crab uber
Cops wanted to keep mass surveillance app secret; privacy advocates refused
Much is known about how the federal government leverages location data by serving warrants to major tech companies like Google or Facebook to investigate crime in America. However, much less is known about how location data influences state and local law enforcement investigations. It turns out that’s because many local police agencies intentionally avoid mentioning the under-the-radar tech they use—sometimes without warrants—to monitor private citizens.
As one Maryland-based sergeant wrote in a department email, touting the benefit of “no court paperwork” before purchasing the software, “The success lies in the secrecy.”
you gotta be - *plonk*
The Increasingly Inhumane Gimmicks Cities Are Relying on to “Solve” Homelessness
As the homelessness crisis gets manifestly more urgent and acute in
almost every major city, the “solutions” to this crisis are, in
proportion, becoming more cruel, pseudo-scientific, and cynical. And
more importantly, they almost always involve anything but more
permanent, safe, and secure housing—the only actually proven solution to
homelessness.
the virtues of being a hitman are laziness, hubris, and impatience
TIGERISS roars toward space station spot
Physicists from Washington University in St. Louis are developing a new experiment envisioned for the International Space Station as part of NASA’s Astrophysics Pioneers Program. The Trans-Iron Galactic Element Recorder for the International Space Station (TIGERISS) will be designed to measure the abundances of ultra-heavy galactic cosmic rays. Pioneers Program missions have a total cost cap of $20 million.
[…]
“TIGERISS has the unprecedented ability to measure galactic cosmic ray
abundances with single-element resolution spanning the periodic table
from boron to lead,” said Brian Rauch,
research associate professor of physics in Arts & Sciences,
principal investigator for the TIGERISS program. “At the end of the
five-year mission, our transformational measurements will increase
understanding on how the galaxy produces and distributes the elements.
Greenland ice sheet climate disequilibrium and committed sea-level rise
Ice loss from the Greenland ice sheet is one of the largest sources of contemporary sea-level rise (SLR). While process-based models place timescales on Greenland’s deglaciation, their confidence is obscured by model shortcomings including imprecise atmospheric and oceanic couplings. Here, we present a complementary approach resolving ice sheet disequilibrium with climate constrained by satellite-derived bare-ice extent, tidewater sector ice flow discharge and surface mass balance data. We find that Greenland ice imbalance with the recent (2000–2019) climate commits at least 274 ± 68 mm SLR from 59 ± 15 × 103 km2 ice retreat, equivalent to 3.3 ± 0.9% volume loss, regardless of twenty-first-century climate pathways. This is a result of increasing mass turnover from precipitation, ice flow discharge and meltwater run-off. The high-melt year of 2012 applied in perpetuity yields an ice loss commitment of 782 ± 135 mm SLR, serving as an ominous prognosis for Greenland’s trajectory through a twenty-first century of warming.
Exploring 12 Million of the 2.3 Billion Images Used to Train Stable Diffusion’s Image Generator
One of the biggest frustrations of text-to-image generation AI models is that they feel like a black box. We know they were trained on images pulled from the web, but which ones? As an artist or photographer, an obvious question is whether your work was used to train the AI model, but this is surprisingly hard to answer.
Sometimes, the data isn’t available at all: OpenAI has said it’s trained DALL-E 2 on hundreds of millions of captioned images, but hasn’t released the proprietary data. By contrast, the team behind Stable Diffusion have been very transparent about how their model is trained. Since it was released publicly last week, Stable Diffusion has exploded in popularity, in large part because of its free and permissive licensing, already incorporated into the new Midjourney beta, NightCafe, and Stability AI’s own DreamStudio app, as well as for use on your own computer.
Machine-learning systems, which use algorithms to detect patterns in large collections of data, have excelled at analyzing human language, giving rise to voice assistants that recognize speech, transcription software that converts speech to text and digital tools that translate between human languages.
In recent years, scientists have begun deploying this technology to decode animal communication, using machine-learning algorithms to identify when squeaking mice are stressed or why fruit bats are shouting. Even more ambitious projects are underway — to create a comprehensive catalog of crow calls, map the syntax of sperm whales and even to build technologies that allow humans to talk back.
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