that regard, even if the specific project doesn't seem like a good one.

We’re heading to #OSFair2025
Together with the European e-Infrastructures Assembly, we’ll be at CERN with a joint booth and a shared mission: to help shape a more open, inclusive, and connected future for research and #openscience
📅 Register by 31 July:

Pseudoscalar meson dominance, the pion-nucleon coupling constant and the Goldberger-Treiman discrepancy
Enrique Ruiz Arriola, Pablo Sanchez-Puertas
https://arxiv.org/abs/2507.22726

Pseudoscalar meson dominance, the pion-nucleon coupling constant and the Goldberger-Treiman discrepancy
We analyze the matrix elements of the pseudoscalar density with pion-quantum numbers $I^G J^{PC}= 1^- 0^{-+}$ in the nucleon in terms of dispersion relations, PCAC and pQCD asymptotic sum rules for the pseudoscalar form factor. We show that the corresponding spectral density must have at least one zero. A model based on ChPT at low energies, resonances at intermediate energies, Regge power-like behaviour at high energies and pQCD at asymptotically high energies allows to deduce the pion-nucleon…

Machine-Learning Prediction of Virus-like Particle Stoichiometry and Stability using Persistent Topological Laplacians
Xiang Liu, Xuefei Huang, Guo-Wei Wei
https://arxiv.org/abs/2507.21417

Machine-Learning Prediction of Virus-like Particle Stoichiometry and Stability using Persistent Topological Laplacians
Understanding the stoichiometry and associated stability of virus-like particles (VLPs) is crucial for optimizing their assembly efficiency and immunogenic properties, which are essential for advancing biotechnology, vaccine design, and drug delivery. However, current experimental methods for determining VLP stoichiometry are labor-intensive, and time consuming. Machine learning approaches have hardly been applied to the study of VLPs. To address this challenge, we introduce a novel persistent …

DISTIL: Data-Free Inversion of Suspicious Trojan Inputs via Latent Diffusion
Hossein Mirzaei, Zeinab Taghavi, Sepehr Rezaee, Masoud Hadi, Moein Madadi, Mackenzie W. Mathis
https://arxiv.org/abs/2507.22813

DISTIL: Data-Free Inversion of Suspicious Trojan Inputs via Latent Diffusion
Deep neural networks have demonstrated remarkable success across numerous tasks, yet they remain vulnerable to Trojan (backdoor) attacks, raising serious concerns about their safety in real-world mission-critical applications. A common countermeasure is trigger inversion -- reconstructing malicious "shortcut" patterns (triggers) inserted by an adversary during training. Current trigger-inversion methods typically search the full pixel space under specific assumptions but offer no assurances tha…

Measuring Sample Quality with Copula Discrepancies
Agnideep Aich, Ashit Baran Aich, Bruce Wade
https://arxiv.org/abs/2507.21434 https://arxiv.org/pdf/2507.…

Measuring Sample Quality with Copula Discrepancies
The scalable Markov chain Monte Carlo (MCMC) algorithms that underpin modern Bayesian machine learning, such as Stochastic Gradient Langevin Dynamics (SGLD), sacrifice asymptotic exactness for computational speed, creating a critical diagnostic gap: traditional sample quality measures fail catastrophically when applied to biased samplers. While powerful Stein-based diagnostics can detect distributional mismatches, they provide no direct assessment of dependence structure, often the primary infe…

Playbook: Mission accomplished? (Politico)
https://www.politico.com/newsletters/playbook/2025/06/23/mission-accomplished-00417937
http://www.memeorandum.com/250623/p9#a250623p9

Vibe Coding: Is Human Nature the Ghost in the Machine?
Cory Knobel, Nicole Radziwill
https://arxiv.org/abs/2508.20918 https://arxiv.org/pdf/2508.20918

Vibe Coding: Is Human Nature the Ghost in the Machine?
This exploratory study examined the consistency of human-AI collaboration by analyzing three extensive "vibe coding" sessions between a human product lead and an AI software engineer. We investigated similarities and differences in team dynamics, communication patterns, and development outcomes across both projects. To our surprise, later conversations revealed that the AI agent had systematically misrepresented its accomplishments, inflating its contributions and systematically downplaying imp…

A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks
Xinli Shi, Xingxing Yuan, Longkang Zhu, Guanghui Wen
https://arxiv.org/abs/2508.20645

A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks
With the increasing scale and dynamics of data, distributed online optimization has become essential for real-time decision-making in various applications. However, existing algorithms often rely on bounded gradient assumptions and overlook the impact of stochastic gradients, especially in time-varying directed networks. This study proposes a novel Time-Varying Hybrid Stochastic Gradient Tracking algorithm named TV-HSGT, based on hybrid stochastic gradient tracking and variance reduction mechan…

Statistical Inference for Differentially Private Stochastic Gradient Descent
Xintao Xia, Linjun Zhang, Zhanrui Cai
https://arxiv.org/abs/2507.20560 https://

Statistical Inference for Differentially Private Stochastic Gradient Descent
Privacy preservation in machine learning, particularly through Differentially Private Stochastic Gradient Descent (DP-SGD), is critical for sensitive data analysis. However, existing statistical inference methods for SGD predominantly focus on cyclic subsampling, while DP-SGD requires randomized subsampling. This paper first bridges this gap by establishing the asymptotic properties of SGD under the randomized rule and extending these results to DP-SGD. For the output of DP-SGD, we show that th…