Archive 24.10.2018

I have two kids in college and one of my biggest concerns is their knowledge that what they have labored hard to acquire will become obsolete by the time of graduation. Our age is driven by the hypersonic accelerations of technology and data forcing innovative educators to create new pedagogical systems that empower students with the skills today to lead tomorrow.
The new CyberNYC initiative announced last week by the City of New York is just one example of this growing partnership between online platforms and traditional academia in the hope of fostering a new generation of wage earners.

The goal of CyberNYC is to train close to 5% of the city’s working population to become “cyber specialists.” In order to accomplish this lofty objective, the NYCEDC forged an educational partnership with CUNY, NYU, Columbia, Cornell Tech, and iQ4. One of the most compelling aspects of the partnership is the advanced degree program offered by CUNY and Facebook, enabling students to achieve a masters in computer science in just a year through the online educational hosting site EdX, which also enables users to stack credentials from other universities.

As Anant Agarwal, CEO of edX, explains, “The workplace is changing more rapidly today than ever before and employers are in need of highly-developed talent. Meanwhile, college graduates want to advance professionally, but are realizing they do not have the career-relevant skills that the modern workplace demands. EdX recognizes this mismatch between business and education for learners, employees and employers. The MicroMasters initiative provides the next level of innovation in learning to address this skills gap by creating a bridge between higher education and industry to create a skillful, successful 21st-century workforce.”

Realizing that not everyone is cut out for higher education, the Big Apple is also working to create boot camps to upskill existing tech operators in a matter of weeks with industry-specific cyber competencies. Fullstack Academy is leading the effort to create a catalogue of intensive boot camps throughout the boroughs. LaGuardia Community College (LAGCC) is also providing free preparatory courses for adults with minimum computing proficiency in order to qualify for Fullstack’s programs. Most importantly, LAGCC will act as a liaison to CyberNYC’s corporate partners to match graduates with open positions.

In 2012, Sebastian Thrun famously declared that, “access to higher education should be a a basic human right.” Thrun, who left his position of running Google X to “democratize education” worldwide by launching a free online open-university platform, Udacity, is now transforming the learning paradigm. The German inventor is no stranger to innovation, in 2011 he unveiled at the Ted Conference one of the first self-driving cars, inspired by losing his best friend to a car accident. Similar to CyberNYC’s fast-track masters in computer science program, Udacity is teamed up with AT&T and Georgia Tech to offer similar degrees for less than $7,000 (compared to $26,860 for an on-campus program).

In the words of AT&T’s Chief Executive Randall Stephenson, “We believe that high-quality and 100 percent online degrees can be on par with degrees received in traditional on-campus settings, and that this program could be a blueprint for helping the United States address the shortage of people with STEM degrees, as well as exponentially expand access to computer science education for students around the world.”

In 2003, Reid Hoffman launched LinkedIn, the first business social network. Today, there are more than a half billion profiles (resumes) posted on the site. Last March, Hoffman sat down with University of California President (and former director of Homeland Security) Janet Napolitano to discuss the future of education. The leading advocate for entrepreneurship explained that he believes everyone should be “in permanent beta,” whereby one is constantly consuming information. Hoffman states that this is the only way an individual and a society will be able to compete in a world driven by data and artificial intelligence. Universities, like the UC system, Hoffman suggests should move towards a cross-disciplinary system. As Hoffman espouses, “What we’re actually in fact primarily teaching is that learning how to learn as you get to new areas, not areas where it’s necessarily the apprenticeship model, which is we teach you this thing and you know how to do this one thing. You know how to do this thing really well, but actually, in fact, you’re going to be crossing domains. That’s how I would somewhat shift the focus overall in terms of thinking about it.”

In his book, “Thank You For Being Late: An Optimistic’s Guide To Thriving In The Age Of Accelerations,” Thomas Friedman quotes Nest Labs’ founder, Tony Fadell, as asserting that the future economy rests on businesses’ ability of turning “AI into IA,” or “Intelligent Assistants.” Friedman specifically singled out LinkedIn as one of these IAs that are creating human networks to amplify people in finding the best opportunities and highest demanded skills. In order to utilize his IA, Hoffman advised Napolitano’s audience to be versatile, “As opposed to thinking about this as a career ladder, a career escalator, to think of it as more of a career jungle gym, that you’re actually going to be changing around in terms industries. The exact shape of certain different job professions will change, and that you need to be adaptive with that.” He continued, “I do think that the notion that is still too often preached, which is you go to college, you discover your calling, and that’s your job for the next 50 years, that’s gone. ” The key to harnessing this trend says Hoffman is “to constantly be learning and to be learning new things. Some of them by taking ongoing classes but some of them also by doing, and talking to people and finding out what the relevant things are, and then tracking what’s going on.”

All these efforts are not happening fast enough in the United States to fill the current gap between the 6.9 million job openings and the number of unemployed. While the unemployment rate is at a forty year low with 6.2 million workers out of work, there still is a significant disparity with more open job listings. The primary reason stated by business leaders across the nation is that the current class of applicants lack the versatility of skills required for the modern workplace, resulting in the push towards full automation. Eric Schmidt, former Executive Chairman of Google and Alphabet, claims that “Today we all live and work in a new era, the Internet Century, where technology is roiling the business landscape and the pace of change is accelerating.” This Internet Century (and by extension cloud computing and unmanned systems) requires a new type of worker, which he affectionally calls the “smart creative” that is first and foremost an “adaptive learner.”

The deficiency of graduating “smart creatives” could be the reason why America, which is almost at full employment, is still producing historically low output resulting is stagnant wages. Mark Zandi, Moody’s Chief Economist, explains, “Wage growth feels low by historical standards and that’s largely because productivity growth is low relative to historical standards. Productivity growth between World War II and up through the Great Recession was, on average, 2 percent per annum. Since the recession 10 years ago, it’s been 1 percent.” The virtuous efforts of CyberNYC, and other grassroots initiatives, are only the first of many towards the complete restructuring of America’s educational framework to nurture a culture of smart creatives that are in permanent beta.

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In this episode of Robots in Depth, Per Sjöborg speaks with Nicola Tomatis about his long road into robotics and how BlueBotics handles indoor navigation and integrates it in automated guided vehicles (AGV).

Like many, Nicola started out tinkering when he was young, and then got interested in computer science as he wanted to understand it better.

Nicola gives us an overview of indoor navigation and its challenges. He shares a number of interesting projects, including professional cleaning and intralogistics in hospitals. We also find out what someone who wants to use indoor navigation and AGVs should think about.

This interview was recorded in 2016.

From about 245 to 66 million years ago, dinosaurs roamed the Earth. Although well-preserved skeletons give us a good idea of what they looked like, the way their limbs worked remains a bigger mystery. But computer simulations may soon provide a realistic glimpse into how some species moved and inform work in fields such as robotics, prosthetics and architecture.

Developing automated systems that track occupants and self-adapt to their preferences is a major next step for the future of smart homes. When you walk into a room, for instance, a system could set to your preferred temperature. Or when you sit on the couch, a system could instantly flick the television to your favorite channel.

But enabling a home system to recognize occupants as they move around the house is a more complex problem. Recently, systems have been built that localize humans by measuring the reflections of wireless signals off their bodies. But these systems can’t identify the individuals. Other systems can identify people, but only if they’re always carrying their mobile devices. Both systems also rely on tracking signals that could be weak or get blocked by various structures.

MIT researchers have built a system that takes a step toward fully automated smart home by identifying occupants, even when they’re not carrying mobile devices. The system, called Duet, uses reflected wireless signals to localize individuals. But it also incorporates algorithms that ping nearby mobile devices to predict the individuals’ identities, based on who last used the device and their predicted movement trajectory. It also uses logic to figure out who’s who, even in signal-denied areas.

“Smart homes are still based on explicit input from apps or telling Alexa to do something. Ideally, we want homes to be more reactive to what we do, to adapt to us,” says Deepak Vasisht, a PhD student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author on a paper describing the system that was presented at last week’s Ubicomp conference. “If you enable location awareness and identification awareness for smart homes, you could do this automatically. Your home knows it’s you walking, and where you’re walking, and it can update itself.”

Experiments done in a two-bedroom apartment with four people and an office with nine people, over two weeks, showed the system can identify individuals with 96 percent and 94 percent accuracy, respectively, including when people weren’t carrying their smartphones or were in blocked areas.

But the system isn’t just novelty. Duet could potentially be used to recognize intruders or ensure visitors don’t enter private areas of your home. Moreover, Vasisht says, the system could capture behavioral-analytics insights for health care applications. Someone suffering from depression, for instance, may move around more or less, depending on how they’re feeling on any given day. Such information, collected over time, could be valuable for monitoring and treatment.

“In behavioral studies, you care about how people are moving over time and how people are behaving,” Vasisht says. “All those questions can be answered by getting information on people’s locations and how they’re moving.”

The researchers envision that their system would be used with explicit consent from anyone who would be identified and tracked with Duet. If needed, they could also develop an app for users to grant or revoke Duet’s access to their location information at any time, Vasisht adds.

Co-authors on the paper are: Dina Katabi, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science; former CSAIL researcher Anubhav Jain ’16; and CSAIL PhD students Chen-Yu Hsu and Zachary Kabelac.

Tracking and identification

Duet is a wireless sensor installed on a wall that’s about a foot and a half squared. It incorporates a floor map with annotated areas, such as the bedroom, kitchen, bed, and living room couch. It also collects identification tags from the occupants’ phones.

The system builds upon a device-based localization system built by Vasisht, Katabi, and other researchers that tracks individuals within tens of centimeters, based on wireless signal reflections from their devices. It does so by using a central node to calculate the time it takes the signals to hit a person’s device and travel back. In experiments, the system was able to pinpoint where people were in a two-bedroom apartment and in a café.

The system, however, relied on people carrying mobile devices. “But in building [Duet] we realized, at home you don’t always carry your phone,” Vasisht says. “Most people leave devices on desks or tables, and walk around the house.”

The researchers combined their device-based localization with a device-free tracking system, called WiTrack, developed by Katabi and other CSAIL researchers, that localizes people by measuring the reflections of wireless signals off their bodies.

Duet locates a smartphone and correlates its movement with individual movement captured by the device-free localization. If both are moving in tightly correlated trajectories, the system pairs the device with the individual and, therefore, knows the identity of the individual.

To ensure Duet knows someone’s identity when they’re away from their device, the researchers designed the system to capture the power profile of the signal received from the phone when it’s used. That profile changes, depending on the orientation of the signal, and that change be mapped to an individual’s trajectory to identify them. For example, when a phone is used and then put down, the system will capture the initial power profile. Then it will estimate how the power profile would look if it were still being carried along a path by a nearby moving individual. The closer the changing power profile correlates to the moving individual’s path, the more likely it is that individual owns the phone.

Logical thinking

One final issue is that structures such as bathroom tiles, television screens, mirrors, and various metal equipment can block signals.

To compensate for that, the researchers incorporated probabilistic algorithms to apply logical reasoning to localization. To do so, they designed the system to recognize entrance and exit boundaries of specific spaces in the home, such as doors to each room, the bedside, and the side of a couch. At any moment, the system will recognize the most likely identity for each individual in each boundary. It then infers who is who by process of elimination.

Suppose an apartment has two occupants: Alisha and Betsy. Duet sees Alisha and Betsy walk into the living room, by pairing their smartphone motion with their movement trajectories. Both then leave their phones on a nearby coffee table to charge — Betsy goes into the bedroom to nap; Alisha stays on the couch to watch television. Duet infers that Betsy has entered the bed boundary and didn’t exit, so must be on the bed. After a while, Alisha and Betsy move into, say, the kitchen — and the signal drops. Duet reasons that two people are in the kitchen, but it doesn’t know their identities. When Betsy returns to the living room and picks up her phone, however, the system automatically re-tags the individual as Betsy. By process of elimination, the other person still in the kitchen is Alisha.

“There are blind spots in homes where systems won’t work. But, because you have logical framework, you can make these inferences,” Vasisht says.

“Duet takes a smart approach of combining the location of different devices and associating it to humans, and leverages device-free localization techniques for localizing humans,” says Ranveer Chandra, a principal researcher at Microsoft, who was not involved in the work. “Accurately determining the location of all residents in a home has the potential to significantly enhance the in-home experience of users. … The home assistant can personalize the responses based on who all are around it; the temperature can be automatically controlled based on personal preferences, thereby resulting in energy savings. Future robots in the home could be more intelligent if they knew who was where in the house. The potential is endless.”

Next, the researchers aim for long-term deployments of Duet in more spaces and to provide high-level analytic services for applications such as health monitoring and responsive smart homes.

With the recent demise of Rethink Robotics, there were dozens of testimonials that the company revolutionized industrial robotics and kickstarted the collaborative robotics trend. There is no doubt that Baxter and Sawyer were truly innovative and more sophisticated than the average industrial robot. They were also safer than most other cobots, though at the expense of precision. So was Rethink Robotics the pioneer of collaborative robots?

Rethink Robotics was certainly one of the first companies to enter the collaborative robots market. However, I don’t think that the company had a major impact on that industry. Surely, Rethink’s marketing team did an exceptional job promoting the concept of cobots, and thanks to Baxter (which sold for only US$25,000, nearly four times cheaper than Kawada’s Nextage, featured in the above photo), hundreds of research labs were able to safely train students in the field of robotics and AI. However, Rethink’s technology itself did not influence any other robot manufacturer.

Baxter was released in September 2011, but it was only in May 2013 that our lab managed to acquire one of the very first Baxters outside USA. (We now have two of them: one is the research version, the other is the industrial model.) By that time, Universal Robots had already sold more than a thousand cobots. It was only in March 2015 that Rethink Robotics introduced the more practical Sawyer. Two months later, Denmark-based Universal Robots was acquired by Teradyne, neighbor of Rethink Robotics, for $350M. By that time, Universal Robots had sold more than 4,000 cobots, whereas Rethink had shipped only several hundred Baxters, mostly to academia.

Today, there are dozens of cobot models that closely “resemble” the 25,000 (!) robot arms already sold by Universal Robots. And even the latest version of UR arms, while more sophisticated than the EasyBot (the very first version of the UR5), shares none of the advanced features of Rethink’s cobots (many of which are patented, of course). The e-series UR cobots do not have embedded camera, nor seven degrees of freedom, nor torque sensors in the joints, not series elastic actuators, nor self-collision avoidance algorithms. Universal Robots started with a simple, easy-to-use programming interface, a relatively standard, easy-to-manufacture robot arm, and some basic safety functionality. The company then gradually improved its product, and most importantly its distribution network. Rethink Robotics tried to release immediately a technology that was never tested before.

Rethink Robotics developed cobots that were way too different from other industrial robots, both from technological and user points of view. It’s not because you target users that are possibly not engineers with robotics background that you need to make a perfectly safe, friendly-faced robot that can only be programmed through demonstration. I am truly sad about the company’s engineers; they did create two revolutionary robots that marked the robotics field and will continue to be mentioned in every future textbook on robotics. However, I doubt that there will be much interest in the company’s patent portfolio and other intellectual property, which are currently for sale.

By Xue Bin (Jason) Peng and Angjoo Kanazawa

Whether it’s everyday tasks like washing our hands or stunning feats of acrobatic prowess, humans are able to learn an incredible array of skills by watching other humans. With the proliferation of publicly available video data from sources like YouTube, it is now easier than ever to find video clips of whatever skills we are interested in.

Simulated characters imitating skills from YouTube videos.

A staggering 300 hours of videos are uploaded to YouTube every minute. Unfortunately, it is still very challenging for our machines to learn skills from this vast volume of visual data. Most imitation learning approaches require concise representations, such as those recorded from motion capture (mocap). But getting mocap data can be quite a hassle, often requiring heavy instrumentation. Mocap systems also tend to be restricted to indoor environments with minimal occlusion, which can limit the types of skills that can be recorded. So wouldn’t it be nice if our agents can also learn skills by watching video clips?

In this work, we present a framework for learning skills from videos (SFV). By combining state-of-the-art techniques in computer vision and reinforcement learning, our system enables simulated characters to learn a diverse repertoire of skills from video clips. Given a single monocular video of an actor performing some skill, such as a cartwheel or a backflip, our characters are able to learn policies that reproduce that skill in a physics simulation, without requiring any manual pose annotations.

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I love to talk about the coming robocar world. Over the next few decades, more and more trips will be made in robocars, and more and more people will reduce or give up car ownership to live the robotaxi life. This won’t be instantaneous, and it will happen in some places decades before it happens in others, but I think it’s coming.

But what of the driver of the regular car? What lies ahead for those who love driving and want to own a traditional car? I often see people declare that nobody will own cars in the future, and that human driving will even be banned. Is that realistic?

Nobody restricts human driving for quite some time

The transition to robocars must be gradual, one car at a time, at least in most of the world. That means lots of human driven cars on all the roads for decades to come.

Some people predict that human driving will quickly be banned. This won’t happen in most places simply because there will still be lots of places robocars don’t go because it’s not commercially viable to certify them there. In addition, there will be strong political opposition. At a rough guess, around 1/3rd of people never have a car accident in their lives. What is the justification in taking away their licences?

When I give talks on robocars, I usually get some people telling me they can’t imagine why anybody would drive or own a car in the future, and others declaring that only a minority will give up the fun, freedom and control of manual driving. The real answer will be a mix. Though to those who tell me that Americans love cars too much to ever give them up, I ask how it is that car-loving Californians can move to Manhattan and give up car ownership in 15 minutes.

Restricted zones

We might see the rise of robocar-only lanes in certain places. There might be a special highway lane, where faster driving is allowed, and platooning takes place.

More dramatic would be the designation of certain downtown areas as robocar only at certain times or all the time. It’s not unusual for there to be driving restrictions in downtowns, particularly “old city” downtowns with small streets as found in Europe. Sometimes downtown streets are converted to pedestrian malls as well. It’s possible to imagine the robocars being deemed well behaved enough to go into these restricted areas.

We might also see robocars allowed to access, under certain rules, the private right-of-way used by transit lines, particularly bus rapid transit paths. It’s also possible rail lines and tunnels could get partially paved to allow the robocars to use them when the transit vehicle is not. They can be trusted not to interfere, and they can also drive reliably on thin strips of pavement — like rails for tires — if necessary.

This is not taking anything away from regular cars, but rather it’s giving the robocars a privilege the traditional cars never got.

More threatening to the human driver might be time restrictions or congestion restrictions. Once robocars provably cause less congestion, we might see congestion taxes on human drivers, or limitations on human driving during congested times, just as we sometimes see for trucks.

New infrastructure only for robocars

Many years down the road, cities might realize that when building new infrastructure, there are advantages in making it only for robocars, or for certain classes of robocars, such as lightweight electric single passenger cars. You can build very small tunnels for them, or much cheaper bridges and elevated roadways. They can drive reliably on a thin lane, saving a lot of money. Your old car won’t go there, but then again, it can’t go there now.

It’s also possible we might see housing neighbourhoods that take advantage of some things robocars can do, like serve the homes only with a single lane back alley, which comes with occasional wide spots for cars to do perfectly timed passing of one another on the short lanes.

Like many people, I think that robocars will be electric. As we get more electric cars, they might get privileges on the road you don’t get if you drive a manual gas guzzler. This even includes things like driving into buildings.

The fun roads will still be fun roads

The vast majority of the changes due to robocars will be on urban streets and highways. Commuting roads. Even those who love to drive don’t tend to love the urban commute. The country roads, the scenic coastal and mountain routes — these will stay pretty much the same for a long time to come. In fact, you might like having a robocar drive you out of the city to places where it’s fun to drive, and then take the wheel (or switch to a sportscar) to enjoy those great drives.

New traffic control and virtual infrastructure

Robocars must mix with regular cars, but they are able to do a better job at helping reduce traffic congestion by obeying on-the-fly rules about road use. They make use of “virtual infrastructure” — things like maps that show everything about the roads and their rules.

Drivers of regular cars can participate in this as well, with just a smartphone. If the day comes when cities want to meter or charge for roads using computer networks, the robocars will be ready to do it, but so will the ordinary drivers as long as they have that phone.

Parking will dwindle, but get cheaper for robocars

Robotaxis don’t need to park that much — they would rather be out working on another ride. Private robocars will drop their masters off at the door and then go find the cheapest parking nearby. That parking doesn’t have to be right nearby, they will shop around in a way that human drivers can’t. And park themselves densely in valet style. So they won’t pay that much for parking. You’ll probably have to pay more since you need to park your car right where you’re going while they don’t. You also take up more space.

Many parking spaces on the street might be restricted to robocars at certain times. That’s because robocars don’t park, they “stand,” able to move away at any time. A robocar can stand in front of a driveway or even a fire hydrant. And we might see important streets that only let robocars park on them as rush hour approaches, because they can be trusted to reliably clear the parking lanes and make them open for traffic. Robocars can also double park where there is room, because they can get out of the way if the “blocked” car wants to get out.

Trucks

The existence of cheap delivery robots might change how much people want trucks or vehicles with lots of cargo space. If you know that, when you want to move a big thing, you can easily call up a delivery robot, you might reduce your desire for cargo space. On the other hand, if you want a van with everything in it all the time, you might still go for that.

Your insurance might go down a bit

The price of insurance is based on how frequently people like you have accidents. Unless you think you will have more accidents, your insurance won’t go up. In fact, as the roads get safer and more orderly, you might have fewer accidents. And late model manually driven cars will still be loaded up with new accident prevention features, some of them a result of robocar research. That means your insurance will get a bit cheaper. Of course, the robocars will drive more safely than you, and pay even less for the cost of accidents. If not, they’re doing it wrong.

Some kids never get a licence

Kids are alive today who won’t get a licence due to robocars. That’s easy because kids are alive today who are not getting licences due to transit and Uber. This will grow. As it grows, it will become more normal, which means more effort to accommodate them, and a bit more pressure against human driving in some areas and times. Parents will cut back on the idea of giving a teenager a car, preferring to offer them a “card” which provides robotaxi service. A time will come when preferring to manual drive will be looked on as a touch odd, and for the older generation and the enthusiast.

Still plenty of places without robocars

As I outline in my timeline for deployment, robocars will roll out on very different schedules based on where you are. There will be cities that have heavy robotaxi penetration in the 2020s, while other cities have next to none. There will be countries which have no robocars even in the 2030s.

That’s good news for people who want to give up car ownership and sell their existing car. Even if they might have a hard time selling a used car in a city where car ownership is decreasing, there will still be plenty of places, both in the USA and around the world, where the car can be sold.

In addition, robotaxi service will not be available in rural areas for a very long time. Out there you will find only traditional cars and privately owned robocars. There will be many places to enjoy driving and sell cars.

Conversely, there is talk that countries like China, which have been known to do bold projects and are building brand-new cities, might declare a city to be all-robocars before too long. While that’s possible, it is not going to affect how you drive outside that city.

Privately owned robocars will have a steering wheel available, though if it’s for rare use it may be more like a video game wheel. You’ll need it to drive places robocars are not rated to go, like off-road or the dirt road to grandpa’s house — or just the many places the robocar doesn’t have maps and safety certification for. This probably won’t be “fun” sports driving, but it could be. Taxis won’t have these wheels (except for use by staff if the vehicle fails) but might drop you off next to a vehicle that has a wheel if that’s needed to complete your trip.

Society may not tolerate your mistakes

The biggest threat to the lover of driving is that while you won’t get any more dangerous, you will be more dangerous by comparison. You might not get away with things you could get away with before when everybody was dangerous.

It’s possible that you might lose your licence after one or two accidents. Perhaps even after just one DUI or a few serious tickets. Today, society doesn’t want to take away people’s licences because that destroys their mobility. In the future, it won’t — you will still be able to get around. So society may not tolerate you making big mistakes.

This also might happen more to people as they get older. Again, today we don’t want to take away a senior’s licence even when they are half-blind, because that ends their mobile life. Not any more.

The more distant future

As time passes, a new generation will grow up without learning how to drive. A couple of generations from now, manual driving may be mostly a sport or affectation. Still allowed, but uncommon. After all, people still ride horses a century after they stopped being used for transportation.

In this far away time, driving may be seen mostly as a sport, not a means of transportation. Racetracks and special roads will still exist, but for fun, not travel.