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#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
#435828 Video Friday: Boston Dynamics’ ...
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):
RoboBusiness 2019 – October 1-3, 2019 – Santa Clara, Calif., USA
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.
You’ve almost certainly seen the new Spot and Atlas videos from Boston Dynamics, if for no other reason than we posted about Spot’s commercial availability earlier this week. But what, are we supposed to NOT include them in Video Friday anyway? Psh! Here you go:
[ Boston Dynamics ]
Eight deadly-looking robots. One Giant Nut trophy. Tonight is the BattleBots season finale, airing on Discovery, 8 p.m. ET, or check your local channels.
[ BattleBots ]
Thanks Trey!
Speaking of battling robots… Having giant robots fight each other is one of those things that sounds really great in theory, but doesn’t work out so well in reality. And sadly, MegaBots is having to deal with reality, which means putting their giant fighting robot up on eBay.
As of Friday afternoon, the current bid is just over $100,000 with a week to go.
[ MegaBots ]
Michigan Engineering has figured out the secret formula to getting 150,000 views on YouTube: drone plus nail gun.
[ Michigan Engineering ]
Michael Burke from the University of Edinburgh writes:
We’ve been learning to scoop grapefruit segments using a PR2, by “feeling” the difference between peel and pulp. We use joint torque measurements to predict the probability that the knife is in the peel or pulp, and use this to apply feedback control to a nominal cutting trajectory learned from human demonstration, so that we remain in a position of maximum uncertainty about which medium we’re cutting. This means we slice along the boundary between the two mediums. It works pretty well!
[ Paper ] via [ Robust Autonomy and Decisions Group ]
Thanks Michael!
Hey look, it’s Jan with eight EMYS robot heads. Hi, Jan! Hi, EMYSes!
[ EMYS ]
We’re putting the KRAKEN Arm through its paces, demonstrating that it can unfold from an Express Rack locker on the International Space Station and access neighboring lockers in NASA’s FabLab system to enable transfer of materials and parts between manufacturing, inspection, and storage stations. The KRAKEN arm will be able to change between multiple ’end effector’ tools such as grippers and inspection sensors – those are in development so they’re not shown in this video.
[ Tethers Unlimited ]
UBTECH’s Alpha Mini Robot with Smart Robot’s “Maatje” software is offering healthcare service to children at Praktijk Intraverte Multidisciplinary Institution in Netherlands.
This institution is using Alpha Mini in counseling children’s behavior. Alpha Mini can move and talk to children and offers games and activities to stimulate and interact with them. Alpha Mini talks, helps and motivates children thereby becoming more flexible in society.
[ UBTECH ]
Some impressive work here from Anusha Nagabandi, Kurt Konoglie, Sergey Levine, Vikash Kumar at Google Brain, training a dexterous multi-fingered hand to do that thing with two balls that I’m really bad at.
Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also notoriously difficult to control: Performing in-hand object manipulation, executing finger gaits to move objects, and exhibiting precise fine motor skills such as writing, all require finely balancing contact forces, breaking and reestablishing contacts repeatedly, and maintaining control of unactuated objects. In this work, we demonstrate that our method of online planning with deep dynamics models (PDDM) addresses both of these limitations; we show that improvements in learned dynamics models, together with improvements in online model-predictive control, can indeed enable efficient and effective learning of flexible contact-rich dexterous manipulation skills — and that too, on a 24-DoF anthropomorphic hand in the real world, using just 2-4 hours of purely real-world data to learn to simultaneously coordinate multiple free-floating objects.
[ PDDM ]
Thanks Vikash!
CMU’s Ballbot has a deceptively light touch that’s ideal for leading people around.
A paper on this has been submitted to IROS 2019.
[ CMU ]
The Autonomous Robots Lab at the University of Nevada is sharing some of the work they’ve done on path planning and exploration for aerial robots during the DARPA SubT Challenge.
[ Autonomous Robots Lab ]
More proof that anything can be a drone if you staple some motors to it. Even 32 feet of styrofoam insulation.
[ YouTube ]
Whatever you think of military drones, we can all agree that they look cool.
[ Boeing ]
I appreciate the fact that iCub has eyelids, I really do, but sometimes, it ends up looking kinda sleepy in research videos.
[ EPFL LASA ]
Video shows autonomous flight of a lightweight aerial vehicle outdoors and indoors on the campus of Carnegie Mellon University. The vehicle is equipped with limited onboard sensing from a front-facing camera and a proximity sensor. The aerial autonomy is enabled by utilizing a 3D prior map built in Step 1.
[ CMU ]
The Stanford Space Robotics Facility allows researchers to test innovative guidance and navigation algorithms on a realistic frictionless, underactuated system.
[ Stanford ASL ]
In this video, Ian and CP discuss Misty’s many capabilities including robust locomotion, obstacle avoidance, 3D mapping/SLAM, face detection and recognition, sound localization, hardware extensibility, photo and video capture, and programmable personality. They also talk about some of the skills he’s built using these capabilities (and others) and how those skills can be expanded upon by you.
[ Misty Robotics ]
This week’s CMU RI Seminar comes from Aaron Parness at Caltech and NASA JPL, on “Robotic Grippers for Planetary Applications.”
The previous generation of NASA missions to the outer solar system discovered salt water oceans on Europa and Enceladus, each with more liquid water than Earth – compelling targets to look for extraterrestrial life. Closer to home, JAXA and NASA have imaged sky-light entrances to lava tube caves on the Moon more than 100 m in diameter and ESA has characterized the incredibly varied and complex terrain of Comet 67P. While JPL has successfully landed and operated four rovers on the surface of Mars using a 6-wheeled rocker-bogie architecture, future missions will require new mobility architectures for these extreme environments. Unfortunately, the highest value science targets often lie in the terrain that is hardest to access. This talk will explore robotic grippers that enable missions to these extreme terrains through their ability to grip a wide variety of surfaces (shapes, sizes, and geotechnical properties). To prepare for use in space where repair or replacement is not possible, we field-test these grippers and robots in analog extreme terrain on Earth. Many of these systems are enabled by advances in autonomy. The talk will present a rapid overview of my work and a detailed case study of an underactuated rock gripper for deflecting asteroids.
[ CMU ]
Rod Brooks gives some of the best robotics talks ever. He gave this one earlier this week at UC Berkeley, on “Steps Toward Super Intelligence and the Search for a New Path.”
[ UC Berkeley ] Continue reading
Posted in Human Robots
#435804 New AI Systems Are Here to Personalize ...
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
#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