The New York Times points out that so-called “shadow libraries,” like Library Genesis, Z-Library or Bibliotik, “are obscure repositories storing millions of titles, in many cases without permission — and are often used as A.I. training data.”

A.I. companies have acknowledged in research papers that they rely on shadow libraries. OpenAI’s GPT-1 was trained on BookCorpus, which has over 7,000 unpublished titles scraped from the self-publishing platform Smashwords. To train GPT-3, OpenAI said that about 16 percent of the data it used came from two “internet-based books corpora” that it called “Books1” and “Books2.” According to a lawsuit by the comedian Sarah Silverman and two other authors against OpenAI, Books2 is most likely a “flagrantly illegal” shadow library.

These sites have been under scrutiny for some time. The Authors Guild, which organized the authors’ open letter to tech executives, cited studies in 2016 and 2017 that suggested text piracy depressed legitimate book sales by as much as 14 percent.

Efforts to shut down these sites have floundered. Last year, the F.B.I., with help from the Authors Guild, charged two people accused of running Z-Library with copyright infringement, fraud and money laundering. But afterward, some of these sites were moved to the dark web and torrent sites, making it harder to trace them. And because many of these sites are run outside the United States and anonymously, actually punishing the operators is a tall task.

Tech companies are becoming more tight-lipped about the data used to train their systems.

Read more of this story at Slashdot.

Source: Slashdot – Is AI Training on Libraries of Pirated Books?

Tuesday North America’s largest lithium mining operation cleared its last legal hurdle in federal appeals court, giving a green light to the mining of 6,000 acres in an 18,000-acre project site near Nevada’s northern border.

But meanwhile, in Southern Nevada…

Federal land managers have formally withdrawn their authorization of a Canadian mining company’s lithium exploration project bordering a national wildlife refuge in southern Nevada after conservationists sought a court order to block it.

The Center for Biological Diversity and the Amargosa Conservancy said in a lawsuit filed July 7 that the project on the edge of the Ash Meadows National Wildlife Refuge outside Las Vegas posed an illegal risk to a dozen fish, snail and plant species currently protected under the Endangered Species Act. They filed an additional motion this week in federal court seeking a temporary injunction prohibiting Rover Metals from initiating the drilling of 30 bore sites in search of the highly sought-after metal used to manufacture batteries for electric vehicles.
But before a judge in Las Vegas could rule on the request, the Bureau of Land Management notified Rover Metals on Wednesday that its earlier acceptance of the company’s notice of its intent to proceed “was in error… The agency has concluded that proposed operations are likely to result in disturbance to localized groundwaters that supply the connected surface waters associated with Threatened and Endangered species in local springs,” said Angelita Bulletts, district manager of the bureau’s southern Nevada district…

Conservationists said the reversal provides at least a temporary reprieve for the lush oasis in the Mojave Desert that is home to 25 species of fish, plants, insects and snails that are found nowhere else on Earth — one of the highest concentrations of endemic species in North America at one of the hottest, driest places on the planet.
The article ends with this quote from a director at the Center for Biological Diversity and the Amargosa Conservancy. “We need lithium for our renewable energy transition, but this episode sends a message loud and clear that some places are just too special to drill.”

Read more of this story at Slashdot.

Source: Slashdot – US Pulls Authorization for Lithium Exploration Project in Southern Nevada, Citing Wildlife

Forbes’ senior writer on cybersecurity writes on the “warrantless monitoring of citizens en masse” in the United States.

Here’s how county police armed with a “powerful new AI tool” identified the suspicious driving pattern of a grey Chevy owned by David Zayas:
Searching through a database of 1.6 billion license plate records collected over the last two years from locations across New York State, the AI determined that Zayas’ car was on a journey typical of a drug trafficker. According to a Department of Justice prosecutor filing, it made nine trips from Massachusetts to different parts of New York between October 2020 and August 2021 following routes known to be used by narcotics pushers and for conspicuously short stays. So on March 10 last year, Westchester PD pulled him over and searched his car, finding 112 grams of crack cocaine, a semiautomatic pistol and $34,000 in cash inside, according to court documents. A year later, Zayas pleaded guilty to a drug trafficking charge.

The previously unreported case is a window into the evolution of AI-powered policing, and a harbinger of the constitutional issues that will inevitably accompany it… Westchester PD’s license plate surveillance system was built by Rekor, a $125 million market cap AI company trading on the NASDAQ. Local reporting and public government data reviewed by Forbes show Rekor has sold its ALPR tech to at least 23 police departments and local governments across America, from Lauderhill, Florida to San Diego, California. That’s not including more than 40 police departments across New York state who can avail themselves of Westchester County PD’s system, which runs out of its Real-Time Crime Center… It also runs the Rekor Public Safety Network, an opt-in project that has been aggregating vehicle location data from customers for the last three years, since it launched with information from 30 states that, at the time, were reading 150 million plates per month. That kind of centralized database with cross-state data sharing, has troubled civil rights activists, especially in light of recent revelations that Sacramento County Sheriff’s Office was sharing license plate reader data with states that have banned abortion…

The ALPR market is growing thanks to a glut of Rekor rivals, including Flock, Motorola, Genetec, Jenoptik and many others who have contracts across federal and state governments. They’re each trying to grab a slice of a market estimated to be worth at least $2.5 billion… In pursuit of that elusive profit, the market is looking beyond law enforcement to retail and fast food. Corporate giants have toyed with the idea of tying license plates to customer identities. McDonalds and White Castle have already begun using ALPR to tailor drive-through experiences, detecting returning customers and using past orders to guide them through the ordering process or offer individualized promotion offers. The latter restaurant chain uses Rekor tech to do that via a partnership with Mastercard.
A senior staff attorney at the ACLU tells Forbes that “The scale of this kind of surveillance is just incredibly massive.”

Thanks to long-time Slashdot reader Geek_Cop for sharing the article.

Read more of this story at Slashdot.

Source: Slashdot – AI Watches Millions of Cars and Tells Cops if You Might Be a Criminal

Since 2006, Slashdot has been covering a contest CmdrTaco once summarized as “Compress Wikipedia and Win.” It rewards progress on compressing a 1-billion-character excerpt of Wikipedia — approximately the amount that a human can read in a lifetime.

And today a new record was announced. The 1 billion characters have now been compressed to just 114,156,155 bytes — about 114 megabytes, or just 11.41% of the original size — by Saurabh Kumar, a New York-based quantitative developer for a high-frequency/algorithmic trading and financial services fund. The amount of each “Hutter Prize for Lossless Compression of Human Knowledge” increases based on how much compression is achieved (so if you compress the file x% better you receive x% of the prize). Kumar’s compression was 1.04% smaller than the previous record, so they’ll receive €5187.

But “The intention of this prize is to encourage development of intelligent compressors/programs as a path to AGI,” said Marcus Hutter (now a senior researcher at Google DeepMind) in a 2020 interview with Lex Fridman.

17 years after their original post announcing the competition, Baldrson (Slashdot reader #78,598) returns to explain the contest’s significance to AI research, starting with a quote from mathematician Gregory Chaitin — that “Compression is comprehension.”

But they emphasize that the contest also has one specific hardware constraint rooted in theories of AI optimization:

The Hutter Prize is geared toward research in that it restricts computation resources to the most general purpose hardware that is widely available. Why? As described by the seminal paper “The Hardware Lottery” by Sara Hooker, AI research is biased toward algorithms optimized for existing hardware infrastructure. While this hardware bias is justified for engineering (applying existing scientific understanding to the “utility function” of making money) to quote Sara Hooker, it “can delay research progress by casting successful ideas as failures.”

The complaint that this is “mere” optimization ignores the fact that this was done on general purpose computation hardware, and is therefore in line with the spirit of Sara Hookers admonition to researchers in “The Hardware Lottery”. By showing how to optimize within the constraint of general purpose computation, Saurabh’s contribution may help point the way toward future directions in hardware architecture.

Read more of this story at Slashdot.

Source: Slashdot – Sixth ‘Hutter Prize’ Awarded for Achieving New Data Compression Milestone