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Volvo Adds In-Car Cameras to Monitor Drunk, Distracted Drivers

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Speeding, intoxication, and distraction are primary traffic safety concerns.

And since Volvo already addressed the first with a new top speed limit, the Swedish automaker is moving on to the next two.

In a Wednesday announcement, Volvo revealed plans to install a suite of in-car cameras and sensors to monitor drivers.

If, for instance, a clearly drunk or distracted motorist doesn’t respond to warning signals, the vehicle could automatically intervene by limiting its speed, alerting the Volvo on Call assistance service, or even slowing down and parking the car.

“When it comes to safety, our aim is to avoid accidents altogether rather than limit the impact when an accident is imminent and unavoidable,” according to Henrik Green, senior vice president of research and development at Volvo Cars. “In this case, cameras will monitor for behavior that may lead to serious injury or death.”

That could mean someone who has fallen asleep at the wheel or is preoccupied with a mobile phone, as well as extreme weaving across lanes or excessively slow reaction times.

Cameras will begin appearing in all Volvo models starting in the early 2020s; details on the number of sensors and their placement will be revealed closer to launch.

“There are many accidents that occur as a result of intoxicated drivers,” Trent Victor, professor of driver behavior at Volvo Cars, said in a statement. “Some people still believe that they can drive after having had a drink, and that this will not affect their capabilities.

“We want to ensure that people are not put in danger as a result of intoxication,” he added.

As part of Volvo Vision 2020—a plan to reduce the number of people who die or are seriously injured in road traffic accidents to zero—the company identified “gaps” in its safety protocols.

Unsurprisingly, speeding is a “very prominent” one.

Early this month, Volvo sent a “strong signal” about the dangers of speeding by promising to limit cars’ top rate of motion at 112 mph. The manufacturer is also looking into how a combination of smart speed control and geofencing technology can automatically slow down vehicles near schools and hospitals.

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Adobe’s New AI Tool Can Identify Photoshopped Faces

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The Internet cannot be trusted: Between doctored photos and deepfaked videos, there’s just no telling what is fact and fiction.

In an effort to regulate the digital Wild West it helped usher in 30 years ago, Photoshop maker Adobe developed a new tool for identifying altered images.

Researchers Richard Zhang and Oliver Wang—along with UC Berkeley collaborators Sheng-Yu Wang, Andrew Owens, and Alexei Efros—created a method for detecting edits made using Photoshop’s Face Aware Liquify filter.

The function automatically distinguishes facial features, making it easy to adjust eye size, nose height, smile width, and face shape.

Popular with photographers who didn’t quite capture the expression they wanted, the feature’s delicate effects “made it an intriguing test case for detecting both drastic and subtle alterations to faces,” an Adobe blog post said.

“While we are proud of the impact that Photoshop and Adobe’s other creative tools have made on the world, we also recognize the ethical implications of our technology,” the company wrote.

“Trust in what we see is increasingly important in a world where image editing has become ubiquitous,” it continued. “Fake content is a serious and increasingly pressing issue.”

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With that in mind, Adobe partnered with the University of California, Berkeley, as part of a broader effort to better expose image, video, audio, and document manipulations.

Using pictures scraped from the Internet—as well as some modified by a human artist—the team trained a Convolutional Neural Network (CNN) to recognize altered images of faces.

“We started by showing image pairs (an original and an alteration) to people who knew that one of the faces was altered,” Oliver Wang said in a statement. “For this approach to be useful, it should be able to perform significantly better than the human eye at identifying edited faces.”

Spoiler alert: It does.

Flesh-and-blood people were able to ID the revised face 53 percent of the time (slightly better than chance), the neural network achieved results as high as 99 percent.

The tool also pinpointed specific areas and methods of facial warping, and was able to revert images to what it estimated was their original state. The results, according to Adobe, impressed “even the researchers.”

Adobe’s new tool was nearly twice as good at identifying manipulated images as humans (via Adobe/UC Berkeley)

“It might sound impossible because there are so many variations of facial geometry possible,” UC Berkeley professor Efros said. ‘But, in this case, because deep learning can look at a combination of low-level image data, such as warping artifacts, as well as higher level cues such as layout, it seems to work.”

This isn’t the end of fake news just yet, though.

“The idea of a magic universal ‘undo’ button to revert image edits is still far from reality,” Zhang admitted, bursting our collective bubble. “But we live in a world where it’s becoming harder to trust the digital information we consume, and I look forward to further exploring this area of research.”

“This is an important step in being able to detect certain types of image editing, and the undo capability works surprisingly well,” Gavin Miller, head of Adobe Research, added.

“Beyond technologies like this,” he said, “the best defense will be a sophisticated public who know that content can be manipulated—often to delight them, but sometimes to mislead them.”

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A Smart Speaker Could Save You From Cardiac Arrest

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My fiancé is a deep sleeper: If I stopped breathing or began gasping for air in the middle of the night, he’d snore right through it.

But a smart speaker could save my life.

Researchers at the University of Washington developed a tool to monitor people for cardiac arrest when they’re asleep.

Nearly 500,000 Americans die each year from sudden heart failure—many in the comfort of their own bedroom.

A new skill for a smart speaker or phone, however, could detect the gasping sound of abnormal breathing and call for help.

On average, the proof-of-concept tool—trained on real agonal breathing instances captured from 911 calls—identified labored breathing patterns 97 percent of the time, from up to 20 feet away.

The findings were published this week in a Nature journal.

“A lot of people have smart speakers in their homes, and these devices have amazing capabilities that we can take advantage of,” study co-author Shyam Gollakota, an associate professor at UW, said in a statement.

Researchers envision a contactless system that works by continuously and passively monitoring the bedroom for an agonal breathing event and call for help (via Sarah McQuate/University of Washington)

Picture this: You’re enjoying a pleasant vision of shirtless Hugh Jackman serenading you on a yacht as it cruises through the canals of Venice. Suddenly, your heart stops pumping and blood flow ceases; you’re fighting for breath, making guttural gasping noises, and involuntarily twitching. Neither Hugh nor anyone else is there to perform first aid.

The smart speaker on the bookshelf, however, recognizes signs of agonal breathing and calls 911.

You’re alive and well to stalk Hugh Jackman live another day.

Researchers gathered more than 162 IRL 911 calls to Seattle’s Emergency Medical Services, collecting short recordings on different devices, including an Amazon smart speaker, iPhone 5s, and Samsung Galaxy S4.

They added interfering sounds like barking dogs, honking cars, and humming A/C units: “Things that you might normally hear in a home,” according to first author and UW doctoral student Justin Chan.

And accounted for distractions like snoring or obstructive sleep apnea.

“We don’t want to alert either emergency services or loved ones unnecessarily, so it’s important that we reduce our false-positive rate,” Chen said.

Moving forward, the team envisions the algorithm functioning like a mobile app or Alexa skill that runs in passively—in real time, so there’s no need to store data or send it to the cloud—while people sleep.

“Cardiac arrests are a very common way for people to die, and right now many of them can go unwitnessed,” co-author Jacon Sunshine, an assistant professor at the UW School of Medicine, said. “Part of what makes this technology so compelling is that it could help us catch more patients in time for them to be treated.”

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Netflix Hacks Highlight What Could Be

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Netflix’s internal hackathons have gained a reputation for producing some genuinely great ideas—from quitting when Fitbit detects you’re asleep, to a floating desktop window that lets you stream content while you work.

And Hack Day 2019 did not disappoint.

Last month’s event focused on “studio efforts” (whatever that means).

“The goal remained the same: team up with new colleagues and have fun while learning, creating, and experimenting,” according to a company blog post. “We know even the silliest idea can spur something more.”

Project Rumble Pak

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Picture this: You’re watching an episode of Stranger Things. Eleven—a psychokinetic teenager—mind-pushes a table across the room. Your phone violently shakes with the action.

No matter how great a special effects team is, sight and sound alone can only take an audience so far. Which is why Hans van de Bruggen and Ed Barker developed Project Rumble Pak.

“The … Hack Day project explores how haptics can enhance the content you’re watching,” Netflix explained. “With every explosion, sword clank, and laser blast, you get force feedback to amp up the excitement.”

Barker and van de Bruggen focused on animated series Voltron, synchronizing video content with haptic effects using Immersion Corporation technology.

The Voice of Netflix

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If Netflix could speak, what would it sound like?

A weird amalgamation of characters and vocalizations, apparently.

Carenina Garcia Motion and Guy Cirino teamed up to create “The Voice of Netflix“: a neural network trained to spot words in streaming content and reassemble them into new sentences—on demand.

Type any sentence into the online portal, and listen for yourself.

The current vocabulary is limited to 2,340 words (with more to come), meaning the digital voice will simply skip over any unfamiliar terms—including “Netflix”—to create stunted, hilariously awkward sentences.

Not every Netflix Hack Day concept is directly related to the streaming platform.

This year, a team invented TerraVision—a new way for filmmakers to search for and discover locations. Just drop a photo of a particular look (a house, a park, a castle, a swimming pool) into an interface to find the closest matches.

Another dreamed up a clever way to clear out office conference rooms.

With the click of a button, Netflix employees the world over can nudge co-workers out of a meeting room after their session has ended.

The web app looks up calendar events associated with a given space and finds the latest session that should have ended, then automatically calls into the room, playing walk-off music (just like at the Oscars).

“The most important value of hack days is that they support a culture of innovation,” the Netflix TechBlog said. “We believe in this work, even if it never ships, and love to share the creativity and thought put into these ideas.”

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