Tag Archives: machine
#436021 AI Faces Speed Bumps and Potholes on Its ...
Implementing machine learning in the real world isn’t easy. The tools are available and the road is well-marked—but the speed bumps are many.
That was the conclusion of panelists wrapping up a day of discussions at the IEEE AI Symposium 2019, held at Cisco’s San Jose, Calif., campus last week.
The toughest problem, says Ben Irving, senior manager of Cisco’s strategy innovations group, is people.
It’s tough to find data scientist expertise, he indicated, so companies are looking into non-traditional sources of personnel, like political science. “There are some untapped areas with a lot of untapped data science expertise,” Irving says.
Lazard’s artificial intelligence manager Trevor Mottl agreed that would-be data scientists don’t need formal training or experience to break into the field. “This field is changing really rapidly,” he says. “There are new language models coming out every month, and new tools, so [anyone should] expect to not know everything. Experiment, try out new tools and techniques, read, study, spend time; there aren’t any true experts at this point because the foundational elements are shifting so rapidly.”
“It is a wonderful time to get into a field,” he reasons, noting that it doesn’t take long to catch up because there aren’t 20 years of history.”
Confusion about what different kinds of machine learning specialists do doesn’t help the personnel situation. An audience member asked panelists to explain the difference between data scientist, data analyst, and data engineer. Darrin Johnson, Nvidia global director of technical marketing for enterprise, admitted it’s hard to sort out, and any two companies could define the positions differently. “Sometimes,” he says, particularly at smaller companies, “a data scientist plays all three roles. But as companies grow, there are different groups that ingest data, clean data, and use data. At some companies, training and inference are separate. It really depends, which is a challenge when you are trying to hire someone.”
Mitigating the risks of a hot job market
The competition to hire data scientists, analysts, engineers, or whatever companies call them requires that managers make sure any work being done is structured and comprehensible at all times, the panelists cautioned.
“We need to remember that our data scientists go home every day and sometimes they don’t come back because they go home and then go to a different company,” says Lazard’s Mottl. “That’s a fact of life. If you give people choice on [how they do development], and have a successful person who gets poached by competitor, you have to either hire a team to unwrap what that person built or jettison their work and rebuild it.”
By contrast, he says, “places that have structured coding and structured commits and organized constructions of software have done very well.”
But keeping all of a company’s engineers working with the same languages and on the same development paths is not easy to do in a field that moves as fast as machine learning. Zongjie Diao, Cisco director of product management for machine learning, quipped: “I have a data scientist friend who says the speed at which he changes girlfriends is less than speed at which he changes languages.”
The data scientist/IT manager clash
Once a company finds the data engineers and scientists they need and get them started on the task of applying machine learning to that company’s operations, one of the first obstacles they face just might be the company’s IT department, the panelists suggested.
“IT is process oriented,” Mottl says. The IT team “knows how to keep data secure, to set up servers. But when you bring in a data science team, they want sandboxes, they want freedom, they want to explore and play.”
Also, Nvidia’s Johnson pointed out, “There is a language barrier.” The AI world, he says, is very different from networking or storage, and data scientists find it hard to articulate their requirements to IT.
On the ground or in the cloud?
And then there is the decision of where exactly machine learning should happen—on site, or in the cloud? At Lazard, Mottl says, the deep learning engineers do their experimentation on premises; that’s their sandbox. “But when we deploy, we deploy in the cloud,” he says.
Nvidia, Johnson says, thinks the opposite approach is better. We see the cloud as “the sandbox,” he says. “So you can run as many experiments as possible, fail fast, and learn faster.”
For Cisco’s Irving, the “where” of machine learning depends on the confidentiality of the data.
Mottl, who says rolling machine learning technology into operation can hit resistance from all across the company, had one last word of caution for those aiming to implement AI:
Data scientists are building things that might change the ways other people in the organization work, like sales and even knowledge workers. [You need to] think about the internal stakeholders and prepare them, because the last thing you want to do is to create a valuable new thing that nobody likes and people take potshots against.
The AI Symposium was organized by the Silicon Valley chapters of the IEEE Young Professionals, the IEEE Consultants’ Network, and IEEE Women in Engineering and supported by Cisco. 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
#435822 The Internet Is Coming to the Rest of ...
People surf it. Spiders crawl it. Gophers navigate it.
Now, a leading group of cognitive biologists and computer scientists want to make the tools of the Internet accessible to the rest of the animal kingdom.
Dubbed the Interspecies Internet, the project aims to provide intelligent animals such as elephants, dolphins, magpies, and great apes with a means to communicate among each other and with people online.
And through artificial intelligence, virtual reality, and other digital technologies, researchers hope to crack the code of all the chirps, yips, growls, and whistles that underpin animal communication.
Oh, and musician Peter Gabriel is involved.
“We can use data analysis and technology tools to give non-humans a lot more choice and control,” the former Genesis frontman, dressed in his signature Nehru-style collar shirt and loose, open waistcoat, told IEEE Spectrum at the inaugural Interspecies Internet Workshop, held Monday in Cambridge, Mass. “This will be integral to changing our relationship with the natural world.”
The workshop was a long time in the making.
Eighteen years ago, Gabriel visited a primate research center in Atlanta, Georgia, where he jammed with two bonobos, a male named Kanzi and his half-sister Panbanisha. It was the first time either bonobo had sat at a piano before, and both displayed an exquisite sense of musical timing and melody.
Gabriel seemed to be speaking to the great apes through his synthesizer. It was a shock to the man who once sang “Shock the Monkey.”
“It blew me away,” he says.
Add in the bonobos’ ability to communicate by pointing to abstract symbols, Gabriel notes, and “you’d have to be deaf, dumb, and very blind not to notice language being used.”
Gabriel eventually teamed up with Internet protocol co-inventor Vint Cerf, cognitive psychologist Diana Reiss, and IoT pioneer Neil Gershenfeld to propose building an Interspecies Internet. Presented in a 2013 TED Talk as an “idea in progress,” the concept proved to be ahead of the technology.
“It wasn’t ready,” says Gershenfeld, director of MIT’s Center for Bits and Atoms. “It needed to incubate.”
So, for the past six years, the architects of the Dolittlesque initiative embarked on two small pilot projects, one for dolphins and one for chimpanzees.
At her Hunter College lab in New York City, Reiss developed what she calls the D-Pad—a touchpad for dolphins.
Reiss had been trying for years to create an underwater touchscreen with which to probe the cognition and communication skills of bottlenose dolphins. But “it was a nightmare coming up with something that was dolphin-safe and would work,” she says.
Her first attempt emitted too much heat. A Wii-like system of gesture recognition proved too difficult to install in the dolphin tanks.
Eventually, she joined forces with Rockefeller University biophysicist Marcelo Magnasco and invented an optical detection system in which images and infrared sensors are projected through an underwater viewing window onto a glass panel, allowing the dolphins to play specially designed apps, including one dubbed Whack-a-Fish.
Meanwhile, in the United Kingdom, Gabriel worked with Alison Cronin, director of the ape rescue center Monkey World, to test the feasibility of using FaceTime with chimpanzees.
The chimps engaged with the technology, Cronin reported at this week’s workshop. However, our hominid cousins proved as adept at videotelephonic discourse as my three-year-old son is at video chatting with his grandparents—which is to say, there was a lot of pass-the-banana-through-the-screen and other silly games, and not much meaningful conversation.
“We can use data analysis and technology tools to give non-humans a lot more choice and control.”
—Peter Gabriel
The buggy, rudimentary attempt at interspecies online communication—what Cronin calls her “Max Headroom experiment”—shows that building the Interspecies Internet will not be as simple as giving out Skype-enabled tablets to smart animals.
“There are all sorts of problems with creating a human-centered experience for another animal,” says Gabriel Miller, director of research and development at the San Diego Zoo.
Miller has been working on animal-focused sensory tools such as an “Elephone” (for elephants) and a “Joybranch” (for birds), but it’s not easy to design efficient interactive systems for other creatures—and for the Interspecies Internet to be successful, Miller points out, “that will be super-foundational.”
Researchers are making progress on natural language processing of animal tongues. Through a non-profit organization called the Earth Species Project, former Firefox designer Aza Raskin and early Twitter engineer Britt Selvitelle are applying deep learning algorithms developed for unsupervised machine translation of human languages to fashion a Rosetta Stone–like tool capable of interpreting the vocalizations of whales, primates, and other animals.
Inspired by the scientists who first documented the complex sonic arrangements of humpback whales in the 1960s—a discovery that ushered in the modern marine conservation movement—Selvitelle hopes that an AI-powered animal translator can have a similar effect on environmentalism today.
“A lot of shifts happen when someone who doesn’t have a voice gains a voice,” he says.
A challenge with this sort of AI software remains verification and validation. Normally, machine-learning algorithms are benchmarked against a human expert, but who is to say if a cybernetic translation of a sperm whale’s clicks is accurate or not?
One could back-translate an English expression into sperm whale-ese and then into English again. But with the great apes, there might be a better option.
According to primatologist Sue Savage-Rumbaugh, expertly trained bonobos could serve as bilingual interpreters, translating the argot of apes into the parlance of people, and vice versa.
Not just any trained ape will do, though. They have to grow up in a mixed Pan/Homo environment, as Kanzi and Panbanisha were.
“If I can have a chat with a cow, maybe I can have more compassion for it.”
—Jeremy Coller
Those bonobos were raised effectively from birth both by Savage-Rumbaugh, who taught the animals to understand spoken English and to communicate via hundreds of different pictographic “lexigrams,” and a bonobo mother named Matata that had lived for six years in the Congolese rainforests before her capture.
Unlike all other research primates—which are brought into captivity as infants, reared by human caretakers, and have limited exposure to their natural cultures or languages—those apes thus grew up fluent in both bonobo and human.
Panbanisha died in 2012, but Kanzi, aged 38, is still going strong, living at an ape sanctuary in Des Moines, Iowa. Researchers continue to study his cognitive abilities—Francine Dolins, a primatologist at the University of Michigan-Dearborn, is running one study in which Kanzi and other apes hunt rabbits and forage for fruit through avatars on a touchscreen. Kanzi could, in theory, be recruited to check the accuracy of any Google Translate–like app for bonobo hoots, barks, grunts, and cries.
Alternatively, Kanzi could simply provide Internet-based interpreting services for our two species. He’s already proficient at video chatting with humans, notes Emily Walco, a PhD student at Harvard University who has personally Skyped with Kanzi. “He was super into it,” Walco says.
And if wild bonobos in Central Africa can be coaxed to gather around a computer screen, Savage-Rumbaugh is confident Kanzi could communicate with them that way. “It can all be put together,” she says. “We can have an Interspecies Internet.”
“Both the technology and the knowledge had to advance,” Savage-Rumbaugh notes. However, now, “the techniques that we learned could really be extended to a cow or a pig.”
That’s music to the ears of Jeremy Coller, a private equity specialist whose foundation partially funded the Interspecies Internet Workshop. Coller is passionate about animal welfare and has devoted much of his philanthropic efforts toward the goal of ending factory farming.
At the workshop, his foundation announced the creation of the Coller Doolittle Prize, a US $100,000 award to help fund further research related to the Interspecies Internet. (A working group also formed to synthesize plans for the emerging field, to facilitate future event planning, and to guide testing of shared technology platforms.)
Why would a multi-millionaire with no background in digital communication systems or cognitive psychology research want to back the initiative? For Coller, the motivation boils to interspecies empathy.
“If I can have a chat with a cow,” he says, “maybe I can have more compassion for it.”
An abridged version of this post appears in the September 2019 print issue as “Elephants, Dolphins, and Chimps Need the Internet, Too.” Continue reading
Posted in Human Robots
#435775 Jaco Is a Low-Power Robot Arm That Hooks ...
We usually think of robots as taking the place of humans in various tasks, but robots of all kinds can also enhance human capabilities. This may be especially true for people with disabilities. And while the Cybathlon competition showed what's possible when cutting-edge research robotics is paired with expert humans, that competition isn't necessarily reflective of the kind of robotics available to most people today.
Kinova Robotics's Jaco arm is an assistive robotic arm designed to be mounted on an electric wheelchair. With six degrees of freedom plus a three-fingered gripper, the lightweight carbon fiber arm is frequently used in research because it's rugged and versatile. But from the start, Kinova created it to add autonomy to the lives of people with mobility constraints.
Earlier this year, Kinova shared the story of Mary Nelson, an 11-year-old girl with spinal muscular atrophy, who uses her Jaco arm to show her horse in competition. Spinal muscular atrophy is a neuromuscular disorder that impairs voluntary muscle movement, including muscles that help with respiration, and Mary depends on a power chair for mobility.
We wanted to learn more about how Kinova designs its Jaco arm, and what that means for folks like Mary, so we spoke with both Kinova and Mary's parents to find out how much of a difference a robot arm can make.
IEEE Spectrum: How did Mary interact with the world before having her arm, and what was involved in the decision to try a robot arm in general? And why then Kinova's arm specifically?
Ryan Nelson: Mary interacts with the world much like you and I do, she just uses different tools to do so. For example, she is 100 percent independent using her computer, iPad, and phone, and she prefers to use a mouse. However, she cannot move a standard mouse, so she connects her wheelchair to each device with Bluetooth to move the mouse pointer/cursor using her wheelchair joystick.
For years, we had a Manfrotto magic arm and super clamp attached to her wheelchair and she used that much like the robotic arm. We could put a baseball bat, paint brush, toys, etc. in the super clamp so that Mary could hold the object and interact as physically able children do. Mary has always wanted to be more independent, so we knew the robotic arm was something she must try. We had seen videos of the Kinova arm on YouTube and on their website, so we reached out to them to get a trial.
Can you tell us about the Jaco arm, and how the process of designing an assistive robot arm is different from the process of designing a conventional robot arm?
Nathaniel Swenson, Director of U.S. Operations — Assistive Technologies at Kinova: Jaco is our flagship robotic arm. Inspired by our CEO's uncle and its namesake, Jacques “Jaco” Forest, it was designed as assistive technology with power wheelchair users in mind.
The primary differences between Jaco and our other robots, such as the new Gen3, which was designed to meet the needs of academic and industry research teams, are speed and power consumption. Other robots such as the Gen3 can move faster and draw slightly more power because they aren't limited by the battery size of power wheelchairs. Depending on the use case, they might not interact directly with a human being in the research setting and can safely move more quickly. Jaco is designed to move at safe speeds and make direct contact with the end user and draw very little power directly from their wheelchair.
The most important consideration in the design process of an assistive robot is the safety of the end user. Jaco users operate their robots through their existing drive controls to assist them in daily activities such as eating, drinking, and opening doors and they don't have to worry about the robot draining their chair's batteries throughout the day. The elegant design that results from meeting the needs of our power chair users has benefited subsequent iterations, [of products] such as the Gen3, as well: Kinova's robots are lightweight, extremely efficient in their power consumption, and safe for direct human-robot interaction. This is not true of conventional industrial robots.
What was the learning process like for Mary? Does she feel like she's mastered the arm, or is it a continuous learning process?
Ryan Nelson: The learning process was super quick for Mary. However, she amazes us every day with the new things that she can do with the arm. Literally within minutes of installing the arm on her chair, Mary had it figured out and was shaking hands with the Kinova rep. The control of the arm is super intuitive and the Kinova reps say that SMA (Spinal Muscular Atrophy) children are perfect users because they are so smart—they pick it up right away. Mary has learned to do many fine motor tasks with the arm, from picking up small objects like a pencil or a ruler, to adjusting her glasses on her face, to doing science experiments.
Photo: The Nelson Family
Mary uses a headset microphone to amplify her voice, and she will use the arm and finger to adjust the microphone in front of her mouth after she is done eating (also a task she mastered quickly with the arm). Additionally, Mary will use the arms to reach down and adjust her feet or leg by grabbing them with the arm and moving them to a more comfortable position. All of these examples are things she never really asked us to do, but something she needed and just did on her own, with the help of the arm.
What is the most common feedback that you get from new users of the arm? How about from experienced users who have been using the arm for a while?
Nathaniel Swenson: New users always tell us how excited they are to see what they can accomplish with their new Jaco. From day one, they are able to do things that they have longed to do without assistance from a caregiver: take a drink of water or coffee, scratch an itch, push the button to open an “accessible” door or elevator, or even feed their baby with a bottle.
The most common feedback I hear from experienced users is that Jaco has changed their life. Our experienced users like Mary are rock stars: everywhere they go, people get excited to see what they'll do next. The difference between a new user and an experienced user could be as little as two weeks. People who operate power wheelchairs every day are already expert drivers and we just add a new “gear” to their chair: robot mode. It's fun to see how quickly new users master the intuitive Jaco control modes.
What changes would you like to see in the next generation of Jaco arm?
Ryan Nelson: Titanium fingers! Make it lift heavier objects, hold heavier items like a baseball bat, machine gun, flame thrower, etc., and Mary literally said this last night: “I wish the arm moved fast enough to play the piano.”
Nathaniel Swenson: I love the idea of titanium fingers! Jaco's fingers are made from a flexible polymer and designed to avoid harm. This allows the fingers to bend or dislocate, rather than break, but it also means they are not as durable as a material like titanium. Increased payload, the ability to manipulate heavier objects, requires increased power consumption. We've struck a careful balance between providing enough strength to accomplish most medically necessary Activities of Daily Living and efficient use of the power chair's batteries.
We take Isaac Asimov's Laws of Robotics pretty seriously. When we start to combine machine guns, flame throwers, and artificial intelligence with robots, I get very nervous!
I wish the arm moved fast enough to play the piano, too! I am also a musician and I share Mary's dream of an assistive robot that would enable her to make music. In the meantime, while we work on that, please enjoy this beautiful violin piece by Manami Ito and her one-of-a-kind violin prosthesis:
To what extent could more autonomy for the arm be helpful for users? What would be involved in implementing that?
Nathaniel Swenson: Artificial intelligence, machine learning, and deep learning will introduce greater autonomy in future iterations of assistive robots. This will enable them to perform more complex tasks that aren't currently possible, and enable them to accomplish routine tasks more quickly and with less input than the current manual control requires.
For assistive robots, implementation of greater autonomy involves a focus on end-user safety and improvements in the robot's awareness of its environment. Autonomous robots that work in close proximity with humans need vision. They must be able to see to avoid collisions and they use haptic feedback to tell the robot how much force is being exerted on objects. All of these technologies exist, but the largest obstacle to bringing them to the assistive technology market is to prove to the health insurance companies who will fund them that they are both safe and medically necessary. Continue reading
Posted in Human Robots