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Scaled Robotics keeps an autonomous eye on busy construction sites – TechCrunch

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Buildings under construction are a maze of half-completed structures, gantries, stacked materials, and busy workers — tracking what’s going on can be a nightmare. Scaled Robotics has designed a robot that can navigate this chaos and produce 3D progress maps in minutes, precise enough to detect that a beam is just a centimeter or two off.

Bottlenecks in construction aren’t limited to manpower and materials. Understanding exactly what’s been done and what needs doing is a critical part of completing a project in good time, but it’s the kind of painstaking work that requires special training and equipment. Or, as Scaled Robotics showed today at TC Disrupt Berlin 2019, specially trained equipment.

The team has created a robot that trundles autonomously around construction sites, using a 360-degree camera and custom lidar system to systematically document its surroundings. An object recognition system allows it to tell the difference between a constructed wall and a piece of sheet rock leaned against it, between a staircase and temporary stairs for electric work, and so on.

By comparing this to a source CAD model of the building, it can paint a very precise picture of the progress being made. They’ve built a special computer vision model that’s suited to the task of sorting obstructions from the constructions and identifying everything in between.

All this information goes into a software backend where the supervisors can check things like which pieces are in place on which floor, whether they have been placed within the required tolerances, or if there are safety issues like too much detritus on the ground in work areas. But it’s not all about making the suits happy.

“It’s not just about getting management to buy in, you need the guy who’s going to use it every day to buy in. So we’ve made a conscious effort to fit seamlessly into what they do, and they love that aspect of it,” explained co-founder Bharath Sankaran. “You don’t need a computer scientist in the room. Issues get flagged in the morning, and that’s a coffee conversation – here’s the problem, bam, let’s go take a look at it.”

The robot can make its rounds faster than a couple humans with measuring tapes and clipboards, certainly, but also someone equipped with a stationary laser ranging device that they carry from room to room. An advantage of simultaneous location and ranging (SLAM) tech is that it measures from multiple points of view over time, building a highly accurate and rich model of the environment.

The data is assembled automatically but the robot can be either autonomous or manually controlled — in developing it, they’ve brought the weight down from about 70 kilograms to 20, meaning it can be carried easily from floor to floor if necessary (or take the elevator); and simple joystick controls mean anyone can drive it.

A trio of pilot projects concluded this year and have resulted in paid pilots next year, which is of course a promising development.

Interestingly, the team found that construction companies were using outdated information and often more or less assumed they had done everything in the meantime correctly.

“Right now decisions are being made on data that’s maybe a month old,” said co-founder Stuart Maggs. “We can probably cover 2000 square meters in 40 minutes. One of the first times we took data on a site, they were completely convinced everything they’d done was perfect. We put the data in front of them and they found out there was a structural wall just missing, and it had been missing for 4 weeks.”

The company uses a service-based business model, providing the robot and software on a monthly basis, with prices rising with square footage. That saves the construction company the trouble of actually buying, certifying, and maintaining an unfamiliar new robotic system.

Scaled Robotics

But the founders emphasized that tracking progress is only the first hint of what can be done with this kind of accurate, timely data.

“The big picture version of where this is going is that this is the visual wiki for everything related to your construction site. You just click and you see everything that’s relevant,” said Sankaran. “Then you can provide other ancillary products, like health and safety stuff, where is storage space on site, predicting whether the project is on schedule.”

“At the moment, what you’re seeing is about looking at one moment in time and diagnosing it as quickly as possible,” said Maggs. “But it will also be about tracking that over time: We can find patterns within that construction process. That data feeds that back into their processes, so it goes from a reactive workflow to a proactive one.”

“As the product evolves you start unwrapping, like an onion, the different layers of functionality,” said Sankaran.

The company has come this far on $1 million of seed funding, but is hot on the track of more. Perhaps more importantly, its partnerships with construction giant PERI and Autodesk, which has helped push digital construction tools, may make it a familiar presence at building sites around the world soon.



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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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