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 With Apple's upcoming iPhone Ultra there's been a lingering question over the exact placement of the selfie cameras in the device's folded and unfolded states - but today we may have stumbled upon the answer.
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Lead times of nine to 12 or even 18 months. Costs rising by 35%, 45%, even 50% to 200%. More than halfway through 2026, the market for IT infrastructure that's crucial for enterprise projects, including those involving artificial intelligence, is strapped.
Memory is at the root of the shortages. Memory prices "have risen by 50% to 200%, resulting in PC prices increasing by 35% to 45% and some server prices rising over 125%," according to Jon Forest, VP analyst at Gartner. Network switches also need memory, albeit in lesser amounts than servers, so they are not immune, with prices and lead times likewise rising dramatically.
Industry experts agree that most of the issues stem from hyperscalers gobbling up memory capacity, which trickles down to servers, storage systems, and networking devices. But while the source of the problem may be new, supply chain disruptions are far from unprecedented.
As a result, industry insiders are not short on advice on how best to deal with the situation, with tips including making better use of what you have, considering options beyond your usual scope, and lots of planning with your vendors and internal finance teams.
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The 19th-century French novelist Honore de Balzac is believed to have said that behind every great fortune lies a great crime.
That's even more true today than it was 200 years ago — just look at how Big AI, including Anthropic, OpenAI, Google, and others have built their trillion-dollar fortunes.
They all use vast amounts of copyrighted material to train their large language models (LLMs) without paying the copyright holders. In other words, they steal it. They don't call it stealing, though. They call it "fair use," which in this case amounts to the same thing.
Generative AI (genAI) training requires massive amounts of text. The better-written and more information-dense that text is, the more it helps. AI gets a lot smarter a lot faster when it's trained on well-written books and magazine and newspaper articles than when it's trained on social media banter (or most everything else you find on the internet).
Since the dawn of AI, companies have been hoovering up copyrighted material wherever they find it — on the open web, behind paywalls, even in manually scanned books — and then used the scanned text. And they do it all without asking authors' or publishers' permissions, and without paying them.
It's the greatest intellectual property theft in history by a long shot — billions and b
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 Image Credit: GoogleGoogle has been publicly building tiny radar chips since 2015. They can tell you how well you sleep, control a smartwatch, count sheets of paper, and have you play the world's smallest violin. But the company's Soli radar hasn't necessarily seen commercial success, primarily in an ill-fated Pixel phone. Now Google has launched an open source API standard called Ripple that could theoretically bring the technology to additional devices outside of Google, possibly even a car, as Ford is one of the participants in the new standard.
Technically, Ripple is under the auspices of the Consumer Technology Association (CTA), the same industry body that h
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