Tootfinder

"regardless of the underlying technology, the pursuit of artificial general intelligence is not necessarily the most efficient route to useful applications. Artificial specific intelligence (AI approaches focused on a specific domain, such as the Nobel prize-winning, protein-folding algorithm, AlphaFold2) gives more reliable and transparent results by combining the subtle pattern detection at which GenAI excels with explicitly encoded, domain-specific knowledge."

Am MIttwoch entscheidet der baden-württembergische Landtag über eine Änderung des Polizeigesetzes und damit über den Einsatz der Überwachungssoftware des US-Konzerns Palantir. Zivilgesellschaft schickt offenen Brief an die Grünen-Fraktion in Baden-Württemberg: Keine Palantir-Software in den Polizeibehörden https://www.

Gianni Infantino ist für mich ein einst abgewiesener Kandidat für die Rechtsabteilung bei der Fifa. Auf gut Glück wurde er bei der Uefa Generalsekretär. In dieser Rolle imitierte er den italienischen Politiker und Philosophen Machiavelli. Und was hat der gesagt? «Es ist nicht der Titel, der zählt, sondern die Macht, die er verleiht. Und was nützt die Macht, wenn du sie nicht missbrauchst?» Das ist Machiavelli.
~Sepp Blatter
https://www.tagesanzeiger.ch/sepp-blatter-ueber-einen-boykott-der-fussball-wm-in-den-usa-873363760295

Manifolds and Modules: How Function Develops in a Neural Foundation Model
Johannes Bertram, Luciano Dyballa, T. Anderson Keller, Savik Kinger, Steven W. Zucker
https://arxiv.org/abs/2512.07869 https://arxiv.org/pdf/2512.07869 https://arxiv.org/html/2512.07869
arXiv:2512.07869v1 Announce Type: new
Abstract: Foundation models have shown remarkable success in fitting biological visual systems; however, their black-box nature inherently limits their utility for under- standing brain function. Here, we peek inside a SOTA foundation model of neural activity (Wang et al., 2025) as a physiologist might, characterizing each 'neuron' based on its temporal response properties to parametric stimuli. We analyze how different stimuli are represented in neural activity space by building decoding man- ifolds, and we analyze how different neurons are represented in stimulus-response space by building neural encoding manifolds. We find that the different processing stages of the model (i.e., the feedforward encoder, recurrent, and readout modules) each exhibit qualitatively different representational structures in these manifolds. The recurrent module shows a jump in capabilities over the encoder module by 'pushing apart' the representations of different temporal stimulus patterns; while the readout module achieves biological fidelity by using numerous specialized feature maps rather than biologically plausible mechanisms. Overall, we present this work as a study of the inner workings of a prominent neural foundation model, gaining insights into the biological relevance of its internals through the novel analysis of its neurons' joint temporal response patterns.
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