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Category: robotics/AI – Page 1930

Chrome 74 was released only a few days ago, which means v75 has moved up to the Beta Channel. This release doesn’t have any drastic changes, but it does continue to improve the dark theme, and there are some fancy new APIs that web apps can use.

Dark theme changes

Chrome 75 includes a few changes to the dark theme that first arrived in Chrome 74, though you still have to activate the #enable-android-night-mode flag to try it out. The settings menu for it has been changed from ‘Dark mode’ to ‘Themes,’ and you can now force light mode to be on at all times (even when the system itself is in dark mode).

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The Navy just awarded Boeing a contract to build a giant robot submarine, called the Orca Extra-Large Unmanned Undersea Vehicle, which it says will prowl the depths of the ocean autonomously for months at a time.

The U.S. Naval Institute says the sub will be used for “mine countermeasures, anti-submarine warfare, anti-surface warfare, electronic warfare and strike missions.”

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Do you hear that? It’s the sound of Google executives practicing their lines ahead of Google I/O. The company’s annual developer conference in Mountain View, California, kicks off this Tuesday. The three-day event gives Google a chance to show off its latest work and set the tone for the year to come.

Can’t make it to the Shoreline Amphitheater? You can watch the entire keynote on the event page or on the Google Developers YouTube channel. It begins at 10 am PT (1 pm ET) on May 7 and should last for about 90 minutes. We’ll liveblog the whole thing here on WIRED.com.

Google I/O is technically a developer’s conference, and there should be plenty of talk about all the fun things developers can build using Google’s latest tools. But it’s also an opportunity to get consumers excited about what’s cooking in Mountain View. Last year, the company used the conference to debut its “digital wellness” initiative and a suite of new visual search tools for Google Lens. It also introduced Duplex, the eerily realistic AI assistant that can make dinner reservations and schedule haircuts like a human would.

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A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research published in the Journal of Biomedical and Health Informatics.

Around one in five suffer from anxiety and depression, collectively known as “internalizing disorders.” But because children under the age of eight can’t reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment.

“We need quick, objective tests to catch kids when they are suffering,” says Ellen McGinnis, a at the University of Vermont Medical Center’s Vermont Center for Children, Youth and Families and lead author of the study. “The majority of kids under eight are undiagnosed.”

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Twenty years ago, a cryptographic puzzle was included in the construction of a building on the MIT campus. The structure that houses what is now MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) includes a time capsule designed by the building’s architect, [Frank Gehry]. It contains artifacts related to the history of computing, and was meant to be opened whenever someone solved a cryptographic puzzle, or after 35 years had elapsed.

The puzzle was not expected to be solved early, but [Bernard Fabrot], a developer in Belgium, has managed it using not a supercomputer but a run-of-the-mill Intel i7 processor. The capsule will be opened later in May.

The famous cryptographer, [Ronald Rivest], put together what we now know is a deceptively simple challenge. It involves a successive squaring operation, and since it is inherently sequential there is no possibility of using parallel computing techniques to take any shortcuts. [Fabrot] used the GNU Multiple Precision Arithmetic Library in his code, and took over 3 years of computing time to solve it. Meanwhile another team is using an FPGA and are expecting a solution in months, though have been pipped to the post by the Belgian.

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For most patients, a diagnosis of stage 4 non-small cell lung cancer comes with a dire prognosis. But for patients with specific mutations that cause the disease, there are potentially life-saving therapies.

The problem is that these mutations, known as ALK and EGFR, are not always identified in patients — meaning they never get the treatment.

A new study from the Fred Hutchinson Cancer Research Center in Seattle used machine learning to find these needle-in-a-haystack patients. The idea was to leverage cancer databases to see if patients were being tested for the mutations and receiving these personalized treatments.

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