Comeback war hart - Trotz Krankheit: Céline Dion gibt erstes Konzert #News #Nachrichten
An interview with Deep South Today Chief Content Officer Adam Ganucheau and CEO Warwick Sabin on the network's expansion into Arkansas, video success, and more (Sophie Culpepper/Nieman Lab)
https://www.niemanlab.org/2026/09/at-three
@… Schon so, es haben in der Stadt und auf dem Land leute Ja und Nein gestimmt. Das ist ein Versuch der SVP ihre Stammwähler bei (Kampf-)Laune zu halten. Trotzdem auffällig, Regionen wo Millionen (zurecht) reingebuttert wird um nicht auszusterben, haben Ja gestimmt. Alle grossen Städte wo wirklich gewisse Probleme herrschen Nein. Demokratie ist manchmal lustig.
"Almost two thirds of all the energy we dig up is wasted before it does a single thing of value, a loss RMI puts at more than $4.6 trillion a year, or roughly $600 for every person on Earth.”
#FossilFuels https://cleantechnica.com/2026/06/13/clean-energy-investments-surge-but-that-is-only-part-of-the-story/
On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic, Interaction-Based, and Stochastic-Dynamical Perspective
Nestor R. Barraza, Gabriel Pena
https://arxiv.org/abs/2608.13510 https://arxiv.org/pdf/2608.13510 https://arxiv.org/html/2608.13510
arXiv:2608.13510v1 Announce Type: new
Abstract: Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency. However, their achievable performance is fundamentally constrained by structural properties of the underlying data-generating process, which are formalized in terms of informational bounds. In this work we examine intrinsic limits of data-driven decision systems from an information-theoretic and interaction-based perspective. We analyze minimal achievable error in classification through Fano-type bounds and precision limits in parametric estimation via the Cram\'er-Rao inequality, emphasizing that such limits depend on the underlying model rather than on algorithmic sophistication alone. We further discuss how implicit assumptions, such as independence, ergodicity, and distributional stability, affect the validity of inferential procedures. Building on interaction-based modeling principles, we review typical frameworks such as Markov Random Fields and potential based representations for encoding dependence mechanisms. We also describe decision systems, including LLM-integrated agent architectures, as feedback-driven stochastic processes where state-dependent dynamics may induce emergent macroscopic behavior. This perspective highlights the importance of having adequate models for the data as a prerequi- site for expanding predictive capability, and situates algorithmic learning within the informational limits imposed by the models.
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South Africa Bullied by a protector
When Donald Trump returned to the US presidency in January 2025, South Africans were surprised to find themselves in his line of fire.
The false claim that white South African farmers are being murdered for their land has long been a staple conspiracy theory of the white far-right
(white farmers have been killed, but not at a higher rate than the thousands of other victims of South Africa’s high violent crime rate).
With Trump back i…