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Selected’s recruiting platform matches teachers with schools they’ll love

A “dating app for teachers” is an odd but useful way to describe the startup Selected, which has just closed on $1.2 million in seed funding for its recruiting platform for educators. And, in all fairness, Selected said it first. The startup’s own website describes itself (a bit tongue-in-cheek) as a “dating app for job-seeking teachers and hiring schools.”

Before you roll your eyes at the shorthand being used here, let’s skip ahead to the main point. And that is – like dating apps – Selected takes advantage of profile-matching technology in order to help teachers find good jobs they’ll want to keep.

With Selected, this involves connecting candidates to schools based on mutual fit in terms of personal preferences, school culture, and teaching methods, among other things.

The dating app comparison didn’t just come out of nowhere, though.

The company began as a tutoring app in New York City, during which time it had teachers building out profiles where they would details their certifications and expertise. But the team found that it was schools who had interest in this app, not parents. In fact, the schools asked if they could reach out to the tutors and offer them jobs.

Seeing an opportunity, Selected pivoted to work on a teacher-to-schools matching app instead, instead of one for tutors.

Another reason for the comparison is that early employee, COO Eric Kim, was formerly a senior product manager at the dating app OKCupid.

“We started talking to him early on as we were thinking about how matching should be designed,” explains Selected co-founder and CEO Waine Tam, a Princeton grad whose own background is in software engineering and education.”[Selected is] similar to a dating app-type interface where you answer a couple of questions about what you’re looking for,” he adds.

However, Tam cautions that – also like dating apps – matches often don’t click until teachers and those hiring them meet in real life.

But Selected can at least get the process started by asking teachers to answer questions that help schools determine if they’re a fit – things like “how much do you value progressive education?” or “do you prefer inquiry-based learning over explicit instruction?,” for example.

This is combined with the collection of more objective data schools need to know, like teachers’ certifications or where they want to work.

The company has only been through one school year cycle since its launch in May 2016, and it placed around 100 teachers through the service that was then live only in New York.

It’s since expanded to reach 10,000 teachers and over 500 pre-K-12 public and private schools. The schools signed up on its platform are largely spread across the Northeast in urban metros like NYC, Boston, Newark/Trenton/Camden, N.J., Bridgeport/New Haven, Connecticut; Philadelphia, and D.C.

The startup’s long-term goal is to help teachers find jobs they like in order to reduce turnover in the U.S. educational system.

Today, there are over 3.8 million teachers in the U.S., the company notes, making teaching one of the largest professions in the U.S. But every year, over 500,000 teachers turn over nationally – something Selected sees as an opportunity to make better matches, in the hopes of keeping teachers long-term.

One of the issues is that teachers have trouble finding jobs despite high demand because they apply to schools that have different requirements than what they bring to the table. Other times, they don’t end up in the right jobs, because the hiring process doesn’t offer a lot of transparency around critical topics, like school culture.

“The number one driver of teacher retention, or on the other side – attrition – is a poor culture match,” Tam points out.

After teachers sign up on Selected, they’re screened for certifications before being approved. Selected then helps applicants with their resumé, and offers coaching.

The teachers then just sit back and wait for schools to reach out with offers. In their first week, they receive around 5 matches, and average around 15 in total. It’s too soon to say if Selected’s hypothesis around improving teacher retention is paying off. That won’t be known for several years still.

Schools are charged a fixed fee when a teacher is hired, which is currently the only source of revenue for the company.

Propel Capital led the seed round, which included participation from Kapor Capital and other investors.

With the seed funding, Selected will continue to develop its business in the NE U.S., and, later, the rest of the country.

New York-based Selected is currently a team of four full-time and four part-time, including co-founder and CTO Luis Pazmiño.

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Artificial Intelligence

Nvidia’s researchers teach a robot to perform simple tasks by observing a human

Industrial robots are typically all about repeating a well-defined task over and over again. Usually, that means performing those tasks a safe distance away from the fragile humans that programmed them. More and more, however, researchers are now thinking about how robots and humans can work in close proximity to humans and even learn from them. In part, that’s what Nvidia’s new robotics lab in Seattle focuses on and the company’s research team today presented some of its most recent work around teaching robots by observing humans at the International Conference on Robotics and Automation (ICRA), in Brisbane, Australia.

Nvidia’s director of robotics research Dieter Fox.

As Dieter Fox, the senior director of robotics research at Nvidia (and a professor at the University of Washington), told me, the team wants to enable this next generation of robots that can safely work in close proximity to humans. But to do that, those robots need to be able to detect people, tracker their activities and learn how they can help people. That may be in small-scale industrial setting or in somebody’s home.

While it’s possible to train an algorithm to successfully play a video game by rote repetition and teaching it to learn from its mistakes, Fox argues that the decision space for training robots that way is far too large to do this efficiently. Instead, a team of Nvidia researchers led by Stan Birchfield and Jonathan Tremblay, developed a system that allows them to teach a robot to perform new tasks by simply observing a human.

The tasks in this example are pretty straightforward and involve nothing more than stacking a few colored cubes. But it’s also an important step in this overall journey to enable us to quickly teach a robot new tasks.

The researchers first trained a sequence of neural networks to detect objects, infer the relationship between them and then generate a program to repeat the steps it witnessed the human perform. The researchers say this new system allowed them to train their robot to perform this stacking task with a single demonstration in the real world.

One nifty aspect of this system is that it generates a human-readable description of the steps it’s performing. That way, it’s easier for the researchers to figure out what happened when things go wrong.

Nvidia’s Stan Birchfield tells me that the team aimed to make training the robot easy for a non-expert — and few things are easier to do than to demonstrate a basic task like stacking blocks. In the example the team presented in Brisbane, a camera watches the scene and the human simply walks up, picks up the blocks and stacks them. Then the robot repeats the task. Sounds easy enough, but it’s a massively difficult task for a robot.

To train the core models, the team mostly used synthetic data from a simulated environment. As both Birchfield and Fox stressed, it’s these simulations that allow for quickly training robots. Training in the real world would take far longer, after all, and can also be more far more dangerous. And for most of these tasks, there is no labeled training data available to begin with.

“We think using simulation is a powerful paradigm going forward to train robots do things that weren’t possible before,” Birchfield noted. Fox echoed this and noted that this need for simulations is one of the reasons why Nvidia thinks that its hardware and software is ideally suited for this kind of research. There is a very strong visual aspect to this training process, after all, and Nvidia’s background in graphics hardware surely helps.

Fox admitted that there’s still a lot of research left to do be done here (most of the simulations aren’t photorealistic yet, after all), but that the core foundations for this are now in place.

Going forward, the team plans to expand the range of tasks that the robots can learn and the vocabulary necessary to describe those tasks.

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58-year-old NRI masturbates sitting beside woman on board flight, held at Delhi airport

The security control room at the IGI Airport was informed in the early hours today that there was an “unruly passenger” on board a Turkish Airlines flight approaching Delhi.

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After tens of thousands of pre-orders, 3D audio headphones startup Ossic disappears

After taking tens of thousands of crowd-funding pre-orders for a high-end pair of “3D sound” headphones, audio startup Ossic announced this weekend that it is shutting down the company and backers will not be receiving refunds.

The company raised $2.7 million on Kickstarter and $3.2 million on Indiegogo for their Ossic X headphones which they pitched as a pair of high-end head-tracking headphones that would be perfect for listening to 3D audio, especially in a VR environment. While the company also raised a “substantial seed investment,” in a letter on the Ossic website, the company blamed the slow adoption of virtual reality alongside their crowdfunding campaign stretch goals which bogged down their R&D team.

“This was obviously not our desired outcome. The team worked exceptionally hard and created a production-ready product that is a technological and performance breakthrough. To fail at the 5 yard-line is a tragedy. We are extremely sorry that we cannot deliver your product and want you to know that the team has done everything possible including investing our own savings and working without salary to exhaust all possibilities.”

We have reached out to the company for additional details.

Through January 2017, the San Diego company had received more than 22,000 pre-orders for their Ossic X headphones. This past January, Ossic announced that they had shipped out the first units to the 80 backers in their $999 developer tier headphones. In that same update, the company said they would enter “mass production” by late spring 2018.

In the end, after tens of thousands of pre-orders, Ossic only built 250 pairs of headphones and only shipped a few dozen to Kickstarter backers.

Crowdfunding campaign failures for hardware products are rarely shocking, but often the collapse comes from the company not being able to acquire additional funding from outside investors. Here, Ossic appears to have been misguided from the start and even with nearly $6 million in crowdfunding and seed funding, which they said nearly matched that number, they were left unable to begin large-scale manufacturing. The company said in their letter, that it would likely take more than $2 million in additional funding to deliver the existing backlog of pre-orders.

Backers are understandably quite upset about not receiving their headphones. A group of over 1,200 Facebook users have joined a recently-created page threatening a class action lawsuit against the team.

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