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San Francisco-based online staffing marketplace for underemployed workers Wonolo raises $13M Series B led by Sequoia Capital (Bérénice…

Bérénice Magistretti / VentureBeat : San Francisco-based online staffing marketplace for underemployed workers Wonolo raises $13M Series B led by Sequoia Capital   —  About 5.2 million Americans are currently underemployed, defined as those who are seeking full-time work but receive fewer than 30 hours per week from their employer.

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San Francisco-based online staffing marketplace for underemployed workers Wonolo raises $13M Series B led by Sequoia Capital (Bérénice…

Bérénice Magistretti / VentureBeat : San Francisco-based online staffing marketplace for underemployed workers Wonolo raises $13M Series B led by Sequoia Capital   —  About 5.2 million Americans are currently underemployed, defined as those who are seeking full-time work but receive fewer than 30 hours per week from their employer.

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Want to fool a computer vision system? Just tweak some colors

Research into machine learning and the interesting AI models created as a consequence are popular topics these days. But there’s a sort of shadow world of scientists working to undermine these systems — not to show they’re worthless but to shore up their weaknesses. A new paper demonstrates this by showing how vulnerable image recognition models are to the simplest color manipulations of the pictures they’re meant to identify. It’s not some deep indictment of computer vision — techniques to “beat” image recognition systems might just as easily be characterized as situations in which they perform particularly poorly. Sometimes this is something surprisingly simple: rotating an image, for example, or adding a crazy sticker . Unless a system has been trained specifically on a given manipulation or has orders to check common variations like that, it’s pretty much just going to fail. In this case it’s research from the University of Washington led by grad student Hossein Hosseini. Their “adversarial” imagery was similarly simple: switch up the colors. Probably many of you have tried something similar to this when fiddling around in an image manipulation program: by changing the “hue” and “saturation” values on a picture, you can make someone have green skin, a banana appear blue, and so on. That’s exactly what the researchers did: twiddled the knobs so a dog looked a bit yellow, a deer looked purplish, etc. The original images are at left; color-shifted versions and the systems’ best guesses at right.

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ServiceTitan is LA’s least likely contender to be the next billion dollar startup

The city of Glendale, Calif. seems like an unlikely place to grow one of the next billion dollar startups in the booming Los Angeles tech ecosystem. Located at the southeastern tip of the San Fernando Valley, the Los Angeles suburb counts its biggest employers as the adhesive manufacturer Avery Dennison; the Los Angeles industrial team for the real estate developer CBRE; the International House of Pancakes; Disney Consumer Products; DreamWorks Studios; Walt Disney Animation and Univision. “Silicon Beach” this ain’t. But it’s here in the (other) Valley’s southernmost edge that investors have found a startup they consider to be the next potential billion dollar “unicorn” that will come out of Los Angeles. The company is  ServiceTitan,  and its market… is air conditioners. More specifically, it’s the contractors that service equipment like the heating, ventilation and cooling systems at commercial and residential properties across the U.S. Founded by Ara Mahdessian and Vahe Kuzoyan back in 2012, ServiceTitan is very much an up-and-coming billion-dollar business that’s a family (minded) affair. Mahdessian and Kuzoyan met on a ski trip organized by the Armenian student associations at Stanford and the University of Southern California back when both men were in college. Both programmers, the two men reconnected after doing stints as custom developers during and after college and then when they were developing tools for their families’ businesses as residential contractors in the Los Angeles suburb of Glendale. The two men built a suite of services to help contractors like their fathers manage their businesses. Now following a $62 million round of funding led by Battery Ventures last month, the company is worth roughly $800 million, according to people with knowledge of the investment, and is on its way to becoming Los Angeles’ next billion-dollar business. Battery isn’t the only marquee investor to find value in ServiceTitan’s business developing software managing day labor compelling.

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Who’s a good AI? Dog-based data creates a canine machine learning system

We’ve trained machine learning systems to identify objects, navigate streets and recognize facial expressions, but as difficult as they may be, they don’t even touch the level of sophistication required to simulate, for example, a dog. Well, this project aims to do just that — in a very limited way, of course. By observing the behavior of A Very Good Girl, this AI learned the rudiments of how to act like a dog. It’s a collaboration between the University of Washington and the Allen Institute for AI, and the resulting paper will be presented at CVPR in June . Why do this? Well, although much work has been done to simulate the sub-tasks of perception like identifying an object and picking it up, little has been done in terms of “understanding visual data to the extent that an agent can take actions and perform tasks in the visual world.” In other words, act not as the eye, but as the thing controlling the eye. And why dogs? Because they’re intelligent agents of sufficient complexity, “yet their goals and motivations are often unknown a priori .” In other words, dogs are clearly smart, but we have no idea what they’re thinking. As an initial foray into this line of research, the team wanted to see if by monitoring the dog closely and mapping its movements and actions to the environment it sees, they could create a system that accurately predicted those movements. In order to do so, they loaded up a Malamute named Kelp M. Redmon with a basic suite of sensors. There’s a GoPro camera on Kelp’s head, six inertial measurement units (on the legs, tail and trunk) to tell where everything is, a microphone and an Arduino that tied the data together. They recorded many hours of activities — walking in various environments, fetching things, playing at a dog park, eating — syncing the dog’s movements to what it saw. The result is the Dataset of Ego-Centric Actions in a Dog Environment, or DECADE, which they used to train a new AI agent. This agent, given certain sensory input — say a view of a room or street, or a ball flying past it — was to predict what a dog would do in that situation

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Spectral Edge’s image enhancing tech pulls in $5.3M

Cambridge, U.K.-based startup  Spectral Edge  has closed a $5.3M Series A funding round from existing investors Parkwalk Advisors and IQ Capital. The team, which in 2014 spun the business out of academic research at the University of East Anglia, has developed a mathematical  technique for improving photographic imagery in real-time, also using machine learning technology.  As we’ve reported previously , their technology — which can be embedded in software or in silicon — is designed to enhance pictures and videos on mass-market devices. Mooted use cases include for enhancing low light smartphone images, improving security camera footage or even for drone cameras.  This month Spectral Edge announced its first customer, IT services provider NTT data, which said it would be incorporating the technology into its broadcast infrastructure offering — to offer its customers an “HDR-like experience”, via improved image quality, without the need for them to upgrade their hardware. “We are in advanced trials with a number of global tech companies — household names — and hope to be able to announce more deals later this year,” CEO Rhodri Thomas tells us, adding that he expects 2-3 more deals in the broadcast space to follow “soon”, and enhance viewing experiences “in a variety of ways”. On the smartphone front, Thomas says the company is waiting for consumer hardware to catch up — noting that RGB-IR sensors “haven’t yet begun to deploy on smartphones on a great scale”. Once the smartphone hardware is there he reckons its technology will be able to help with various issues such as white balancing and bokeh processing. “Right now there is no real solution for white balancing across the whole image on smartphones — so you’ll get areas of the image with excessive blues or yellows, perhaps, because the balance is out — but our tech allows this to be solved elegantly and with great results,” he suggests. “We also can support bokeh processing by eliminating artifacts that are common in these images.” The new funding is going towards ramping up Spectral Edge’s efforts to commercialize its tech, including by growing the R&D team to 12 — with hires planned for specialists in image processing, machine learning and embedded software development. The startup will also focus on developing real-world apps for smartphones, webcams and security applications alongside its existing products for the TV & display industries. “The company is already very IP strong, with 10 patent families in the world (some granted, some filed and a couple about to be filed),” says Thomas. “The focus now is productizing and commercializing.” “In a year, I expect our technology to be launched or launching on major flagship smartphone devices,” he adds. “We also believe that by then our CVD (color vision deficiency) product, Eyeteq, is helping millions of people suffering from color blindness to enjoy significantly better video experiences.”

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Department of Energy hosts competition to train cyber defense warriors

From leaked passwords to identity theft, cybersecurity issues are constantly in the news. Few issues, though, are as important — or as under-reported by the media — as the security of America’s industrial control infrastructure. Oil rigs, power plants, water treatment facilities and other critical infrastructure are increasingly connecting to the internet, but often without the kinds of foolproof security systems in place to ensure bad actors can’t gain access or disrupt service delivery. This is a growing area of the economy with a wealth of jobs, but few students even realize that industrial and infrastructure cybersecurity is an interesting career path. So, over the past three years, the Department of Energy has hosted a Cyber Defense Competition to encourage university students to engage in the field. The latest incarnation of the completion was held this past weekend and hosted by Argonne, Pacific Northwest and Oak Ridge national laboratories. Lewis University won the competition this year in a total field of 25 entrants. That is up from 15 teams last year, and 9 teams in the inaugural competition. Nate Evans leads the program at Argonne, and explained to me the design of the competition. Teams get a month before the competition to learn how to defend industrial control systems against hackers. Each team is given a small industrial control system that emulates a real-world model.

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Q1 2018 Global Investment Report: jump in late-stage deals pushes total dollar volume to nearly $77B, up 106.8% YoY, while overall deal volume is up…

Jason D. Rowley / Crunchbase News : Q1 2018 Global Investment Report: jump in late-stage deals pushes total dollar volume to nearly $77B, up 106.8% YoY, while overall deal volume is up 16.4% YoY   —  The first quarter of 2018 came in roaring for the tech industry but ended up a little rough around the edges.

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Machine learning as a service: Can privacy be taught?

Machine learning requires massive amounts of data to teach the model. But we're often uploading that data to machine learning cloud services run by folks like Amazon and Google, where it might be exposed to malicious actors. Can we use machine-learning-as-service and protect privacy?

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