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#436466 How Two Robots Learned to Grill and ...
The list of things robots can do seems to be growing by the week. They can play sports, help us explore outer space and the deep sea, take over some of our boring everyday tasks, and even assemble Ikea furniture.
Now they can add one more accomplishment to the list: grilling and serving a hot dog.
It seems like a pretty straightforward task, and as far as grilling goes, hot dogs are about as easy as it gets (along with, maybe, burgers? Hot dogs require more rotation, but it’s easier to tell when they’re done since they’re lighter in color).
Let’s paint a picture: you’re manning the grill at your family’s annual Fourth of July celebration. You’ve got a 10-pack of plump, juicy beef franks and a hungry crowd of relatives whose food-to-alcohol ratio is getting pretty skewed—they need some solid calories, pronto. What are the steps you need to take to get those franks from package to plate?
Each one needs to be placed on the grill, rotated every couple minutes for even cooking, removed from the grill when you deem it’s done, then—if you’re the kind of guy or gal who goes the extra mile—placed in a bun and dressed with ketchup, mustard, pickles, and the like before being handed over to salivating, too-loud Uncle Hector or sweet, bored Cousin Margaret.
While carrying out your grillmaster duties, you know better than to drop the hot dogs on the ground, leave them cooking on one side for too long, squeeze them to the point of breaking or bursting, and any other hot-dog-ruining amateur moves.
But for a robot, that’s a lot to figure out, especially if they have no prior knowledge of grilling hot dogs (which, well, most robots don’t).
As described in a paper published in this week’s Science Robotics, a team from Boston University programmed two robotic arms to use reinforcement learning—a branch of machine learning in which software gathers information about its environment then learns from it by replaying its experiences and incorporating rewards—to cook and serve hot dogs.
The team used a set of formulas to specify and combine tasks (“pick up hot dog and place on the grill”), meet safety requirements (“always avoid collisions”), and incorporate general prior knowledge (“you cannot pick up another hot dog if you are already holding one”).
Baxter and Jaco—as the two robots were dubbed—were trained through computer simulations. The paper’s authors emphasized their use of what they call a “formal specification language” for training the software, with the aim of generating easily-interpretable task descriptions. In reinforcement learning, they explain, being able to understand how a reward function influences an AI’s learning process is a key component in understanding the system’s behavior—but most systems lack this quality, and are thus likely to be lumped into the ‘black box’ of AI.
The robots’ decisions throughout the hot dog prep process—when to turn a hot dog, when to take it off the grill, and so on—are, the authors write, “easily interpretable from the beginning because the language is very similar to plain English.”
Besides being a step towards more explainable AI systems, Baxter and Jaco are another example of fast-food robots—following in the footsteps of their burger and pizza counterparts—that may take over some repetitive manual tasks currently performed by human workers. As robots’ capabilities improve through incremental progress like this, they’ll be able to take on additional tasks.
In a not-so-distant future, then, you just may find yourself throwing back drinks with Uncle Hector and Cousin Margaret while your robotic replacement mans the grill, churning out hot dogs that are perfectly cooked every time.
Image Credit: Image by Muhammad Ribkhan from Pixabay Continue reading
Posted in Human Robots
#436462 Robotic Exoskeletons, Like This One, Are ...
When you imagine an exoskeleton, chances are it might look a bit like the Guardian XO from Sarcos Robotics. The XO is literally a robot you wear (or maybe, it wears you). The suit’s powered limbs sense your movements and match their position to yours with little latency to give you effortless superstrength and endurance—lifting 200 pounds will feel like 10.
A vision of robots and humankind working together in harmony. Now, isn’t that nice?
Of course, there isn’t anything terribly novel about an exoskeleton. We’ve seen plenty of concepts and demonstrations in the last decade. These include light exoskeletons tailored to industrial settings—some of which are being tested out by the likes of Honda—and healthcare exoskeletons that support the elderly or folks with disabilities.
Full-body powered robotic exoskeletons are a bit rarer, which makes the Sarcos suit pretty cool to look at. But like all things in robotics, practicality matters as much as vision. It’s worth asking: Will anyone buy and use the thing? Is it more than a concept video?
Sarcos thinks so, and they’re excited about it. “If you were to ask the question, what does 30 years and $300 million look like,” Sarcos CEO, Ben Wolff, told IEEE Spectrum, “you’re going to see it downstairs.”
The XO appears to check a few key boxes. For one, it’s user friendly. According to Sarcos, it only takes a few minutes for the uninitiated to strap in and get up to speed. Feeling comfortable doing work with the suit takes a few hours. This is thanks to a high degree of sensor-based automation that allows the robot to seamlessly match its user’s movements.
The XO can also operate for more than a few minutes. It has two hours of battery life, and with spares on hand, it can go all day. The batteries are hot-swappable, meaning you can replace a drained battery with a new one without shutting the system down.
The suit is aimed at manufacturing, where workers are regularly moving heavy stuff around. Additionally, Wolff told CNET, the suit could see military use. But that doesn’t mean Avatar-style combat. The XO, Wolff said, is primarily about logistics (lifting and moving heavy loads) and isn’t designed to be armored, so it won’t likely see the front lines.
The system will set customers back $100,000 a year to rent, which sounds like a lot, but for industrial or military purposes, the six-figure rental may not deter would-be customers if the suit proves itself a useful bit of equipment. (And it’s reasonable to imagine the price coming down as the technology becomes more commonplace and competitors arrive.)
Sarcos got into exoskeletons a couple decades ago and was originally funded by the military (like many robotics endeavors). Videos hit YouTube as long ago as 2008, but after announcing the company was taking orders for the XO earlier this year, Sarcos says they’ll deliver the first alpha units in January, which is a notable milestone.
Broadly, robotics has advanced a lot in recent years. YouTube sensations like Boston Dynamics have regularly earned millions of views (and inevitably, headlines stoking robot fear). They went from tethered treadmill sessions to untethered backflips off boxes. While today’s robots really are vastly superior to their ancestors, they’ve struggled to prove themselves useful. A counterpoint to flashy YouTube videos, the DARPA Robotics Challenge gave birth to another meme altogether. Robots falling over. Often and awkwardly.
This year marks some of the first commercial fruits of a few decades’ research. Boston Dynamics recently started offering its robot dog, Spot, to select customers in 2019. Whether this proves to be a headline-worthy flash in the pan or something sustainable remains to be seen. But between robots with more autonomy and exoskeletons like the XO, the exoskeleton variety will likely be easier to make more practical for various uses.
Whereas autonomous robots require highly advanced automation to navigate uncertain and ever-changing conditions—automation which, at the moment, remains largely elusive (though the likes of Google are pairing the latest AI with robots to tackle the problem)—an exoskeleton mainly requires physical automation. The really hard bits, like navigating and recognizing and interacting with objects, are outsourced to its human operator.
As it turns out, for today’s robots the best AI is still us. We may yet get chipper automatons like Rosy the Robot, but until then, for complicated applications, we’ll strap into our mechs for their strength and endurance, and they’ll wear us for our brains.
Image Credit: Sarcos Robotics Continue reading
Posted in Human Robots
#436261 AI and the future of work: The prospects ...
AI experts gathered at MIT last week, with the aim of predicting the role artificial intelligence will play in the future of work. Will it be the enemy of the human worker? Will it prove to be a savior? Or will it be just another innovation—like electricity or the internet?
As IEEE Spectrum previously reported, this conference (“AI and the Future of Work Congress”), held at MIT’s Kresge Auditorium, offered sometimes pessimistic outlooks on the job- and industry-destroying path that AI and automation seems to be taking: Self-driving technology will put truck drivers out of work; smart law clerk algorithms will put paralegals out of work; robots will (continue to) put factory and warehouse workers out of work.
Andrew McAfee, co-director of MIT’s Initiative on the Digital Economy, said even just in the past couple years, he’s noticed a shift in the public’s perception of AI. “I remember from previous versions of this conference, it felt like we had to make the case that we’re living in a period of accelerating change and that AI’s going to have a big impact,” he said. “Nobody had to make that case today.”
Elisabeth Reynolds, executive director of MIT’s Task Force on the Work of the Future, noted that following the path of least resistance is not a viable way forward. “If we do nothing, we’re in trouble,” she said. “The future will not take care of itself. We have to do something about it.”
Panelists and speakers spoke about championing productive uses of AI in the workplace, which ultimately benefit both employees and customers.
As one example, Zeynep Ton, professor at MIT Sloan School of Management, highlighted retailer Sam’s Club’s recent rollout of a program called Sam’s Garage. Previously customers shopping for tires for their car spent somewhere between 30 and 45 minutes with a Sam’s Club associate paging through manuals and looking up specs on websites.
But with an AI algorithm, they were able to cut that spec hunting time down to 2.2 minutes. “Now instead of wasting their time trying to figure out the different tires, they can field the different options and talk about which one would work best [for the customer],” she said. “This is a great example of solving a real problem, including [enhancing] the experience of the associate as well as the customer.”
“We think of it as an AI-first world that’s coming,” said Scott Prevost, VP of engineering at Adobe. Prevost said AI agents in Adobe’s software will behave something like a creative assistant or intern who will take care of more mundane tasks for you.
“We need a mindset change. That it is not just about minimizing costs or maximizing tax benefits, but really worrying about what kind of society we’re creating and what kind of environment we’re creating if we keep on just automating and [eliminating] good jobs.”
—Daron Acemoglu, MIT Institute Professor of Economics
Prevost cited an internal survey of Adobe customers that found 74 percent of respondents’ time was spent doing repetitive work—the kind that might be automated by an AI script or smart agent.
“It used to be you’d have the resources to work on three ideas [for a creative pitch or presentation],” Prevost said. “But if the AI can do a lot of the production work, then you can have 10 or 100. Which means you can actually explore some of the further out ideas. It’s also lowering the bar for everyday people to create really compelling output.”
In addition to changing the nature of work, noted a number of speakers at the event, AI is also directly transforming the workforce.
Jacob Hsu, CEO of the recruitment company Catalyte spoke about using AI as a job placement tool. The company seeks to fill myriad positions including auto mechanics, baristas, and office workers—with its sights on candidates including young people and mid-career job changers. To find them, it advertises on Craigslist, social media, and traditional media.
The prospects who sign up with Catalyte take a battery of tests. The company’s AI algorithms then match each prospect’s skills with the field best suited for their talents.
“We want to be like the Harry Potter Sorting Hat,” Hsu said.
Guillermo Miranda, IBM’s global head of corporate social responsibility, said IBM has increasingly been hiring based not on credentials but on skills. For instance, he said, as much as 50 per cent of the company’s new hires in some divisions do not have a traditional four-year college degree. “As a company, we need to be much more clear about hiring by skills,” he said. “It takes discipline. It takes conviction. It takes a little bit of enforcing with H.R. by the business leaders. But if you hire by skills, it works.”
Ardine Williams, Amazon’s VP of workforce development, said the e-commerce giant has been experimenting with developing skills of the employees at its warehouses (a.k.a. fulfillment centers) with an eye toward putting them in a position to get higher-paying work with other companies.
She described an agreement Amazon had made in its Dallas fulfillment center with aircraft maker Sikorsky, which had been experiencing a shortage of skilled workers for its nearby factory. So Amazon offered to its employees a free certification training to seek higher-paying work at Sikorsky.
“I do that because now I have an attraction mechanism—like a G.I. Bill,” Williams said. The program is also only available for employees who have worked at least a year with Amazon. So their program offers medium-term job retention, while ultimately moving workers up the wage ladder.
Radha Basu, CEO of AI data company iMerit, said her firm aggressively hires from the pool of women and under-resourced minority communities in the U.S. and India. The company specializes in turning unstructured data (e.g. video or audio feeds) into tagged and annotated data for machine learning, natural language processing, or computer vision applications.
“There is a motivation with these young people to learn these things,” she said. “It comes with no baggage.”
Alastair Fitzpayne, executive director of The Aspen Institute’s Future of Work Initiative, said the future of work ultimately means, in bottom-line terms, the future of human capital. “We have an R&D tax credit,” he said. “We’ve had it for decades. It provides credit for companies that make new investment in research and development. But we have nothing on the human capital side that’s analogous.”
So a company that’s making a big investment in worker training does it on their own dime, without any of the tax benefits that they might accrue if they, say, spent it on new equipment or new technology. Fitzpayne said a simple tweak to the R&D tax credit could make a big difference by incentivizing new investment programs in worker training. Which still means Amazon’s pre-existing worker training programs—for a company that already famously pays no taxes—would not count.
“We need a different way of developing new technologies,” said Daron Acemoglu, MIT Institute Professor of Economics. He pointed to the clean energy sector as an example. First a consensus around the problem needs to emerge. Then a broadly agreed-upon set of goals and measurements needs to be developed (e.g., that AI and automation would, for instance, create at least X new jobs for every Y jobs that it eliminates).
Then it just needs to be implemented.
“We need to build a consensus that, along the path we’re following at the moment, there are going to be increasing problems for labor,” Acemoglu said. “We need a mindset change. That it is not just about minimizing costs or maximizing tax benefits, but really worrying about what kind of society we’re creating and what kind of environment we’re creating if we keep on just automating and [eliminating] good jobs.” Continue reading
Posted in Human Robots
#435824 A Q&A with Cruise’s head of AI, ...
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