Tootfinder

Google product naming at work. There's a "Google AI Premium" and a "Google AI Pro", both for $20/mo. Apparently they're the same thing? Even its own Gemini AI can't keep it straight.

Advancing Science- and Evidence-based AI Policy
Rishi Bommasani, Sanjeev Arora, Jennifer Chayes, Yejin Choi, Mariano-Florentino Cu\'ellar, Li Fei-Fei, Daniel E. Ho, Dan Jurafsky, Sanmi Koyejo, Hima Lakkaraju, Arvind Narayanan, Alondra Nelson, Emma Pierson, Joelle Pineau, Scott Singer, Ga\"el Varoquaux, Suresh Venkatasubramanian, Ion Stoica, Percy Liang, Dawn Song

Advancing Science- and Evidence-based AI Policy
AI policy should advance AI innovation by ensuring that its potential benefits are responsibly realized and widely shared. To achieve this, AI policymaking should place a premium on evidence: Scientific understanding and systematic analysis should inform policy, and policy should accelerate evidence generation. But policy outcomes reflect institutional constraints, political dynamics, electoral pressures, stakeholder interests, media environment, economic considerations, cultural contexts, and …

Nielsen: Netflix's share of most-watched shows has dropped from 80% in 2021 to ~50%; Amazon, Apple, HBO Max, Hulu, and Paramount have shows in 2025's Top 10 (Lucas Shaw/Bloomberg)
https://www.bloomberg.com/news/newsletters/…

On Naturally and Weakly Mal'tsev Categories
Nelson Martins-Ferreira
https://arxiv.org/abs/2508.13315 https://arxiv.org/pdf/2508.13315

On Naturally and Weakly Mal'tsev Categories
We explore a hierarchy of notions in categorical algebra: Mal'tsev categories (where every reflexive relation is symmetric); naturally Mal'tsev categories (where every reflexive graph underlies a unique internal groupoid structure, also known as the Lawvere Condition); and weakly Mal'tsev categories (defined via the joint epimorphicity of local product injections). While varying in generality and historical prominence, these notions can be uniformly described using suitable classes of spans and…

Revisiting the magnetic ground states of RECo$_5$ permanent magnets
F. de Almeida Passos, G. J. Nilsen, R. Dankelman, M. Thijs, G. Balakrishnan, S. Kumar, A. Thamizhavel, J. Larrea Jim\'enez
https://arxiv.org/abs/2507.15170

Revisiting the magnetic ground states of RECo$_5$ permanent magnets
In light of recent improvements in the theory of rare earth magnets, as well as the availability of improved neutron powder diffraction data on these materials, we revisit the magnetic single-ion properties of SmCo$_5$ and the magnetic structures of YCo$_5$ and NdCo$_5$. From neutron diffraction patterns in a wide range of temperatures between 3 K and 800 K, we obtain the thermal coefficient expansion and the magnetic moment values for the latter two materials. For SmCo$_5$, we fit existing neu…

RNAMunin: A Deep Machine Learning Model for Non-coding RNA Discovery
Lauren Lui, Torben Nielsen
https://arxiv.org/abs/2507.11950 https://

RNAMunin: A Deep Machine Learning Model for Non-coding RNA Discovery
Functional annotation of microbial genomes is often biased toward protein-coding genes, leaving a vast, unexplored landscape of non-coding RNAs (ncRNAs) that are critical for regulating bacterial and archaeal physiology, stress response and metabolism. Identifying ncRNAs directly from genomic sequence is a paramount challenge in bioinformatics and biology, essential for understanding the complete regulatory potential of an organism. This paper presents RNAMunin, a machine learning (ML) model th…