Are AI chatbots quietly shaping the perceptions of U.S. voters? Voters trying to educate themselves about candidates "are unknowingly being served partisan talking points in the guise of neutral news summaries," write two news researchers in Politico magazine: Leading AI chatbots cite partisan websites masquerading as independent local news outlets nearly half of the time when people ask about candidates and issues that the partisan sites have covered in the midterm campaign, according to a new audit by NewsGuard, an organization focused on news reliability where we work as analysts... NewsGuard prompted seven leading AI tools with queries based on recent coverage of candidates and issues by 12 pink slime sites — six left-leaning and six right-leaning. The results were stark. Collectively, the chatbots cited pink slime sites along with other sources in 48.2 percent of their responses. In 7.7 percent of responses, pink slime sites were the only sources at all that were cited in chatbot responses, although other sources appeared in the source list provided at the end of the response. None of the AI tools received results they'd probably be eager to boast about, though the percentage of chatbots' responses that cited a pink slime site had a relatively large range: 70.8 percent for OpenAI's ChatGPT, 54.2 percent for Microsoft's Copilot, 54.2 percent for Perplexity, 50 percent for Anthropic's Claude, 41.7 percent for Google's Gemini; 37.5 percent for Meta AI and 29.2 percent for xAI's Grok... The seven chatbots were also collectively three times more likely to cite left-leaning pink slime sites (cited in 36.3 percent of responses) than their right-leaning counterparts (cited in 11.9 percent of responses) — though that may have more to do with the progressive sites' far more frequent posting than any political bias from the chatbots. "While citing the pink slime sites, only one chatbot response out of 168 total queries noted the partisan nature of the source," the article points out. But they also note that the real problem seems to be that consumer-oriented AI chatbots just "use much of the internet's content — regardless of the reliability or standards of the source — to frame their answers."

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"A federal jury in San Diego awarded Taction Technology more than $5.7 billion in damages Friday after finding that Apple infringed claims from two haptics patents," reports CNBC: Taction sued Apple in 2021 in the U.S. District Court for the Southern District of California. The company alleged that Apple was improperly "capitalizing on Taction's innovation and success" by selling devices that infringed on its vibration technology, according to the complaint. Apple initially won dismissal in 2023, and the Federal Circuit later revived the case.... Taction argued that Apple's "Taptic Engine," which is embedded in its Apple Watches and iPhones, uses its inventions without proper license or authority. Taction's lead counsel told CNBC "Taction waited five and a half years for this case to get to trial, so it was a long time coming." CNBC also reported that the jury "did not find Apple's infringement willful" — and that Apple said they'd appeal.

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A new Brookings Institution study notes the "strikingly physical" economic footprint of AI's buildout, from specialized chips and electricity to purpose-built data centers. (Two-thirds of a data center's costs are IT equipment, with one-third going to real estate and its associated power infrastructure.) "At an average of 3.63 percent of GDP per year, the projected buildout would be larger relative to the economy than the major U.S. canal, railroad, electrification, highway, and telecommunications investment booms." This is pushing up prices for workers, electricity, and even commercial real estate (as well as consumer products that use chips), notes the Wall Street Journal, and reducing the construction on new houses and apartment buildings. And in addition, the paper points out, projections for this buildout "would double the electricity consumption of the entire U.S. residential sector." The calculations come from Columbia Business School finance/real estate professor Stijn van Nieuwerburgh — and Reuters explains their significance: Just as the rail and telecoms expansions led to notable bubbles and busts, Van Nieuwerburgh wrote that the extent of the buildout, the still-untested revenue streams, and the intricate financing structure emerging around AI mean it could be primed for a fall. "This is freaking complicated," he said in a briefing with reporters of the arrangements emerging between AI firms, major tech hyperscalers, banks, private credit lenders, real estate firms, and a host of other players involved in building what he conservatively estimated at 183 gigawatts worth of new data-center capacity over the next seven years, compared with about 57 gigawatts currently installed.... The investment underway already has outstripped what the major players can fund from their own cash flows. The shift to outside financing has increased leverage, redistributed risks across the economy, and made the venture dependent on revenue streams that have yet to be proven, Van Nieuwerburgh noted in the paper, which will be presented on Friday... "These developments do not imply that financial distress is imminent. Strong growth in AI applications, high utilization, and continued improvements in model capability could support the projected infrastructure and generate stable cash flows," he wrote. "But the combination of uncertain demand, rapid technological change, execution bottlenecks, and high leverage creates meaningful downside risk if expectations are revised." As an example, he wrote that the AI industry will need to be earning about $3.7 trillion in annual revenue by 2032 to achieve the expected return on the investment, and "given current estimates of annual combined revenues of OpenAI and Anthropic of around $100 billion, revenues would need to grow at roughly 80% per year." The paper suggests policies that "improve measurement and transparency" for financing.

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Waymo's self-driving car technology "continues to outperform human benchmarks," the company claimed this week. "It was involved in 841 fewer injury-causing crashes — an 82% reduction compared to human drivers." Electrek reports: We've seen various Waymo crash data before, with Waymo claiming crash reductions. That's all well and good when the company says it, but we've also seen independent data confirming similar (though lower) crash reduction numbers... Waymo has enough miles that it's ready to start quoting how many injuries it has prevented, and the number is pretty high. Its newest crash data states that it had operated a total of 271 million driverless miles through June of this year, which is 50 million more miles added in the 3 months since its end-of-March update. Over those miles, Waymo says there was an 82% reduction in crashes that caused injury, and a 95% reduction in crashes that cause "serious injury or worse" [compared to human drivers]. Waymo also says that compared to human drivers it's reduced injury-causing crashes involving pedestrians by 93%, cyclists by 86%, and motorcyclists by 82%. Waymo's analysis comes from San Francisco, Los Angeles, Austin, Atlanta, and Phoenix, and its blog post includes video showing some near-misses where it says its automated system prevented an injury-causing collision.

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