Google Photos 3.0 Released, Bringing Smarter Sharing, Suggestions and Shared Libraries

Google is rolling out Google Photos 3.0, which features an AI-powered Suggested Sharing feature along with Shared Libraries, “both of which are designed to make the Google Photos app a more social experience, rather than just a personal collection of photo memories,” reports TechCrunch. From the report: With the addition of Suggested Sharing, Google Photos will now prompt you to share photos you took by pushing an alert to your smartphone. The feature will identify people in the photos using facial recognition technology and machine learning, which helps it understand who you typically share photos with, among other things. It also looks at the photos you’ve taken at a particular location, before organizing them in a ready-to-share album by selecting the best shots (e.g., removing blurry or dark photos). You can edit the album if you choose, then share with the people the app suggests, remove suggestions, or add others. Even if your friends or family doesn’t use Google Photos, you can share by sending them a link via text or email. A second feature called Shared Libraries is designed more for use with families or significant others. This lets you either share your entire photo collection with someone else, or you can configure it to share only selected photos — for example, photos of your children.

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$7.5 Billion Kemper Power Plant Suspends Coal Gasification

romanval writes: A coal gasification plant in Mississippi is iswitching to natural gas after 5 years of delays and $4 billion cost overrun. Megan Geuss writes via Ars Technica: “The Kemper County plant was supposed to be a cutting-edge demonstration of the power of ‘clean coal,’ and, despite running five years late and more than $4 billion over budget, Kemper was able to start testing its coal gasification operations late last year. The plant used a chemical process to break down lignite coal into synthesis gas, or ‘syngas,’ which was then fed into a generator. The syngas burns cleaner than pulverized lignite coal does. In addition, emissions were caught by a carbon capture system and delivered to a nearby oil field to help with oil extraction. That, Southern and Mississippi Power said, would reduce the greenhouse emissions of burning lignite by up to 65 percent. But with only 200 days of gasification operations under its belt, Kemper identified more issues with its technology, including design flaws that caused leaks and ash buildup.”

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There Is a Point At Which It Will Make Economical Sense To Defect From the Electrical Grid

Michael J. Coren reports via Quartz: More than 1 million U.S. homes have solar systems installed on their rooftops. Batteries are set to join many of them, giving homeowners the ability to not only generate but also store their electricity on-site. And once that happens, customers can drastically reduce their reliance on the grid. It’s great news for those receiving utility bills. It’s possible armageddon for utilities. A new study by the consulting firm McKinsey modeled two scenarios: one in which homeowners leave the electrical grid entirely, and one in which they obtain most of their power through solar and battery storage but keep a backup connection to the grid. Given the current costs of generating and storing power at home, even residents of sunny Arizona would not have much economic incentive to leave the electric-power system completely — full grid-defection, as McKinsey refers to it — until around 2028. But partial defection, where some homeowners generate and store 80% to 90% of their electricity on site and use the grid only as a backup, makes economic sense as early as 2020. [A]s daily needs for many are supplied instead by solar and batteries, McKinsey predicts the electrical grid will be repurposed as an enormous, sophisticated backup. Utilities would step up and supply power during the few days or weeks per year when distributed systems run out of juice.

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New Study Finds How Much Sleep Fitbit Users Really Get

Fitbit has published the results of a study that uses their longitudinal sleep database to analyze millions of nights of Sleep Stages data to determine how age, gender, and duration affect sleep quality. (Sleep Stages is a relatively new Fitbit feature that “uses motion detection and heart rate variability to estimate the amount of time users spend awake in light, deep, and REM sleep each night.”) Here are the findings: The average Fitbit user is in bed for 7 hours and 33 minutes but only gets 6 hours and 38 minutes of sleep. The remaining 55 minutes is spent restless or awake. That may seem like a lot, but it’s actually pretty common. That said, 6 hours and 38 minutes is still shy of the 7+ hours the the CDC recommends adults get. For the second year in a row Fitbit data scientists found women get about 25 minutes more sleep on average each night compared to men. The percentage of time spent in each sleep stage was also similar — until you factor in age. Fitbit data shows that men get a slightly higher percentage of deep sleep than women until around age 55 when women take the lead. Women win when it comes to REM, logging an average of 10 more minutes per night than men. Although women tend to average more REM than men over the course of their lifetime, the gap appears to widen around age 50.

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Artificially Intelligent Painters Invent New Styles of Art

Dthief shares a report from New Scientist: Now and then, a painter like Claude Monet or Pablo Picasso comes along and turns the art world on its head. They invent new aesthetic styles, forging movements such as impressionism or abstract expressionism. But could the next big shake-up be the work of a machine? An artificial intelligence has been developed that produces images in unconventional styles — and much of its output has already been given the thumbs up by members of the public. The team [of researchers] modified a type of algorithm known as a generative adversarial network (GAN), in which two neural nets play off against each other to get better and better results. One creates a solution, the other judges it — and the algorithm loops back and forth until the desired result is reached. In the art AI, one of these roles is played by a generator network, which creates images. The other is played by a discriminator network, which was trained on 81,500 paintings to tell the difference between images we would class as artworks and those we wouldn’t — such as a photo or diagram, say. The discriminator was also trained to distinguish different styles of art, such as rococo or cubism. The clever twist is that the generator is primed to produce an image that the discriminator recognizes as art, but which does not fall into any of the existing styles.

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