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Google brings its Jacquard wearables tech to Levi’s Trucker Jacket – TechCrunch

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Back in 2015, Google’s ATAP team demoed a new kind of wearable tech at Google I/O that used functional fabrics and conductive yarns to allow you to interact with your clothing and, by extension, the phone in your pocket. The company then released a jacket with Levi’s in 2017, but that was expensive, at $350, and never really quite caught on. Now, however, Jacquard is back. A few weeks ago, Saint Laurent launched a backpack with Jacquard support, but at $1,000, that was very much a luxury product. Today, however, Google and Levi’s are announcing their latest collaboration: Jacquard-enabled versions of Levi’s Trucker Jacket.

These jackets, which will come in different styles, including the Classic Trucker and the Sherpa Trucker, and in men’s and women’s versions, will retail for $198 for the Classic Trucker and $248 for the Sherpa Trucker. In addition to the U.S., it’ll be available in Australia, France, Germany, Italy, Japan and the U.K.

The idea here is simple and hasn’t changed since the original launch: a dongle in your jacket’s cuff connects to conductive yarns in your jacket. You can then swipe over your cuff, tap it or hold your hand over it to issue commands to your phone. You use the Jacquard phone app for iOS or Android to set up what each gesture does, with commands ranging from saving your location to bringing up the Google Assistant in your headphones, from skipping to the next song to controlling your camera for selfies or simply counting things during the day, like the coffees you drink on the go. If you have Bose noise-canceling headphones, the app also lets you set a gesture to turn your noise cancellation on or off. In total, there are currently 19 abilities available, and the dongle also includes a vibration motor for notifications.

What’s maybe most important, though, is that this (re-)launch sets up Jacquard as a more modular technology that Google and its partners hope will take it from a bit of a gimmick to something you’ll see in more places over the next few months and years.

“Since we launched the first product with Levi’s at the end of 2017, we were focused on trying to understand and working really hard on how we can take the technology from a single product […] to create a real technology platform that can be used by multiple brands and by multiple collaborators,” Ivan Poupyrev, the head of Jacquard by Google told me. He noted that the idea behind projects like Jacquard is to take things we use every day, like backpacks, jackets and shoes, and make them better with technology. He argued that, for the most part, technology hasn’t really been added to these things that we use every day. He wants to work with companies like Levi’s to “give people the opportunity to create new digital touchpoints to their digital life through things they already have and own and use every day.”

What’s also important about Jacquard 2.0 is that you can take the dongle from garment to garment. For the original jacket, the dongle only worked with this one specific type of jacket; now, you’ll be able to take it with you and use it in other wearables as well. The dongle, too, is significantly smaller and more powerful. It also now has more memory to support multiple products. Yet, in my own testing, its battery still lasts for a few days of occasional use, with plenty of standby time.

jacquard dongle

Poupyrev also noted that the team focused on reducing cost, “in order to bring the technology into a price range where it’s more attractive to consumers.” The team also made lots of changes to the software that runs on the device and, more importantly, in the cloud to allow it to configure itself for every product it’s being used in and to make it easier for the team to add new functionality over time (when was the last time your jacket got a software upgrade?).

He actually hopes that over time, people will forget that Google was involved in this. He wants the technology to fade into the background. Levi’s, on the other hand, obviously hopes that this technology will enable it to reach a new market. The 2017 version only included the Levi’s Commuter Trucker Jacket. Now, the company is going broader with different styles.

“We had gone out with a really sharp focus on trying to adapt the technology to meet the needs of our commuter customer, which a collection of Levi’s focused on urban cyclists,” Paul Dillinger, the VP of Global Product Innovation at Levi’s, told me when I asked him about the company’s original efforts around Jacquard. But there was a lot of interest beyond that community, he said, yet the built-in features were very much meant to serve the needs of this specific audience and not necessarily relevant to the lifestyles of other users. The jackets, of course, were also pretty expensive. “There was an appetite for the technology to do more and be more accessible,” he said — and the results of that work are these new jackets.

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Dillinger also noted that this changes the relationship his company has with the consumer, because Levi’s can now upgrade the technology in your jacket after you bought it. “This is a really new experience,” he said. “And it’s a completely different approach to fashion. The normal fashion promise from other companies really is that we promise that in six months, we’re going to try to sell you something else. Levi’s prides itself on creating enduring, lasting value in style and we are able to actually improve the value of the garment that was already in the consumer’s closet.”

I spent about a week with the Sherpa jacket before today’s launch. It does exactly what it promises to do. Pairing my phone and jacket took less than a minute and the connection between the two has been perfectly stable. The gesture recognition worked very well — maybe better than I expected. What it can do, it does well, and I appreciate that the team kept the functionality pretty narrow.

Whether Jacquard is for you may depend on your lifestyle, though. I think the ideal user is somebody who is out and about a lot, wearing headphones, given that music controls are one of the main features here. But you don’t have to be wearing headphones to get value out of Jacquard. I almost never wear headphones in public, but I used it to quickly tag where I parked my car, for example, and when I used it with headphones, I found using my jacket’s cuffs easier to forward to the next song than doing the same on my headphones. Your mileage may vary, of course, and while I like the idea of using this kind of tech so you need to take out your phone less often, I wonder if that ship hasn’t sailed at this point — and whether the controls on your headphones can’t do most of the things Jacquard can. Google surely wants Jacquard to be more than a gimmick, but at this stage, it kind of still is.

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Apple and Google’s AI wizardry promises privacy—at a cost

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Since the dawn of the iPhone, many of the smarts in smartphones have come from elsewhere: the corporate computers known as the cloud. Mobile apps sent user data cloudward for useful tasks like transcribing speech or suggesting message replies. Now Apple and Google say smartphones are smart enough to do some crucial and sensitive machine learning tasks like those on their own.

At Apple’s WWDC event this month, the company said its virtual assistant Siri will transcribe speech without tapping the cloud in some languages on recent and future iPhones and iPads. During its own I/O developer event last month, Google said the latest version of its Android operating system has a feature dedicated to secure, on-device processing of sensitive data, called the Private Compute Core. Its initial uses include powering the version of the company’s Smart Reply feature built into its mobile keyboard that can suggest responses to incoming messages.

Apple and Google both say on-device machine learning offers more privacy and snappier apps. Not transmitting personal data cuts the risk of exposure and saves time spent waiting for data to traverse the internet. At the same time, keeping data on devices aligns with the tech giants’ long-term interest in keeping consumers bound into their ecosystems. People that hear their data can be processed more privately might become more willing to agree to share more data.

The companies’ recent promotion of on-device machine learning comes after years of work on technology to constrain the data their clouds can “see.”

In 2014, Google started gathering some data on Chrome browser usage through a technique called differential privacy, which adds noise to harvested data in ways that restrict what those samples reveal about individuals. Apple has used the technique on data gathered from phones to inform emoji and typing predictions and for web browsing data.

More recently, both companies have adopted a technology called federated learning. It allows a cloud-based machine learning system to be updated without scooping in raw data; instead, individual devices process data locally and share only digested updates. As with differential privacy, the companies have discussed using federated learning only in limited cases. Google has used the technique to keep its mobile typing predictions up to date with language trends; Apple has published research on using it to update speech recognition models.

Rachel Cummings, an assistant professor at Columbia who has previously consulted on privacy for Apple, says the rapid shift to do some machine learning on phones has been striking. “It’s incredibly rare to see something going from the first conception to being deployed at scale in so few years,” she says.

That progress has required not just advances in computer science but for companies to take on the practical challenges of processing data on devices owned by consumers. Google has said that its federated learning system only taps users’ devices when they are plugged in, idle, and on a free internet connection. The technique was enabled in part by improvements in the power of mobile processors.

Beefier mobile hardware also contributed to Google’s 2019 announcement that voice recognition for its virtual assistant on Pixel devices would be wholly on-device, free from the crutch of the cloud. Apple’s new on-device voice recognition for Siri, announced at WWDC this month, will use the “neural engine” the company added to its mobile processorsto power up machine learning algorithms.

The technical feats are impressive. It’s debatable how much they will meaningfully change users’ relationship with tech giants.

Presenters at Apple’s WWDC said Siri’s new design was a “major update to privacy” that addressed the risk associated with accidentally transmitting audio to the cloud, saying that was users’ largest privacy concern about voice assistants. Some Siri commands—such as setting timers—can be recognized wholly locally, making for a speedy response. Yet in many cases transcribed commands to Siri—presumably including from accidental recordings—will be sent to Apple servers for software to decode and respond. Siri voice transcription will still be cloud-based for HomePod smart speakers commonly installed in bedrooms and kitchens, where accidental recording can be more concerning.

Google also promotes on-device data processing as a privacy win and has signaled it will expand the practice. The company expects partners such as Samsung that use its Android operating system to adopt the new Privacy Compute Core and use it for features that rely on sensitive data.

Google has also made local analysis of browsing data a feature of its proposal for reinventing online ad targeting, dubbed FLoC and claimed to be more private. Academics and some rival tech companies have said the design is likely to help Google consolidate its dominance of online ads by making targeting more difficult for other companies.

Michael Veale, a lecturer in digital rights at University College London, says on-device data processing can be a good thing but adds that the way tech companies promote it shows they are primarily motivated by a desire to keep people tied into lucrative digital ecosystems.

“Privacy gets confused with keeping data confidential, but it’s also about limiting power,” says Veale. “If you’re a big tech company and manage to reframe privacy as only confidentiality of data, that allows you to continue business as normal and gives you license to operate.”

A Google spokesperson said the company “builds for privacy everywhere computing happens” and that data sent to the Private Compute Core for processing “needs to be tied to user value.” Apple did not respond to a request for comment.

Cummings of Columbia says new privacy techniques and the way companies market them add complexity to the trade-offs of digital life. Over recent years, as machine learning has become more widely deployed, tech companies have steadily expanded the range of data they collect and analyze. There is evidence some consumers misunderstand the privacy protections trumpeted by tech giants.

A forthcoming survey study from Cummings and collaborators at Boston University and the Max Planck Institute showed descriptions of differential privacy drawn from tech companies, media, and academics to 675 Americans. Hearing about the technique made people about twice as likely to report they would be willing to share data. But there was evidence that descriptions of differential privacy’s benefits also encouraged unrealistic expectations. One-fifth of respondents expected their data to be protected against law enforcement searches, something differential privacy does not do. Apple’s and Google’s latest proclamations about on-device data processing may bring new opportunities for misunderstandings.

This story originally appeared on wired.com.

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Amazon joins Apple, Google by reducing its app store cut

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Enlarge / The Amazon Fire HD 8 tablet, which runs Amazon’s Fire OS.

Apparently following the lead of Apple and Google, Amazon has announced that it will take a smaller revenue cut from apps developed by teams earning less than $1 million annually from their apps on the Amazon Appstore. The same applies to developers who are brand-new to the marketplace.

The new program from Amazon, called the Amazon Appstore Small Business Accelerator Program, launches in Q4 of this year, and it will reduce the cut Amazon takes from app revenue, which was previously 30 percent. (Developers making over $1 million annually will continue to pay the original rate.) For some, it’s a slightly worse deal than Apple’s or Google’s, and for others, it’s better.

Amazon’s new indie-friendly rate is 20 percent, in contrast to Apple’s and Google’s 15 percent. Amazon seeks to offset this difference by granting developers 10 percent of their Appstore revenue in the form of a credit for AWS. For certain developers who use AWS, it could mean that Amazon’s effective cut is actually 10 percent, not 15 or 20 percent.

But for some, it amounts to something more like giving the developer a coupon on a purchase of services from Amazon than actually putting more cash in their pockets. It leaves small developers who aren’t spending a bunch of money on Amazon’s services with a worse deal than they’d get on Apple’s or Google’s marketplaces.

As with Apple’s program—but not Google’s—the lower rate applies to developers only if they made $1 million or less in total (in this case, the numbers assessed are those from the previous year). Crossing that threshold will lead developers to pay the older, higher rate on all of their earnings. In contrast, Google always takes a smaller cut of the first million in a given year and then applies the bigger cut to revenues after $1 million without changing the amount it took from the first million.

The Amazon Appstore primarily exists as the app store for Amazon’s Android-based Fire OS software that runs on tablets. It’s also offered as an alternative App Store for users of other Android-based operating systems.

All three companies are facing various forms of regulatory scrutiny, and that scrutiny was likely a factor in Apple’s decision to cut the fees it applies to apps released by small developers on the Apple App Store. Google followed shortly afterward for its Google Play marketplace.

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Microsoft’s Linux repositories were down for 18+ hours

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Enlarge / In 2017, Tux was sad that he had a Microsoft logo on his chest. In 2021, he’s mostly sad that Microsoft’s repositories were down for most of a day.

Jim Salter

Yesterday, packages.microsoft.com—the repository from which Microsoft serves software installers for Linux distributions including CentOS, Debian, Fedora, OpenSUSE, and more—went down hard, and it stayed down for around 18 hours. The outage impacted users trying to install .NET Core, Microsoft Teams, Microsoft SQL Server for Linux (yes, that’s a thing) and more—as well as Azure’s own devops pipelines.

We first became aware of the problem Wednesday evening when we saw 404 errors in the output of apt update on an Ubuntu workstation with Microsoft Teams installed. The outage is somewhat better documented at this .NET Core-issue report on Github, with many users from all around the world sharing their experiences and theories.

The short version is, the entire repository cluster which serves all Linux packages for Microsoft was completely down—issuing a range of HTTP 404 (content not found) and 500 (Internal Server Error) messages for any URL—for roughly 18 hours. Microsoft engineer Rahul Bhandari confirmed the outage roughly five hours after it was initially reported, with a cryptic comment about the infrastructure team “running into some space issues.”

Eighteen hours after the issue was reported, Bhandari reported that the mirrors were once again available—although with temporarily degraded performance, likely due to cold caches. In this update, Bhandari said that the original cause of the outage was “a regression in [apt repositories] during some feature migration work that resulted in those packages becoming unavailable on the mirrors.”

We’re still waiting for a comprehensive incident report, since Bhandari’s status updates provide clues but no real explanations. The good news is, we can confirm that packages.microsoft.com is indeed up once again, and it is serving packages as it should.

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