What’s new in tech
iPhone falls behind on photos
The iPhone camera is no longer the best in its class, said Vlad Savov in TheVerge.com. Thanks to a new wave of Android devices, Apple is in the “unusual position of having to run to catch up.” The leaders are Huawei and Google, which have raised the bar of expectation for mobile photography in recent years. Google’s Night Sight, for example, “was a revolution for nighttime imaging” when it came out last year. Google has been a leader in using its phone’s computing power to optimize images, and the upcoming “Pixel 4 is likely to push us even further into the age of unbelievable computational photography.” Outside the U.S., Huawei has introduced even more elaborate cameras, adding a 5x optical zoom to its P30 Pro. All this leaves people who care about their photos asking, “Where is the iPhone?”
A secret online trust score
There’s a score you don’t know about that websites use to evaluate whether you might be a bot or a fraudster, said Christopher Mims in The Wall Street Journal. It comes from a company called Sift, and it’s based on more than 16,000 signals to flag devices, credit cards, or accounts “that a company might want to block.” You can’t find out the score, because it’s not tied to your name. But data gathered by one company is shared with others. So when one site finds fishy behavior linked to an email, “that could mark it as risky for other companies.” One signal that an account really belongs to a human: Problems signing in. “The bots log in perfectly every time.”
More than two dozen AI experts signed a letter urging Amazon to stop selling its facial-recognition technology to law enforcement agencies, said Cade Metz and Natasha Singer in The New York Times. The experts, including a recent winner of the Turing Award—the Nobel Prize of computing—argued that Amazon’s Rekognition program is biased against women and minorities. In January, researchers at the Massachusetts Institute of Technology found Rekognition “had more trouble identifying the gender of female and darker-skinned faces in photos than similar services from IBM and Microsoft.” It mistook women for men in almost one in five cases, and did even worse with darker-skinned women, getting the gender wrong 31 percent of the time. Similar problems—since resolved—have afflicted Microsoft’s technology.