Tag Archives: spirit
#437630 How Toyota Research Envisions the Future ...
Yesterday, the Toyota Research Institute (TRI) showed off some of the projects that it’s been working on recently, including a ceiling-mounted robot that could one day help us with household chores. That system is just one example of how TRI envisions the future of robotics and artificial intelligence. As TRI CEO Gill Pratt told us, the company is focusing on robotics and AI technology for “amplifying, rather than replacing, human beings.” In other words, Toyota wants to develop robots not for convenience or to do our jobs for us, but rather to allow people to continue to live and work independently even as we age.
To better understand Toyota’s vision of robotics 15 to 20 years from now, it’s worth watching the 20-minute video below, which depicts various scenarios “where the application of robotic capabilities is enabling members of an aging society to live full and independent lives in spite of the challenges that getting older brings.” It’s a long video, but it helps explains TRI’s perspective on how robots will collaborate with humans in our daily lives over the next couple of decades.
Those are some interesting conceptual telepresence-controlled bipeds they’ve got running around in that video, right?
For more details, we sent TRI some questions on how it plans to go from concepts like the ones shown in the video to real products that can be deployed in human environments. Below are answers from TRI CEO Gill Pratt, who is also chief scientist for Toyota Motor Corp.; Steffi Paepcke, senior UX designer at TRI; and Max Bajracharya, VP of robotics at TRI.
IEEE Spectrum: TRI seems to have a more explicit focus on eventual commercialization than most of the robotics research that we cover. At what point TRI starts to think about things like reliability and cost?
Photo: TRI
Toyota is exploring robots capable of manipulating dishes in a sink and a dishwasher, performing experiments and simulations to make sure that the robots can handle a wide range of conditions.
Gill Pratt: It’s a really interesting question, because the normal way to think about this would be to say, well, both reliability and cost are product development tasks. But actually, we need to think about it at the earliest possible stage with research as well. The hardware that we use in the laboratory for doing experiments, we don’t worry about cost there, or not nearly as much as you’d worry about for a product. However, in terms of what research we do, we very much have to think about, is it possible (if the research is successful) for it to end up in a product that has a reasonable cost. Because if a customer can’t afford what we come up with, maybe it has some academic value but it’s not actually going to make a difference in their quality of life in the real world. So we think about cost very much from the beginning.
The same is true with reliability. Right now, we’re working very hard to make our control techniques robust to wide variations in the environment. For instance, in work that Russ Tedrake is doing with manipulating dishes in a sink and a dishwasher, both in physical testing and in simulation, we’re doing thousands and now millions of different experiments to make sure that we can handle the edge cases and it works over a very wide range of conditions.
A tremendous amount of work that we do is trying to bring robotics out of the age of doing demonstrations. There’s been a history of robotics where for some time, things have not been reliable, so we’d catch the robot succeeding just once and then show that video to the world, and people would get the mis-impression that it worked all of the time. Some researchers have been very good about showing the blooper reel too, to show that some of the time, robots don’t work.
“A tremendous amount of work that we do is trying to bring robotics out of the age of doing demonstrations. There’s been a history of robotics where for some time, things have not been reliable, so we’d catch the robot succeeding just once and then show that video to the world, and people would get the mis-impression that it worked all of the time.”
—Gill Pratt, TRI
In the spirit of sharing things that didn’t work, can you tell us a bit about some of the robots that TRI has had under development that didn’t make it into the demo yesterday because they were abandoned along the way?
Steffi Paepcke: We’re really looking at how we can connect people; it can be hard to stay in touch and see our loved ones as much as we would like to. There have been a few prototypes that we’ve worked on that had to be put on the shelf, at least for the time being. We were exploring how to use light so that people could be ambiently aware of one another across distances. I was very excited about that—the internal name was “glowing orb.” For a variety of reasons, it didn’t work out, but it was really fascinating to investigate different modalities for keeping in touch.
Another prototype we worked on—we found through our research that grocery shopping is obviously an important part of life, and for a lot of older adults, it’s not necessarily the right answer to always have groceries delivered. Getting up and getting out of the house keeps you physically active, and a lot of people prefer to continue doing it themselves. But it can be challenging, especially if you’re purchasing heavy items that you need to transport. We had a prototype that assisted with grocery shopping, but when we pivoted our focus to Japan, we found that the inside of a Japanese home really needs to stay inside, and the outside needs to stay outside, so a robot that traverses both domains is probably not the right fit for a Japanese audience, and those were some really valuable lessons for us.
Photo: TRI
Toyota recently demonstrated a gantry robot that would hang from the ceiling to perform tasks like wiping surfaces and clearing clutter.
I love that TRI is exploring things like the gantry robot both in terms of near-term research and as part of its long-term vision, but is a robot like this actually worth pursuing? Or more generally, what’s the right way to compromise between making an environment robot friendly, and asking humans to make changes to their homes?
Max Bajracharya: We think a lot about the problems that we’re trying to address in a holistic way. We don’t want to just give people a robot, and assume that they’re not going to change anything about their lifestyle. We have a lot of evidence from people who use automated vacuum cleaners that people will adapt to the tools you give them, and they’ll change their lifestyle. So we want to think about what is that trade between changing the environment, and giving people robotic assistance and tools.
We certainly think that there are ways to make the gantry system plausible. The one you saw today is obviously a prototype and does require significant infrastructure. If we’re going to retrofit a home, that isn’t going to be the way to do it. But we still feel like we’re very much in the prototype phase, where we’re trying to understand whether this is worth it to be able to bypass navigation challenges, and coming up with the pros and cons of the gantry system. We’re evaluating whether we think this is the right approach to solving the problem.
To what extent do you think humans should be either directly or indirectly in the loop with home and service robots?
Bajracharya: Our goal is to amplify people, so achieving this is going to require robots to be in a loop with people in some form. One thing we have learned is that using people in a slow loop with robots, such as teaching them or helping them when they make mistakes, gives a robot an important advantage over one that has to do everything perfectly 100 percent of the time. In unstructured human environments, robots are going to encounter corner cases, and are going to need to learn to adapt. People will likely play an important role in helping the robots learn. 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