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US Release of Unredacted JFK Files 'Doxxed' Officials, Including Social Security Numbers

3 months 1 week ago
"I intend to sue the National Archives," said Joseph diGenova, an 80-year-old former Trump campaign lawyer (and a U.S. Attorney from 1983 to 1988). While releasing 63,000 unredacted pages about the 1963 assassination of President Kennedy, the U.S. government erroneously "made public the Social Security numbers and other sensitive personal information of potentially hundreds of former congressional staffers and other people," reports USA Today. ("It is virtually impossible to tell the scope of the breach because the National Archives put them online without a way to search them by keyword, some JFK files experts and victims of the information release told USA TODAY...") Mark Zaid, a national security lawyer who represented current and former spies and other officials in cases against the government, told USA Today that he "saw a few names I know and I informed them of the breach... Hundreds were doxxed but of that number I don't know how many are still living." Zaid, who has fought for decades for the JFK records to be made public, said many of the thousands of investigative documents had been made public long ago with everything declassified and unredacted except for the personal information. Releasing that information now, he told USA TODAY, poses significant threats to those whose information is now public, including dates and places of birth, but especially their Social Security numbers. "The purpose of the release was to inform the public about the JFK assassination, not to help permit identity theft of those who actually investigated the events of that day," Zaid said. The Associated Press reported Thursday afternoon that government officials "said they are still screening the records to identify all the Social Security numbers that were released." One of the newly unredacted documents... discloses the Social Security numbers of more than two dozen people seeking security clearances in the 1990s to review JFK-related documents for the Assassination Records Review Board.

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EditorDavid

Majority of AI Researchers Say Tech Industry Is Pouring Billions Into a Dead End

3 months 1 week ago
Founded in 1979, the Association for the Advancement of AI is an international scientific society. Recently 25 of its AI researchers surveyed 475 respondents in the AAAI community about "the trajectory of AI research" — and their results were surprising. Futurism calls the results "a resounding rebuff to the tech industry's long-preferred method of achieving AI gains" — namely, adding more hardware: You can only throw so much money at a problem. This, more or less, is the line being taken by AI researchers in a recent survey. Asked whether "scaling up" current AI approaches could lead to achieving artificial general intelligence (AGI), or a general purpose AI that matches or surpasses human cognition, an overwhelming 76 percent of respondents said it was "unlikely" or "very unlikely" to succeed... "The vast investments in scaling, unaccompanied by any comparable efforts to understand what was going on, always seemed to me to be misplaced," Stuart Russel, a computer scientist at UC Berkeley who helped organize the report, told New Scientist. "I think that, about a year ago, it started to become obvious to everyone that the benefits of scaling in the conventional sense had plateaued...." In November last year, reports indicated that OpenAI researchers discovered that the upcoming version of its GPT large language model displayed significantly less improvement, and in some cases, no improvements at all than previous versions did over their predecessors. In December, Google CEO Sundar Pichai went on the record as saying that easy AI gains were "over" — but confidently asserted that there was no reason the industry couldn't "just keep scaling up." Cheaper, more efficient approaches are being explored. OpenAI has used a method known as test-time compute with its latest models, in which the AI spends more time to "think" before selecting the most promising solution. That achieved a performance boost that would've otherwise taken mountains of scaling to replicate, researchers claimed. But this approach is "unlikely to be a silver bullet," Arvind Narayanan, a computer scientist at Princeton University, told New Scientist.

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EditorDavid