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"Automatically produced texts use language models derived from statistical analysis of vast corpuses of human-generated text to produce machine-generated texts that can be very hard for a human to distinguish from text produced by another human. These models could help malicious actors in many ways, including generating convincing spam, reviews, and comments -- so it's really important to develop tools that can help us distinguish between human-generated and machine-generated texts."

"Already, artificially intelligent software can evolve false political and social constructs highly targeted to sway specific audiences. Users find themselves in highly individualized, algorithmically determined news and information feeds, intentionally designed to: isolate them from conflicting evidence or opinions, create self-reinforcing feedback loops of confirmation, and untether them from fact-based reality. And these are just early days. If memes and disinformation have been weaponized on social media, it is still in the musket stage."

"However, according to data on the force's website, 92% (2,297) of those were found to be "false positives". South Wales police admitted that "no facial recognition system is 100% accurate", but said the technology had led to more than 450 arrests since its introduction. It also said no one had been arrested after an incorrect match."

"Facebook announced late Friday that it had eliminated jobs in its trending module, the part of its news division where staff curated popular news for Facebook users. Over the weekend, the fully automated Facebook trending module pushed out a false story about Fox News host Megyn Kelly, a controversial piece about a comedian's four-letter word attack on rightwing pundit Ann Coulter"

"he foresees fake text being used "for the production of [literal] 'fake news', or to potentially impersonate people who had produced a lot of text online, or simply to generate troll-grade propaganda for social n"