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We speak with moral philosopher Susan Neiman, author of the new book
"Call It Evil: Understanding the Trump Era".
Neiman argues that evil
— far from being an outdated religious concept
— is a vital way to understand how injustice works in the world.
The Trump administration’s abusive policies have been aided by a general reluctance to call them out as evil,
part of how people are “brainwashed … by the idea that morality is for losers, suckers, f…

@cjust@infosec.exchange
2026-06-18 17:24:53

No, Artificial Intelligence Is Not Conscious

--Ted Chiang, The Atlantic
Should we seriously consider the possibility that Claude, or any large language model, might be conscious? And if it has feelings, is it capable of receiving moral instruction?
No. Absolutely not. Generative AI is harmful enough when we understand it as a conventional technology, but if we confuse fluency at generating text with consciousness or moral agency, we’re at risk of assigning re…

@aral@mastodon.ar.al
2026-07-16 08:12:29

‘Last year, the online magazine 972 revealed the existence of a specialised military intelligence unit known as the “legitimisation cell”. Its mandate is narrative management. “Whenever criticism of Israel in the media intensified on a particular issue,” the report said, the cell combed through intercepted calls and fragments of intelligence from Gaza to identify material that could be deployed to defend Israel on the international stage.
“If the global media is talking about Israel ki…

@seeingwithsound@mas.to
2026-08-18 20:27:12

For Elon Musk, Neuralink Blindsight likely merely temporarily serves his current PR needs ("Jesus-level" technology) and as a stepping stone to treating other conditions that are not for a niche market like blindness, or as a stepping stone to mass-market consumer BCIs with AI. Musk is unlikely to do anything mature and sustainable for blindness.
The vOICe vision BCI, on the other hand, has the singular goal of bringing a lasting equivalent of (low) vision to the world

@elduvelle@neuromatch.social
2026-07-14 21:59:28

"Church of England votes against plan to rewild 30% of its land by 2030" theguardian.com/world/2026/jul

@heiseonline@social.heise.de
2026-07-14 12:24:03

Pläne zur Altersverifikation: „Problem ist das Plattformdesign, nicht das Alter“
Die EU plant schärfere Regeln für den Jugendschutz online. Marielle Findorff vom vzbv erklärt, warum Alterskontrollen nicht die Lösung sind.

@primonatura@mstdn.social
2026-07-17 10:00:17

"Church of England votes against plan to rewild 30% of its land by 2030"
#UK #UnitedKingdom #England #Environment

@arXiv_mathST_bot@mastoxiv.page
2026-08-14 08:12:20

On Bridging Mixture Distributions
Pierre Del Moral, Ajay Jasra, Ke Zhao
arxiv.org/abs/2608.13383 arxiv.org/pdf/2608.13383 arxiv.org/html/2608.13383
arXiv:2608.13383v1 Announce Type: new
Abstract: In this article we consider bridging between two mixture probability measures. In particular, given access to a Markov kernel between two component distributions, we provide a general mechanism to generate samples from one mixture to the other. Associated to a given reference and extended state space, we prove entropic optimality of this approach. In order to use this idea one needs to know the underlying mixtures and the Markov kernel, which is seldom available, and so we consider the case of Gaussian mixtures and Schr\"odinger Bridges. We prove a general $2-$Wasserstein continuity bound between the exact bridge and one that is approximated, based on $\epsilon-$covariance inflation, and these rely on a novel continuity analysis of perturbed Riccati maps. We apply our results in the context of bridging mixtures of Gaussians, single Gaussians and empirical estimators of the Gaussian parameters and the Monge map. For mixtures of Gaussians, when the parameters are estimated using the Expectation-Maxmization algorithm, the upper-bound on the $2-$Wasserstein distance between the true and approximated bridges is, under assumptions and with probability at least $1-10N^{-1}$, $\mathcal{O}\big(\big[\big(\tfrac{d\log N}{N}\big)^{1/2}\left\{1 \big(\tfrac{d\log N}{N}\right)^{1/2}(\epsilon^{-2} 1)\big\} \epsilon^2\big]\big) $
and for the other two cases, in expectation, $\mathcal{O}\left(d\left\{\tfrac{1 \epsilon^{-2}}{1 N} \epsilon^2\right\}\right)$, where $d,N\in\mathbb{N}$ is the dimension of the Gaussian and the number of empirical samples respectively. We also investigate our bounds numerically.
toXiv_bot_toot

Wyoming Primary:
One of the most conservative states has a legislature divided between a hard-right House and a merely right Senate.
Ultraconservatives want a complete takeover in the Aug. 18 primaries
nytimes.com/2026/08/13/us/elec…

On Thursday, the Wall Street Journal reported that the US military was preparing to replace the USS Lincoln with the USS George Washington,
as concerns mount over the conditions aboard the Lincoln.
Reports by the Navy Times and Stars and Stripes this week described shortages of supplies and water as well as plumbing problems,
low morale and deteriorating mental health among the Lincoln’s roughly 5,000 sailors,
and further reported that there had been multiple attempts…