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#436065 From Mainframes to PCs: What Robot ...

Posted on October 8, 2019 by Android

This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE.

Autonomous robots are coming around slowly. We already got autonomous vacuum cleaners, autonomous lawn mowers, toys that bleep and blink, and (maybe) soon autonomous cars. Yet, generation after generation, we keep waiting for the robots that we all know from movies and TV shows. Instead, businesses seem to get farther and farther away from the robots that are able to do a large variety of tasks using general-purpose, human anatomy-inspired hardware.

Although these are the droids we have been looking for, anything that came close, such as Willow Garage’s PR2 or Rethink Robotics’ Baxter has bitten the dust. With building a robotic company being particularly hard, compounding business risk with technological risk, the trend goes from selling robots to selling actual services like mowing your lawn, provide taxi rides, fulfilling retail orders, or picking strawberries by the pound. Unfortunately for fans of R2-D2 and C-3PO, these kind of business models emphasize specialized, room- or fridge-sized hardware that is optimized for one very specific task, but does not contribute to a general-purpose robotic platform.

We have actually seen something very similar in the personal computer (PC) industry. In the 1950s, even though computers could be as big as an entire room and were only available to a selected few, the public already had a good idea of what computers would look like. A long list of fictional computers started to populate mainstream entertainment during that time. In a 1962 New York Times article titled “Pocket Computer to Replace Shopping List,” visionary scientist John Mauchly stated that “there is no reason to suppose the average boy or girl cannot be master of a personal computer.”

In 1968, Douglas Engelbart gave us the “mother of all demos,” browsing hypertext on a graphical screen and a mouse, and other ideas that have become standard only decades later. Now that we have finally seen all of this, it might be helpful to examine what actually enabled the computing revolution to learn where robotics is really at and what we need to do next.

The parallels between computers and robots

In the 1970s, mainframes were about to be replaced by the emerging class of mini-computers, fridge-sized devices that cost less than US $25,000 ($165,000 in 2019 dollars). These computers did not use punch-cards, but could be programmed in Fortran and BASIC, dramatically expanding the ease with which potential applications could be created. Yet it was still unclear whether mini-computers could ever replace big mainframes in applications that require fast and efficient processing of large amounts of data, let alone enter every living room. This is very similar to the robotics industry right now, where large-scale factory robots (mainframes) that have existed since the 1960s are seeing competition from a growing industry of collaborative robots that can safely work next to humans and can easily be installed and programmed (minicomputers). As in the ’70s, applications for these devices that reach system prices comparable to that of a luxury car are quite limited, and it is hard to see how they could ever become a consumer product.

Yet, as in the computer industry, successful architectures are quickly being cloned, driving prices down, and entirely new approaches on how to construct or program robotic arms are sprouting left and right. Arm makers are joined by manufacturers of autonomous carts, robotic grippers, and sensors. These components can be combined, paving the way for standard general purpose platforms that follow the model of the IBM PC, which built a capable, open architecture relying as much on commodity parts as possible.

General purpose robotic systems have not been successful for similar reasons that general purpose, also known as “personal,” computers took decades to emerge. Mainframes were custom-built for each application, while typewriters got smarter and smarter, not really leaving room for general purpose computers in between. Indeed, given the cost of hardware and the relatively little abilities of today’s autonomous robots, it is almost always smarter to build a special purpose machine than trying to make a collaborative mobile manipulator smart.

A current example is e-commerce grocery fulfillment. The current trend is to reserve underutilized parts of a brick-and-mortar store for a micro-fulfillment center that stores goods in little crates with an automated retrieval system and a (human) picker. A number of startups like Alert Innovation, Fabric, Ocado Technology, TakeOff Technologies, and Tompkins Robotics, to just name a few, have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves. Such a robotic store clerk would come much closer to our vision of a general purpose robot, but would require many copies of itself that crowd the aisles to churn out hundreds of orders per hour as a microwarehouse could. Although eventually more efficient, the margins in retail are already low and make it unlikely that this industry will produce the technological jump that we need to get friendly C-3POs manning the aisles.

Startups have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves, and would come much closer to our vision of a general purpose robot.

Mainframes were also attacked from the bottom. Fascination with the new digital technology has led to a hobbyist movement to create microcomputers that were sold via mail order or at RadioShack. Initially, a large number of small businesses was selling tens, at most hundreds, of devices, usually as a kit and with wooden enclosures. This trend culminated into the “1977 Trinity” in the form of the Apple II, the Commodore PET, and the Tandy TRS-80, complete computers that were sold for prices around $2500 (TRS) to $5000 (Apple) in today’s dollars. The main application of these computers was their programmability (in BASIC), which would enable consumers to “learn to chart your biorhythms, balance your checking account, or even control your home environment,” according to an original Apple advertisement. Similarly, there exists a myriad of gadgets that explore different aspects of robotics such as mobility, manipulation, and entertainment.

As in the fledgling personal computing industry, the advertised functionality was at best a model of the real deal. A now-famous milestone in entertainment robotics was the original Sony’s Aibo, a robotic dog that was advertised to have many properties that a real dog has such as develop its own personality, play with a toy, and interact with its owner. Released in 1999, and re-launched in 2018, the platform has a solid following among hobbyists and academics who like its programmability, but probably only very few users who accept the device as a pet stand-in.

There also exist countless “build-your-own-robotic-arm” kits. One of the more successful examples is the uArm, which sells for around $800, and is advertised to perform pick and place, assembly, 3D printing, laser engraving, and many other things that sound like high value applications. Using compelling videos of the robot actually doing these things in a constrained environment has led to two successful crowd-funding campaigns, and have established the robot as a successful educational tool.

Finally, there exist platforms that allow hobbyist programmers to explore mobility to construct robots that patrol your house, deliver items, or provide their users with telepresence abilities. An example of that is the Misty II. Much like with the original Apple II, there remains a disconnect between the price of the hardware and the fidelity of the applications that were available.

For computers, this disconnect began to disappear with the invention of the first electronic spreadsheet software VisiCalc that spun out of Harvard in 1979 and prompted many people to buy an entire microcomputer just to run the program. VisiCalc was soon joined by WordStar, a word processing application, that sold for close to $2000 in today’s dollars. WordStar, too, would entice many people to buy the entire hardware just to use the software. The two programs are early examples of what became known as “killer application.”

With factory automation being mature, and robots with the price tag of a minicomputer being capable of driving around and autonomously carrying out many manipulation tasks, the robotics industry is somewhere where the PC industry was between 1973—the release of the Xerox Alto, the first computer with a graphical user interface, mouse, and special software—and 1979—when microcomputers in the under $5000 category began to take off.

Killer apps for robots
So what would it take for robotics to continue to advance like computers did? The market itself already has done a good job distilling what the possible killer apps are. VCs and customers alike push companies who have set out with lofty goals to reduce their offering to a simple value proposition. As a result, companies that started at opposite ends often converge to mirror images of each other that offer very similar autonomous carts, (bin) picking, palletizing, depalletizing, or sorting solutions. Each of these companies usually serves a single application to a single vertical—for example bin-picking clothes, transporting warehouse goods, or picking strawberries by the pound. They are trying to prove that their specific technology works without spreading themselves too thin.

Very few of these companies have really taken off. One example is Kiva Systems, which turned into the logistic robotics division of Amazon. Kiva and others are structured around sound value propositions that are grounded in well-known user needs. As these solutions are very specialized, however, it is unlikely that they result into any economies of scale of the same magnitude that early computer users who bought both a spreadsheet and a word processor application for their expensive minicomputer could enjoy. What would make these robotic solutions more interesting is when functionality becomes stackable. Instead of just being able to do bin picking, palletizing, and transportation with the same hardware, these three skills could be combined to model entire processes.

A skill that is yet little addressed by startups and is historically owned by the mainframe equivalent of robotics is assembly of simple mechatronic devices. The ability to assemble mechatronic parts is equivalent to other tasks such as changing a light bulb, changing the batteries in a remote control, or tending machines like a lever-based espresso machine. These tasks would involve the autonomous execution of complete workflows possible using a single machine, eventually leading to an explosion of industrial productivity across all sectors. For example, picking up an item from a bin, arranging it on the robot, moving it elsewhere, and placing it into a shelf or a machine is a process that equally applies to a manufacturing environment, a retail store, or someone’s kitchen.

Image: Robotic Materials Inc.

Autonomous, vision and force-based assembly of the
Siemens robot learning challenge.

Even though many of the above applications are becoming possible, it is still very hard to get a platform off the ground without added components that provide “killer app” value of their own. Interesting examples are Rethink Robotics or the Robot Operating System (ROS). Rethink Robotics’ Baxter and Sawyer robots pioneered a great user experience (like the 1973 Xerox Alto, really the first PC), but its applications were difficult to extend beyond simple pick-and-place and palletizing and depalletizing items.

ROS pioneered interprocess communication software that was adapted to robotic needs (multiple computers, different programming languages) and the idea of software modularity in robotics, but—in the absence of a common hardware platform—hasn’t yet delivered a single application, e.g. for navigation, path planning, or grasping, that performs beyond research-grade demonstration level and won’t get discarded once developers turn to production systems. At the same time, an increasing number of robotic devices, such as robot arms or 3D perception systems that offer intelligent functionality, provide other ways to wire them together that do not require an intermediary computer, while keeping close control over the real-time aspects of their hardware.

Image: Robotic Materials Inc.

Robotic Materials GPR-1 combines a MIR-100 autonomous cart with an UR-5 collaborative robotic arm, an onRobot force/torque sensor and Robotic Materials’ SmartHand to perform out-of-the-box mobile assembly, bin picking, palletizing, and depalletizing tasks.

At my company, Robotic Materials Inc., we have made strides to identify a few applications such as bin picking and assembly, making them configurable with a single click by combining machine learning and optimization with an intuitive user interface. Here, users can define object classes and how to grasp them using a web browser, which then appear as first-class objects in a robot-specific graphical programming language. We have also done this for assembly, allowing users to stack perception-based picking and force-based assembly primitives by simply dragging and dropping appropriate commands together.

While such an approach might answer the question of a killer app for robots priced in the “minicomputer” range, it is unclear how killer app-type value can be generated with robots in the less-than-$5000 category. A possible answer is two-fold: First, with low-cost arms, mobility platforms, and entertainment devices continuously improving, a confluence of technology readiness and user innovation, like with the Apple II and VisiCalc, will eventually happen. For example, there is not much innovation needed to turn Misty into a home security system; the uArm into a low-cost bin-picking system; or an Aibo-like device into a therapeutic system for the elderly or children with autism.

Second, robots and their components have to become dramatically cheaper. Indeed, computers have seen an exponential reduction in price accompanied by an exponential increase in computational power, thanks in great part to Moore’s Law. This development has helped robotics too, allowing us to reach breakthroughs in mobility and manipulation due to the ability to process massive amounts of image and depth data in real-time, and we can expect it to continue to do so.

Is there a Moore’s Law for robots?
One might ask, however, how a similar dynamics might be possible for robots as a whole, including all their motors and gears, and what a “Moore’s Law” would look like for the robotics industry. Here, it helps to remember that the perpetuation of Moore’s Law is not the reason, but the result of the PC revolution. Indeed, the first killer apps for bookkeeping, editing, and gaming were so good that they unleashed tremendous consumer demand, beating the benchmark on what was thought to be physically possible over and over again. (I vividly remember 56 kbps to be the absolute maximum data rate for copper phone lines until DSL appeared.)

That these economies of scale are also applicable to mechatronics is impressively demonstrated by the car industry. A good example is the 2020 Prius Prime, a highly computerized plug-in hybrid, that is available for one third of the cost of my company’s GPR-1 mobile manipulator while being orders of magnitude more complex, sporting an electrical motor, a combustion engine, and a myriad of sensors and computers. It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal. Given that these robots are part of the equation, actively lowering cost of production, this might happen as fast as never before in the history of industrialization.

It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal.

There is one more driver that might make robots exponentially more capable: the cloud. Once a general purpose robot has learned or was programmed with a new skill, it could share it with every other robot. At some point, a grocer who buys a robot could assume that it already knows how to recognize and handle 99 percent of the retail items in the store. Likewise, a manufacturer can assume that the robot can handle and assemble every item available from McMaster-Carr and Misumi. Finally, families could expect a robot to know every kitchen item that Ikea and Pottery Barn is selling. Sounds like a labor intense problem, but probably more manageable than collecting footage for Google’s Street View using cars, tricycles, and snowmobiles, among other vehicles.

Strategies for robot startups
While we are waiting for these two trends—better and better applications and hardware with decreasing cost—to converge, we as a community have to keep exploring what the canonical robotic applications beyond mobility, bin picking, palletizing, depalletizing, and assembly are. We must also continue to solve the fundamental challenges that stand in the way of making these solutions truly general and robust.

For both questions, it might help to look at the strategies that have been critical in the development of the personal computer, which might equally well apply to robotics:

Start with a solution to a problem your customers have. Unfortunately, their problem is almost never that they need your sensor, widget, or piece of code, but something that already costs them money or negatively affects them in some other way. Example: There are many more people who had a problem calculating their taxes (and wanted to buy VisiCalc) than writing their own solution in BASIC.

Build as little of your own hardware as necessary. Your business model should be stronger than the margin you can make on the hardware. Why taking the risk? Example: Why build your own typewriter if you can write the best typewriting application that makes it worth buying a computer just for that?

If your goal is a platform, make sure it comes with a killer application, which alone justifies the platform cost. Example: Microcomputer companies came and went until the “1977 Trinity” intersected with the killer apps spreadsheet and word processors. Corollary: You can also get lucky.

Use an open architecture, which creates an ecosystem where others compete on creating better components and peripherals, while allowing others to integrate your solution into their vertical and stack it with other devices. Example: Both the Apple II and the IBM PC were completely open architectures, enabling many clones, thereby growing the user and developer base.

It’s worthwhile pursuing this. With most business processes already being digitized, general purpose robots will allow us to fill in gaps in mobility and manipulation, increasing productivity at levels only limited by the amount of resources and energy that are available, possibly creating a utopia in which creativity becomes the ultimate currency. Maybe we’ll even get R2-D2.

Nikolaus Correll is an associate professor of computer science at the University of Colorado at Boulder where he works on mobile manipulation and other robotics applications. He’s co-founder and CTO of Robotic Materials Inc., which is supported by the National Science Foundation and the National Institute of Standards and Technology via their Small Business Innovative Research (SBIR) programs. Continue reading →

Posted in Human Robots

#435824 A Q&A with Cruise’s head of AI, ...

Posted on September 26, 2019 by Android

In 2016, Cruise, an autonomous vehicle startup acquired by General Motors, had about 50 employees. At the beginning of 2019, the headcount at its San Francisco headquarters—mostly software engineers, mostly working on projects connected to machine learning and artificial intelligence—hit around 1000. Now that number is up to 1500, and by the end of this year it’s expected to reach about 2000, sprawling into a recently purchased building that had housed Dropbox. And that’s not counting the 200 or so tech workers that Cruise is aiming to install in a Seattle, Wash., satellite development center and a handful of others in Phoenix, Ariz., and Pasadena, Calif.

Cruise’s recent hires aren’t all engineers—it takes more than engineering talent to manage operations. And there are hundreds of so-called safety drivers that are required to sit in the 180 or so autonomous test vehicles whenever they roam the San Francisco streets. But that’s still a lot of AI experts to be hiring in a time of AI engineer shortages.

Hussein Mehanna, head of AI/ML at Cruise, says the company’s hiring efforts are on track, due to the appeal of the challenge of autonomous vehicles in drawing in AI experts from other fields. Mehanna himself joined Cruise in May from Google, where he was director of engineering at Google Cloud AI. Mehanna had been there about a year and a half, a relatively quick career stop after a short stint at Snap following four years working in machine learning at Facebook.

Mehanna has been immersed in AI and machine learning research since his graduate studies in speech recognition and natural language processing at the University of Cambridge. I sat down with Mehanna to talk about his career, the challenges of recruiting AI experts and autonomous vehicle development in general—and some of the challenges specific to San Francisco. We were joined by Michael Thomas, Cruise’s manager of AI/ML recruiting, who had also spent time recruiting AI engineers at Google and then Facebook.

IEEE Spectrum: When you were at Cambridge, did you think AI was going to take off like a rocket?

Mehanna: Did I imagine that AI was going to be as dominant and prevailing and sometimes hyped as it is now? No. I do recall in 2003 that my supervisor and I were wondering if neural networks could help at all in speech recognition. I remember my supervisor saying if anyone could figure out how use a neural net for speech he would give them a grant immediately. So he was on the right path. Now neural networks have dominated vision, speech, and language [processing]. But that boom started in 2012.

“In the early days, Facebook wasn’t that open to PhDs, it actually had a negative sentiment about researchers, and then Facebook shifted”

I didn’t [expect it], but I certainly aimed for it when [I was at] Microsoft, where I deliberately pushed my career towards machine learning instead of big data, which was more popular at the time. And [I aimed for it] when I joined Facebook.

In the early days, Facebook wasn’t that open to PhDs, or researchers. It actually had a negative sentiment about researchers. And then Facebook shifted to becoming one of the key places where PhD students wanted to do internships or join after they graduated. It was a mindset shift, they were [once] at a point in time where they thought what was needed for success wasn’t research, but now it’s different.

There was definitely an element of risk [in taking a machine learning career path], but I was very lucky, things developed very fast.

IEEE Spectrum: Is it getting harder or easier to find AI engineers to hire, given the reported shortages?

Mehanna: There is a mismatch [between job openings and qualified engineers], though it is hard to quantify it with numbers. There is good news as well: I see a lot more students diving deep into machine learning and data in their [undergraduate] computer science studies, so it’s not as bleak as it seems. But there is massive demand in the market.

Here at Cruise, demand for AI talent is just growing and growing. It might be is saturating or slowing down at other kinds of companies, though, [which] are leveraging more traditional applications—ad prediction, recommendations—that have been out there in the market for a while. These are more mature, better understood problems.

I believe autonomous vehicle technologies is the most difficult AI problem out there. The magnitude of the challenge of these problems is 1000 times more than other problems. They aren’t as well understood yet, and they require far deeper technology. And also the quality at which they are expected to operate is off the roof.

The autonomous vehicle problem is the engineering challenge of our generation. There’s a lot of code to write, and if we think we are going to hire armies of people to write it line by line, it’s not going to work. Machine learning can accelerate the process of generating the code, but that doesn’t mean we aren’t going to have engineers; we actually need a lot more engineers.

Sometimes people worry that AI is taking jobs. It is taking some developer jobs, but it is actually generating other developer jobs as well, protecting developers from the mundane and helping them build software faster and faster.

IEEE Spectrum: Are you concerned that the demand for AI in industry is drawing out the people in academia who are needed to educate future engineers, that is, the “eating the seed corn” problem?

Mehanna: There are some negative examples in the industry, but that’s not our style. We are looking for collaborations with professors, we want to cultivate a very deep and respectful relationship with universities.

And there’s another angle to this: Universities require a thriving industry for them to thrive. It is going to be extremely beneficial for academia to have this flourishing industry in AI, because it attracts more students to academia. I think we are doing them a fantastic favor by building these career opportunities. This is not the same as in my early days, [when] people told me “don’t go to AI; go to networking, work in the mobile industry; mobile is flourishing.”

IEEE Spectrum: Where are you looking as you try to find a thousand or so engineers to hire this year?

Thomas: We look for people who want to use machine learning to solve problems. They can be in many different industries—in the financial markets, in social media, in advertising. The autonomous vehicle industry is in its infancy. You can compare it to mobile in the early days: When the iPhone first came out, everyone was looking for developers with mobile experience, but you weren’t going to find them unless you went to straight to Apple, [so you had to hire other kinds of engineers]. This is the same type of thing: it is so new that you aren’t going to find experts in this area, because we are all still learning.

“You don’t have to be an autonomous vehicle expert to flourish in this world. It’s not too late to move…now would be a great time for AI experts working on other problems to shift their attention to autonomous vehicles.”

Mehanna: Because autonomous vehicle technology is the new frontier for AI experts, [the number of] people with both AI and autonomous vehicle experience is quite limited. So we are acquiring AI experts wherever they are, and helping them grow into the autonomous vehicle area. You don’t have to be an autonomous vehicle expert to flourish in this world. It’s not too late to move; even though there is a lot of great tech developed, there’s even more innovation ahead, so now would be a great time for AI experts working on other problems or applications to shift their attention to autonomous vehicles.

It feels like the Internet in 1980. It’s about to happen, but there are endless applications [to be developed over] the next few decades. Even if we can get a car to drive safely, there is the question of how can we tune the ride comfort, and then applying it all to different cities, different vehicles, different driving situations, and who knows to what other applications.

I can see how I can spend a lifetime career trying to solve this problem.

IEEE Spectrum: Why are you doing most of your development in San Francisco?

Mehanna: I think the best talent of the world is in Silicon Valley, and solving the autonomous vehicle problem is going to require the best of the best. It’s not just the engineering talent that is here, but [also] the entrepreneurial spirit. Solving the problem just as a technology is not going to be successful, you need to solve the product and the technology together. And the entrepreneurial spirit is one of the key reasons Cruise secured 7.5 billion in funding [besides GM, the company has a number of outside investors, including Honda, Softbank, and T. Rowe Price]. That [funding] is another reason Cruise is ahead of many others, because this problem requires deep resources.

“If you can do an autonomous vehicle in San Francisco you can do it almost anywhere.”

[And then there is the driving environment.] When I speak to my peers in the industry, they have a lot of respect for us, because the problems to solve in San Francisco technically are an order of magnitude harder. It is a tight environment, with a lot of pedestrians, and driving patterns that, let’s put it this way, are not necessarily the best in the nation. Which means we are seeing more problems ahead of our competitors, which gets us to better [software]. I think if you can do an autonomous vehicle in San Francisco you can do it almost anywhere.

A version of this post appears in the September 2019 print magazine as “AI Engineers: The Autonomous-Vehicle Industry Wants You.” Continue reading →

Posted in Human Robots

#435804 New AI Systems Are Here to Personalize ...

Posted on September 24, 2019 by Android

The narratives about automation and its impact on jobs go from urgent to hopeful and everything in between. Regardless where you land, it’s hard to argue against the idea that technologies like AI and robotics will change our economy and the nature of work in the coming years.

A recent World Economic Forum report noted that some estimates show automation could displace 75 million jobs by 2022, while at the same time creating 133 million new roles. While these estimates predict a net positive for the number of new jobs in the coming decade, displaced workers will need to learn new skills to adapt to the changes. If employees can’t be retrained quickly for jobs in the changing economy, society is likely to face some degree of turmoil.

According to Bryan Talebi, CEO and founder of AI education startup Ahura AI, the same technologies erasing and creating jobs can help workers bridge the gap between the two.

Ahura is developing a product to capture biometric data from adult learners who are using computers to complete online education programs. The goal is to feed this data to an AI system that can modify and adapt their program to optimize for the most effective teaching method.

While the prospect of a computer recording and scrutinizing a learner’s behavioral data will surely generate unease across a society growing more aware and uncomfortable with digital surveillance, some people may look past such discomfort if they experience improved learning outcomes. Users of the system would, in theory, have their own personalized instruction shaped specifically for their unique learning style.

And according to Talebi, their systems are showing some promise.

“Based on our early tests, our technology allows people to learn three to five times faster than traditional education,” Talebi told me.

Currently, Ahura’s system uses the video camera and microphone that come standard on the laptops, tablets, and mobile devices most students are using for their learning programs.

With the computer’s camera Ahura can capture facial movements and micro expressions, measure eye movements, and track fidget score (a measure of how much a student moves while learning). The microphone tracks voice sentiment, and the AI leverages natural language processing to review the learner’s word usage.

From this collection of data Ahura can, according to Talebi, identify the optimal way to deliver content to each individual.

For some users that might mean a video tutorial is the best style of learning, while others may benefit more from some form of experiential or text-based delivery.

“The goal is to alter the format of the content in real time to optimize for attention and retention of the information,” said Talebi. One of Ahura’s main goals is to reduce the frequency with which students switch from their learning program to distractions like social media.

“We can now predict with a 60 percent confidence interval ten seconds before someone switches over to Facebook or Instagram. There’s a lot of work to do to get that up to a 95 percent level, so I don’t want to overstate things, but that’s a promising indication that we can work to cut down on the amount of context-switching by our students,” Talebi said.

Talebi repeatedly mentioned his ambition to leverage the same design principles used by Facebook, Twitter, and others to increase the time users spend on those platforms, but instead use them to design more compelling and even addictive education programs that can compete for attention with social media.

But the notion that Ahura’s system could one day be used to create compelling or addictive education necessarily presses against a set of justified fears surrounding data privacy. Growing anxiety surrounding the potential to misuse user data for social manipulation is widespread.

“Of course there is a real danger, especially because we are collecting so much data about our users which is specifically connected to how they consume content. And because we are looking so closely at the ways people interact with content, it’s incredibly important that this technology never be used for propaganda or to sell things to people,” Talebi tried to assure me.

Unsurprisingly (and worrying), using this AI system to sell products to people is exactly where some investors’ ambitions immediately turn once they learn about the company’s capabilities, according to Talebi. During our discussion Talebi regularly cited the now infamous example of Cambridge Analytica, the political consulting firm hired by the Trump campaign to run a psychographically targeted persuasion campaign on the US population during the most recent presidential election.

“It’s important that we don’t use this technology in those ways. We’re aware that things can go sideways, so we’re hoping to put up guardrails to ensure our system is helping and not harming society,” Talebi said.

Talebi will surely need to take real action on such a claim, but says the company is in the process of identifying a structure for an ethics review board—one that carries significant influence with similar voting authority as the executive team and the regular board.

“Our goal is to build an ethics review board that has teeth, is diverse in both gender and background but also in thought and belief structures. The idea is to have our ethics review panel ensure we’re building things ethically,” he said.

Data privacy appears to be an important issue for Talebi, who occasionally referenced a major competitor in the space based in China. According to a recent article from MIT Tech Review outlining the astonishing growth of AI-powered education platforms in China, data privacy concerns may be less severe there than in the West.

Ahura is currently developing upgrades to an early alpha-stage prototype, but is already capturing data from students from at least one Ivy League school and a variety of other places. Their next step is to roll out a working beta version to over 200,000 users as part of a partnership with an unnamed corporate client who will be measuring the platform’s efficacy against a control group.

Going forward, Ahura hopes to add to its suite of biometric data capture by including things like pupil dilation and facial flushing, heart rate, sleep patterns, or whatever else may give their system an edge in improving learning outcomes.

As information technologies increasingly automate work, it’s likely we’ll also see rapid changes to our labor systems. It’s also looking increasingly likely that those same technologies will be used to improve our ability to give people the right skills when they need them. It may be one way to address the challenges automation is sure to bring.

Image Credit: Gerd Altmann / Pixabay Continue reading →

Posted in Human Robots

#435742 This ‘Useless’ Social Robot ...

Posted on September 19, 2019 by Android

The recent high profile failures of some home social robots (and the companies behind them) have made it even more challenging than it was before to develop robots in that space. And it was challenging enough to begin with—making a robot that can autonomous interact with random humans in their homes over a long period of time for a price that people can afford is extraordinarily difficult. However, the massive amount of initial interest in robots like Jibo, Kuri, Vector, and Buddy prove that people do want these things, or at least think they do, and while that’s the case, there’s incentive for other companies to give social home robots a try.

One of those companies is Zoetic, founded in 2107 by Mita Yun and Jitu Das, both ex-Googlers. Their robot, Kiki, is more or less exactly what you’d expect from a social home robot: It’s cute, white, roundish, has big eyes, promises that it will be your “robot sidekick,” and is not cheap: It’s on Kicksterter for $800. Kiki is among what appears to be a sort of tentative second wave of social home robots, where designers have (presumably) had a chance to take everything that they learned from the social home robot pioneers and use it to make things better this time around.

Kiki’s Kickstarter video is, again, more or less exactly what you’d expect from a social home robot crowdfunding campaign:

We won’t get into all of the details on Kiki in this article (the Kickstarter page has tons of information), but a few distinguishing features:

Each Kiki will develop its own personality over time through its daily interactions with its owner, other people, and other Kikis.
Interacting with Kiki is more abstract than with most robots—it can understand some specific words and phrases, and will occasionally use a few specific words or two, but otherwise it’s mostly listening to your tone of voice and responding with sounds rather than speech.
Kiki doesn’t move on its own, but it can operate for up to two hours away from its charging dock.
Depending on how your treat Kiki, it can get depressed or neurotic. It also needs to be fed, which you can do by drawing different kinds of food in the app.
Everything Kiki does runs on-board the robot. It has Wi-Fi connectivity for updates, but doesn’t rely on the cloud for anything in real-time, meaning that your data stays on the robot and that the robot will continue to function even if its remote service shuts down.

It’s hard to say whether features like these are unique enough to help Kiki be successful where other social home robots haven’t been, so we spoke with Zoetic co-founder Mita Yun and asked her why she believes that Kiki is going to be the social home robot that makes it.

IEEE Spectrum: What’s your background?

Mita Yun: I was an only child growing up, and so I always wanted something like Doraemon or Totoro. Something that when you come home it’s there to greet you, not just because it’s programmed to do that but because it’s actually actively happy to see you, and only you. I was so interested in this that I went to study robotics at CMU and then after I graduated I joined Google and worked there for five years. I tended to go for the more risky and more fun projects, but they always got cancelled—the first project I joined was called Android at Home, and then I joined Google Glass, and then I joined a team called Robots for Kids. That project was building educational robots, and then I just realized that when we’re adding technology to something, to a product, we’re actually taking the life away somehow, and the kids were more connected with stuffed animals compared to the educational robots we were building. That project was also cancelled, and in 2017, I left with a coworker of mine (Jitu Das) to bring this dream into reality. And now we’re building Kiki.

“Jibo was Alexa plus cuteness equals $800, and I feel like that equation doesn’t work for most people, and that eventually killed the company. So, for Kiki, we are actually building something very different. We’re building something that’s completely useless”
—Mita Yun, Zoetic

You started working on Kiki in 2017, when things were already getting challenging for Jibo—why did you decide to start developing a social home robot at that point?

I thought Jibo was great. It had a special magical way of moving, and it was such a new idea that you could have this robot with embodiment and it can actually be your assistant. The problem with Jibo, in my opinion, was that it took too long to fulfill the orders. It took them three to four years to actually manufacture, because it was a very complex piece of hardware, and then during that period of time Alexa and Google Home came out, and they started selling these voice systems for $30 and then you have Jibo for $800. Jibo was Alexa plus cuteness equals $800, and I feel like that equation doesn’t work for most people, and that eventually killed the company. So, for Kiki, we are actually building something very different. We’re building something that’s completely useless.

Can you elaborate on “completely useless?”

I feel like people are initially connected with robots because they remind them of a character. And it’s the closest we can get to a character other than an organic character like an animal. So we’re connected to a character like when we have a robot in a mall that’s roaming around, even if it looks really ugly, like if it doesn’t have eyes, people still take selfies with it. Why? Because they think it’s a character. And humans are just hardwired to love characters and love stories. With Kiki, we just wanted to build a character that’s alive, we don’t want to have a character do anything super useful.

I understand why other robotics companies are adding Alexa integration to their robots, and I think that’s great. But the dream I had, and the understanding I have about robotics technology, is that for a consumer robot especially, it is very very difficult for the robot to justify its price through usefulness. And then there’s also research showing that the more useless something is, the easier it is to have an emotional connection, so that’s why we want to keep Kiki very useless.

What kind of character are you creating with Kiki?

The whole design principle around Kiki is we want to make it a very vulnerable character. In terms of its status at home, it’s not going to be higher or equal status as the owner, but slightly lower status than the human, and it’s vulnerable and needs you to take care of it in order to grow up into a good personality robot.

We don’t let Kiki speak full English sentences, because whenever it does that, people are going to think it’s at least as intelligent as a baby, which is impossible for robots at this point. And we also don’t let it move around, because when you have it move around, people are going to think “I’m going to call Kiki’s name, and then Kiki is will come to me.” But that is actually very difficult to build. And then also we don’t have any voice integration so it doesn’t tell you about the stock market price and so on.

Photo: Zoetic

Kiki is designed to be “vulnerable,” and it needs you to take care of it so it can “grow up into a good personality robot,” according to its creators.

That sounds similar to what Mayfield did with Kuri, emphasizing an emotional connection rather than specific functionality.

It is very similar, but one of the key differences from Kuri, I think, is that Kuri started with a Kobuki base, and then it’s wrapped into a cute shell, and they added sounds. So Kuri started with utility in mind—navigation is an important part of Kuri, so they started with that challenge. For Kiki, we started with the eyes. The entire thing started with the character itself.

How will you be able to convince your customers to spend $800 on a robot that you’ve described as “useless” in some ways?

Because it’s useless, it’s actually easier to convince people, because it provides you with an emotional connection. I think Kiki is not a utility-driven product, so the adoption cycle is different. For a functional product, it’s very easy to pick up, because you can justify it by saying “I’m going to pay this much and then my life can become this much more efficient.” But it’s also very easy to be replaced and forgotten. For an emotional-driven product, it’s slower to pick up, but once people actually pick it up, they’re going to be hooked—they get be connected with it, and they’re willing to invest more into taking care of the robot so it will grow up to be smarter.

Maintaining value over time has been another challenge for social home robots. How will you make sure that people don’t get bored with Kiki after a few weeks?

Of course Kiki has limits in what it can do. We can combine the eyes, the facial expression, the motors, and lights and sounds, but is it going to be constantly entertaining? So we think of this as, imagine if a human is actually puppeteering Kiki—can Kiki stay interesting if a human is puppeteering it and interacting with the owner? So I think what makes a robot interesting is not just in the physical expressions, but the part in between that and the robot conveying its intentions and emotions.

For example, if you come into the room and then Kiki decides it will turn the other direction, ignore you, and then you feel like, huh, why did the robot do that to me? Did I do something wrong? And then maybe you will come up to it and you will try to figure out why it did that. So, even though Kiki can only express in four different dimensions, it can still make things very interesting, and then when its strategies change, it makes it feel like a new experience.

There’s also an explore and exploit process going on. Kiki wants to make you smile, and it will try different things. It could try to chase its tail, and if you smile, Kiki learns that this works and will exploit it. But maybe after doing it three times, you no longer find it funny, because you’re bored of it, and then Kiki will observe your reactions and be motivated to explore a new strategy.

Photo: Zoetic

Kiki’s creators are hoping that, with an emotionally engaging robot, it will be easier for people to get attached to it and willing to spend time taking care of it.

A particular risk with crowdfunding a robot like this is setting expectations unreasonably high. The emphasis on personality and emotional engagement with Kiki seems like it may be very difficult for the robot to live up to in practice.

I think we invested more than most robotics companies into really building out Kiki’s personality, because that is the single most important thing to us. For Jibo a lot of the focus was in the assistant, and for Kuri, it’s more in the movement. For Kiki, it’s very much in the personality.

I feel like when most people talk about personality, they’re mainly talking about expression. With Kiki, it’s not just in the expression itself, not just in the voice or the eyes or the output layer, it’s in the layer in between—when Kiki receives input, how will it make decisions about what to do? We actually don’t think the personality of Kiki is categorizable, which is why I feel like Kiki has a deeper implementation of how personalities should work. And you’re right, Kiki doesn’t really understand why you’re feeling a certain way, it just reads your facial expressions. It’s maybe not your best friend, but maybe closer to your little guinea pig robot.

Photo: Zoetic

The team behind Kiki paid particular attention to its eyes, and designed the robot to always face the person that it is interacting with.

Is that where you’d put Kiki on the scale of human to pet?

Kiki is definitely not human, we want to keep it very far away from human. And it’s also not a dog or cat. When we were designing Kiki, we took inspiration from mammals because humans are deeply connected to mammals since we’re mammals ourselves. And specifically we’re connected to predator animals. With prey animals, their eyes are usually on the sides of their heads, because they need to see different angles. A predator animal needs to hunt, they need to focus. Cats and dogs are predator animals. So with Kiki, that’s why we made sure the eyes are on one side of the face and the head can actuate independently from the body and the body can turn so it’s always facing the person that it’s paying attention to.

I feel like Kiki is probably does more than a plant. It does more than a fish, because a fish doesn’t look you in the eyes. It’s not as smart as a cat or a dog, so I would just put it in this guinea pig kind of category.

What have you found so far when running user studies with Kiki?

When we were first designing Kiki we went through a whole series of prototypes. One of the earlier prototypes of Kiki looked like a CRT, like a very old monitor, and when we were testing that with people they didn’t even want to touch it. Kiki’s design inspiration actually came from an airplane, with a very angular, futuristic look, but based on user feedback we made it more round and more friendly to the touch. The lights were another feature request from the users, which adds another layer of expressivity to Kiki, and they wanted to see multiple Kikis working together with different personalities. Users also wanted different looks for Kiki, to make it look like a deer or a unicorn, for example, and we actually did take that into consideration because it doesn’t look like any particular mammal. In the future, you’ll be able to have different ears to make it look like completely different animals.

There has been a lot of user feedback that we didn’t implement—I believe we should observe the users reactions and feedback but not listen to their advice. The users shouldn’t be our product designers, because if you test Kiki with 10 users, eight of them will tell you they want Alexa in it. But we’re never going to add Alexa integration to Kiki because that’s not what it’s meant to do.

While it’s far too early to tell whether Kiki will be a long-term success, the Kickstarter campaign is currently over 95 percent funded with 8 days to go, and 34 robots are still available for a May 2020 delivery.

[ Kickstarter ] Continue reading →

Posted in Human Robots

#435687 Humanoid Robots Teach Coping Skills to ...

Posted on September 16, 2019 by Android

Photo: Rob Felt

IEEE Senior Member Ayanna Howard with one of the interactive androids that help children with autism improve their social and emotional engagement.

THE INSTITUTEChildren with autism spectrum disorder can have a difficult time expressing their emotions and can be highly sensitive to sound, sight, and touch. That sometimes restricts their participation in everyday activities, leaving them socially isolated. Occupational therapists can help them cope better, but the time they’re able to spend is limited and the sessions tend to be expensive.

Roboticist Ayanna Howard, an IEEE senior member, has been using interactive androids to guide children with autism on ways to socially and emotionally engage with others—as a supplement to therapy. Howard is chair of the School of Interactive Computing and director of the Human-Automation Systems Lab at Georgia Tech. She helped found Zyrobotics, a Georgia Tech VentureLab startup that is working on AI and robotics technologies to engage children with special needs. Last year Forbes named Howard, Zyrobotics’ chief technology officer, one of the Top 50 U.S. Women in Tech.

In a recent study, Howard and other researchers explored how robots might help children navigate sensory experiences. The experiment involved 18 participants between the ages of 4 and 12; five had autism, and the rest were meeting typical developmental milestones. Two humanoid robots were programmed to express boredom, excitement, nervousness, and 17 other emotional states. As children explored stations set up for hearing, seeing, smelling, tasting, and touching, the robots modeled what the socially acceptable responses should be.

“If a child’s expression is one of happiness or joy, the robot will have a corresponding response of encouragement,” Howard says. “If there are aspects of frustration or sadness, the robot will provide input to try again.” The study suggested that many children with autism exhibit stronger levels of engagement when the robots interact with them at such sensory stations.

It is one of many robotics projects Howard has tackled. She has designed robots for researching glaciers, and she is working on assistive robots for the home, as well as an exoskeleton that can help children who have motor disabilities.

Howard spoke about her work during the Ethics in AI: Impacts of (Anti?) Social Robotics panel session held in May at the IEEE Vision, Innovation, and Challenges Summit in San Diego. You can watch the session on IEEE.tv.

The next IEEE Vision, Innovation, and Challenges Summit and Honors Ceremony will be held on 15 May 2020 at the JW Marriott Parq Vancouver hotel, in Vancouver.

In this interview with The Institute, Howard talks about how she got involved with assistive technologies, the need for a more diverse workforce, and ways IEEE has benefited her career.

FOCUS ON ACCESSIBILITY
Howard was inspired to work on technology that can improve accessibility in 2008 while teaching high school students at a summer camp devoted to science, technology, engineering, and math.

“A young lady with a visual impairment attended camp. The robot programming tools being used at the camp weren’t accessible to her,” Howard says. “As an engineer, I want to fix problems when I see them, so we ended up designing tools to enable access to programming tools that could be used in STEM education.

“That was my starting motivation, and this theme of accessibility has expanded to become a main focus of my research. One of the things about this world of accessibility is that when you start interacting with kids and parents, you discover another world out there of assistive technologies and how robotics can be used for good in education as well as therapy.”

DIVERSITY OF THOUGHT
The Institute asked Howard why it’s important to have a more diverse STEM workforce and what could be done to increase the number of women and others from underrepresented groups.

“The makeup of the current engineering workforce isn’t necessarily representative of the world, which is composed of different races, cultures, ages, disabilities, and socio-economic backgrounds,” Howard says. “We’re creating products used by people around the globe, so we have to ensure they’re being designed for a diverse population. As IEEE members, we also need to engage with people who aren’t engineers, and we don’t do that enough.”

Educational institutions are doing a better job of increasing diversity in areas such as gender, she says, adding that more work is needed because the enrollment numbers still aren’t representative of the population and the gains don’t necessarily carry through after graduation.

“There has been an increase in the number of underrepresented minorities and females going into engineering and computer science,” she says, “but data has shown that their numbers are not sustained in the workforce.”

ROLE MODEL
Because there are more underrepresented groups on today’s college campuses that can form a community, the lack of engineering role models—although a concern on campuses—is more extreme for preuniversity students, Howard says.

“Depending on where you go to school, you may not know what an engineer does or even consider engineering as an option,” she says, “so there’s still a big disconnect there.”

Howard has been involved for many years in math- and science-mentoring programs for at-risk high school girls. She tells them to find what they’re passionate about and combine it with math and science to create something. She also advises them not to let anyone tell them that they can’t.

Howard’s father is an engineer. She says he never encouraged or discouraged her to become one, but when she broke something, he would show her how to fix it and talk her through the process. Along the way, he taught her a logical way of thinking she says all engineers have.

“When I would try to explain something, he would quiz me and tell me to ‘think more logically,’” she says.

Howard earned a bachelor’s degree in engineering from Brown University, in Providence, R.I., then she received both a master’s and doctorate degree in electrical engineering from the University of Southern California. Before joining the faculty of Georgia Tech in 2005, she worked at NASA’s Jet Propulsion Laboratory at the California Institute of Technology for more than a decade as a senior robotics researcher and deputy manager in the Office of the Chief Scientist.

ACTIVE VOLUNTEER
Howard’s father was also an IEEE member, but that’s not why she joined the organization. She says she signed up when she was a student because, “that was something that you just did. Plus, my student membership fee was subsidized.”

She kept the membership as a grad student because of the discounted rates members receive on conferences.

Those conferences have had an impact on her career. “They allow you to understand what the state of the art is,” she says. “Back then you received a printed conference proceeding and reading through it was brutal, but by attending it in person, you got a 15-minute snippet about the research.”

Howard is an active volunteer with the IEEE Robotics and Automation and the IEEE Systems, Man, and Cybernetics societies, holding many positions and serving on several committees. She is also featured in the IEEE Impact Creators campaign. These members were selected because they inspire others to innovate for a better tomorrow.

“I value IEEE for its community,” she says. “One of the nice things about IEEE is that it’s international.” Continue reading →

Posted in Human Robots