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#436200 AI and the Future of Work: The Economic ...
This week at MIT, academics and industry officials compared notes, studies, and predictions about AI and the future of work. During the discussions, an insurance company executive shared details about one AI program that rolled out at his firm earlier this year. A chatbot the company introduced, the executive said, now handles 150,000 calls per month.
Later in the day, a panelist—David Fanning, founder of PBS’s Frontline—remarked that this statistic is emblematic of broader fears he saw when reporting a new Frontline documentary about AI. “People are scared,” Fanning said of the public’s AI anxiety.
Fanning was part of a daylong symposium about AI’s economic consequences—good, bad, and otherwise—convened by MIT’s Task Force on the Work of the Future.
“Dig into every industry, and you’ll find AI changing the nature of work,” said Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). She cited recent McKinsey research that found 45 percent of the work people are paid to do today can be automated with currently available technologies. Those activities, McKinsey found, represent some US $2 trillion in wages.
However, the threat of automation—whether by AI or other technologies—isn’t as new as technologists on America’s coasts seem to believe, said panelist Fred Goff, CEO of Jobcase, Inc.
“If you live in Detroit or Toledo, where I come from, technology has been displacing jobs for the last half-century,” Goff said. “I don’t think that most people in this country have the increased anxiety that the coasts do, because they’ve been living this.”
Goff added that the challenge AI poses for the workforce is not, as he put it, “getting coal miners to code.” Rather, he said, as AI automates some jobs, it will also open opportunities for “reskilling” that may have nothing to do with AI or automation. He touted trade schools—teaching skills like welding, plumbing, and electrical work—and certification programs for sales industry software packages like Salesforce.
On the other hand, a documentarian who reported another recent program on AI—Krishna Andavolu, senior correspondent for Vice Media—said “reskilling” may not be an easy answer.
“People in rooms like this … don’t realize that a lot of people don’t want to work that much,” Andavolu said. “They’re not driven by passion for their career, they’re driven by passion for life. We’re telling a lot of these workers that they need to reskill. But to a lot of people that sounds like, ‘I’ve got to work twice as hard for what I have now.’ That sounds scary. We underestimate that at our peril.”
Part of the problem with “reskilling,” Andavolu said, is that some high-growth industries involve caregiving for seniors and in medical facilities—roles which are traditionally considered “feminized” careers. Destigmatizing these jobs, and increasing the pay to match the salaries of displaced jobs like long-haul truck drivers, is another challenge.
Daron Acemoglu, MIT Institute Professor of Economics, faulted the comparably slim funding of academic research into AI.
“There is nothing preordained about the progress of technology,” he said. Computers, the Internet, antibiotics, and sensors all grew out of government and academic research programs. What he called the “blue-sky thinking” of non-corporate AI research can also develop applications that are not purely focused on maximizing profits.
American companies, Acemoglu said, get tax breaks for capital R&D—but not for developing new technologies for their employees. “We turn around and [tell companies], ‘Use your technologies to empower workers,’” he said. “But why should they do that? Hiring workers is expensive in many ways. And we’re subsidizing capital.”
Said Sarita Gupta, director of the Ford Foundation’s Future of Work(ers) Program, “Low and middle income workers have for over 30 years been experiencing stagnant and declining pay, shrinking benefits, and less power on the job. Now technology is brilliant at enabling scale. But the question we sit with is—how do we make sure that we’re not scaling these longstanding problems?”
Andrew McAfee, co-director of MIT’s Initiative on the Digital Economy, said AI may not reduce the number of jobs available in the workplace today. But the quality of those jobs is another story. He cited the Dutch economist Jan Tinbergen who decades ago said that “Inequality is a race between technology and education.”
McAfee said, ultimately, the time to solve the economic problems AI poses for workers in the United States is when the U.S. economy is doing well—like right now.
“We do have the wind at our backs,” said Elisabeth Reynolds, executive director of MIT’s Task Force on the Work of the Future.
“We have some breathing room right now,” McAfee agreed. “Economic growth has been pretty good. Unemployment is pretty low. Interest rates are very, very low. We might not have that war chest in the future.” Continue reading
Posted in Human Robots
#436176 We’re Making Progress in Explainable ...
Machine learning algorithms are starting to exceed human performance in many narrow and specific domains, such as image recognition and certain types of medical diagnoses. They’re also rapidly improving in more complex domains such as generating eerily human-like text. We increasingly rely on machine learning algorithms to make decisions on a wide range of topics, from what we collectively spend billions of hours watching to who gets the job.
But machine learning algorithms cannot explain the decisions they make.
How can we justify putting these systems in charge of decisions that affect people’s lives if we don’t understand how they’re arriving at those decisions?
This desire to get more than raw numbers from machine learning algorithms has led to a renewed focus on explainable AI: algorithms that can make a decision or take an action, and tell you the reasons behind it.
What Makes You Say That?
In some circumstances, you can see a road to explainable AI already. Take OpenAI’s GTP-2 model, or IBM’s Project Debater. Both of these generate text based on a large corpus of training data, and try to make it as relevant as possible to the prompt that’s given. If these models were also able to provide a quick run-down of the top few sources in that corpus of training data they were drawing information from, it may be easier to understand where the “argument” (or poetic essay about unicorns) was coming from.
This is similar to the approach Google is now looking at for its image classifiers. Many algorithms are more sensitive to textures and the relationship between adjacent pixels in an image, rather than recognizing objects by their outlines as humans do. This leads to strange results: some algorithms can happily identify a totally scrambled image of a polar bear, but not a polar bear silhouette.
Previous attempts to make image classifiers explainable relied on significance mapping. In this method, the algorithm would highlight the areas of the image that contributed the most statistical weight to making the decision. This is usually determined by changing groups of pixels in the image and seeing which contribute to the biggest change in the algorithm’s impression of what the image is. For example, if the algorithm is trying to recognize a stop sign, changing the background is unlikely to be as important as changing the sign.
Google’s new approach changes the way that its algorithm recognizes objects, by examining them at several different resolutions and searching for matches to different “sub-objects” within the main object. You or I might recognize an ambulance from its flashing lights, its tires, and its logo; we might zoom in on the basketball held by an NBA player to deduce their occupation, and so on. By linking the overall categorization of an image to these “concepts,” the algorithm can explain its decision: I categorized this as a cat because of its tail and whiskers.
Even in this experiment, though, the “psychology” of the algorithm in decision-making is counter-intuitive. For example, in the basketball case, the most important factor in making the decision was actually the player’s jerseys rather than the basketball.
Can You Explain What You Don’t Understand?
While it may seem trivial, the conflict here is a fundamental one in approaches to artificial intelligence. Namely, how far can you get with mere statistical associations between huge sets of data, and how much do you need to introduce abstract concepts for real intelligence to arise?
At one end of the spectrum, Good Old-Fashioned AI or GOFAI dreamed up machines that would be entirely based on symbolic logic. The machine would be hard-coded with the concept of a dog, a flower, cars, and so forth, alongside all of the symbolic “rules” which we internalize, allowing us to distinguish between dogs, flowers, and cars. (You can imagine a similar approach to a conversational AI would teach it words and strict grammatical structures from the top down, rather than “learning” languages from statistical associations between letters and words in training data, as GPT-2 broadly does.)
Such a system would be able to explain itself, because it would deal in high-level, human-understandable concepts. The equation is closer to: “ball” + “stitches” + “white” = “baseball”, rather than a set of millions of numbers linking various pathways together. There are elements of GOFAI in Google’s new approach to explaining its image recognition: the new algorithm can recognize objects based on the sub-objects they contain. To do this, it requires at least a rudimentary understanding of what those sub-objects look like, and the rules that link objects to sub-objects, such as “cats have whiskers.”
The issue, of course, is the—maybe impossible—labor-intensive task of defining all these symbolic concepts and every conceivable rule that could possibly link them together by hand. The difficulty of creating systems like this, which could handle the “combinatorial explosion” present in reality, helped to lead to the first AI winter.
Meanwhile, neural networks rely on training themselves on vast sets of data. Without the “labeling” of supervised learning, this process might bear no relation to any concepts a human could understand (and therefore be utterly inexplicable).
Somewhere between these two, hope explainable AI enthusiasts, is a happy medium that can crunch colossal amounts of data, giving us all of the benefits that recent, neural-network AI has bestowed, while showing its working in terms that humans can understand.
Image Credit: Image by Seanbatty from Pixabay Continue reading
Posted in Human Robots
#436165 Video Friday: DJI’s Mavic Mini Is ...
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!):
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.
DJI’s new Mavic Mini looks like a pretty great drone for US $400 ($500 for a combo with more accessories): It’s tiny, flies for 30 minutes, and will do what you need as far as pictures and video (although not a whole lot more).
DJI seems to have put a bunch of effort into making the drone 249 grams, 1 gram under what’s required for FAA registration. That means you save $5 and a few minutes of your time, but that does not mean you don’t have to follow the FAA’s rules and regulations governing drone use.
[ DJI ]
Don’t panic, but Clearpath and HEBI Robotics have armed the Jackal:
After locking eyes across a crowded room at ICRA 2019, Clearpath Robotics and HEBI Robotics basked in that warm and fuzzy feeling that comes with starting a new and exciting relationship. Over a conference hall coffee, they learned that the two companies have many overlapping interests. The most compelling was the realization that customers across a variety of industries are hunting for an elusive true love of their own – a robust but compact robotic platform combined with a long reach manipulator for remote inspection tasks.
After ICRA concluded, Arron Griffiths, Application Engineer at Clearpath, and Matthew Tesch, Software Engineer at HEBI, kept in touch and decided there had been enough magic in the air to warrant further exploration. A couple of months later, Matthew arrived at Clearpath to formally introduce the HEBI’s X-Series Arm to Clearpath’s Jackal UGV. It was love.
[ Clearpath ]
Thanks Dave!
I’m really not a fan of the people-carrying drones, but heavy lift cargo drones seem like a more okay idea.
Volocopter, the pioneer in Urban Air Mobility, presented the demonstrator of its VoloDrone. This marks Volocopters expansion into the logistics, agriculture, infrastructure and public services industry. The VoloDrone is an unmanned, fully electric, heavy-lift utility drone capable of carrying a payload of 200 kg (440 lbs) up to 40 km (25 miles). With a standardized payload attachment, VoloDrone can serve a great variety of purposes from transporting boxes, to liquids, to equipment and beyond. It can be remotely piloted or flown in automated mode on pre-set routes.
[ Volocopter ]
JAY is a mobile service robot that projects a display on the floor and plays sound with its speaker. By playing sounds and videos, it provides visual and audio entertainment in various places such as exhibition halls, airports, hotels, department stores and more.
[ Rainbow Robotics ]
The DARPA Subterranean Challenge Virtual Tunnel Circuit concluded this week—it was the same idea as the physical challenge that took place in August, just with a lot less IRL dirt.
The awards ceremony and team presentations are in this next video, and we’ll have more on this once we get back from IROS.
[ DARPA SubT ]
NASA is sending a mobile robot to the south pole of the Moon to get a close-up view of the location and concentration of water ice in the region and for the first time ever, actually sample the water ice at the same pole where the first woman and next man will land in 2024 under the Artemis program.
About the size of a golf cart, the Volatiles Investigating Polar Exploration Rover, or VIPER, will roam several miles, using its four science instruments — including a 1-meter drill — to sample various soil environments. Planned for delivery in December 2022, VIPER will collect about 100 days of data that will be used to inform development of the first global water resource maps of the Moon.
[ NASA ]
Happy Halloween from HEBI Robotics!
[ HEBI ]
Happy Halloween from Soft Robotics!
[ Soft Robotics ]
Halloween must be really, really confusing for autonomous cars.
[ Waymo ]
Once a year at Halloween, hardworking JPL engineers put their skills to the test in a highly competitive pumpkin carving contest. The result: A pumpkin gently landed on the Moon, its retrorockets smoldering, while across the room a Nemo-inspired pumpkin explored the sub-surface ocean of Jupiter moon Europa. Suffice to say that when the scientists and engineers at NASA’s Jet Propulsion Laboratory compete in a pumpkin-carving contest, the solar system’s the limit. Take a look at some of the masterpieces from 2019.
Now in its ninth year, the contest gives teams only one hour to carve and decorate their pumpkin though they can prepare non-pumpkin materials – like backgrounds, sound effects and motorized parts – ahead of time.
[ JPL ]
The online autonomous navigation and semantic mapping experiment presented [below] is conducted with the Cassie Blue bipedal robot at the University of Michigan. The sensors attached to the robot include an IMU, a 32-beam LiDAR and an RGB-D camera. The whole online process runs in real-time on a Jetson Xavier and a laptop with an i7 processor.
[ BPL ]
Misty II is now available to anyone who wants one, and she’s on sale for a mere $2900.
[ Misty ]
We leveraged LIDAR-based slam, in conjunction with our specialized relative localization sensor UVDAR to perform a de-centralized, communication-free swarm flight without the units knowing their absolute locations. The swarming and obstacle avoidance control is based on a modified Boids-like algorithm, while the whole swarm is controlled by directing a selected leader unit.
[ MRS ]
The MallARD robot is an autonomous surface vehicle (ASV), designed for the monitoring and inspection of wet storage facilities for example spent fuel pools or wet silos. The MallARD is holonomic, uses a LiDAR for localisation and features a robust trajectory tracking controller.
The University of Manchester’s researcher Dr Keir Groves designed and built the autonomous surface vehicle (ASV) for the challenge which came in the top three of the second round in Nov 2017. The MallARD went on to compete in a final 3rd round where it was deployed in a spent fuel pond at a nuclear power plant in Finland by the IAEA, along with two other entries. The MallARD came second overall, in November 2018.
[ RNE ]
Thanks Jennifer!
I sometimes get the sense that in the robotic grasping and manipulation world, suction cups are kinda seen as cheating at times. But, their nature allows you to do some pretty interesting things.
More clever octopus footage please.
[ CMU ]
A Personal, At-Home Teacher For Playful Learning: From academic topics to child-friendly news bulletins, fun facts and more, Miko 2 is packed with relevant and freshly updated content specially designed by educationists and child-specialists. Your little one won’t even realize they’re learning.
As we point out pretty much every time we post a video like this, keep in mind that you’re seeing a heavily edited version of a hypothetical best case scenario for how this robot can function. And things like “creating a relationship that they can then learn how to form with their peers” is almost certainly overselling things. But at $300 (shipping included), this may be a decent robot as long as your expectations are appropriately calibrated.
[ Miko ]
ICRA 2018 plenary talk by Rodney Brooks: “Robots and People: the Research Challenge.”
[ IEEE RAS ]
ICRA-X 2018 talk by Ron Arkin: “Lethal Autonomous Robots and the Plight of the Noncombatant.”
[ IEEE RAS ]
On the most recent episode of the AI Podcast, Lex Fridman interviews Garry Kasparov.
[ AI Podcast ] Continue reading
Posted in Human Robots
#436155 This MIT Robot Wants to Use Your ...
MIT researchers have demonstrated a new kind of teleoperation system that allows a two-legged robot to “borrow” a human operator’s physical skills to move with greater agility. The system works a bit like those haptic suits from the Spielberg movie “Ready Player One.” But while the suits in the film were used to connect humans to their VR avatars, the MIT suit connects the operator to a real robot.
The robot is called Little HERMES, and it’s currently just a pair of little legs, about a third the size of an average adult. It can step and jump in place or walk a short distance while supported by a gantry. While that in itself is not very impressive, the researchers say their approach could help bring capable disaster robots closer to reality. They explain that, despite recent advances, building fully autonomous robots with motor and decision-making skills comparable to those of humans remains a challenge. That’s where a more advanced teleoperation system could help.
The researchers, João Ramos, now an assistant professor at the University of Illinois at Urbana-Champaign, and Sangbae Kim, director of MIT’s Biomimetic Robotics Lab, describe the project in this week’s issue of Science Robotics. In the paper, they argue that existing teleoperation systems often can’t effectively match the operator’s motions to that of a robot. In addition, conventional systems provide no physical feedback to the human teleoperator about what the robot is doing. Their new approach addresses these two limitations, and to see how it would work in practice, they built Little HERMES.
Image: Science Robotics
The main components of MIT’s bipedal robot Little HERMES: (A) Custom actuators designed to withstand impact and capable of producing high torque. (B) Lightweight limbs with low inertia and fast leg swing. (C) Impact-robust and lightweight foot sensors with three-axis contact force sensor. (D) Ruggedized IMU to estimates the robot’s torso posture, angular rate, and linear acceleration. (E) Real-time computer sbRIO 9606 from National Instruments for robot control. (F) Two three-cell lithium-polymer batteries in series. (G) Rigid and lightweight frame to minimize the robot mass.
Early this year, the MIT researchers wrote an in-depth article for IEEE Spectrum about the project, which includes Little HERMES and also its big brother, HERMES (for Highly Efficient Robotic Mechanisms and Electromechanical System). In that article, they describe the two main components of the system:
[…] We are building a telerobotic system that has two parts: a humanoid capable of nimble, dynamic behaviors, and a new kind of two-way human-machine interface that sends your motions to the robot and the robot’s motions to you. So if the robot steps on debris and starts to lose its balance, the operator feels the same instability and instinctively reacts to avoid falling. We then capture that physical response and send it back to the robot, which helps it avoid falling, too. Through this human-robot link, the robot can harness the operator’s innate motor skills and split-second reflexes to keep its footing.
You could say we’re putting a human brain inside the machine.
Image: Science Robotics
The human-machine interface built by the MIT researchers for controlling Little HERMES is different from conventional ones in that it relies on the operator’s reflexes to improve the robot’s stability. The researchers call it the balance-feedback interface, or BFI. The main modules of the BFI include: (A) Custom interface attachments for torso and feet designed to capture human motion data at high speed (1 kHz). (B) Two underactuated modules to track the position and orientation of the torso and apply forces to the operator. (C) Each actuation module has three DoFs, one of which is a push/pull rod actuated by a DC brushless motor. (D) A series of linkages with passive joints connected to the operator’s feet and track their spatial translation. (E) Real-time controller cRIO 9082 from National Instruments to close the BFI control loop. (F) Force plate to estimated the operator’s center of pressure position and measure the shear and normal components of the operator’s net contact force.
Here’s more footage of the experiments, showing Little HERMES stepping and jumping in place, walking a few steps forward and backward, and balancing. Watch until the end to see a compilation of unsuccessful stepping experiments. Poor Little HERMES!
In the new Science Robotics paper, the MIT researchers explain how they solved one of the key challenges in making their teleoperation system effective:
The challenge of this strategy lies in properly mapping human body motion to the machine while simultaneously informing the operator how closely the robot is reproducing the movement. Therefore, we propose a solution for this bilateral feedback policy to control a bipedal robot to take steps, jump, and walk in synchrony with a human operator. Such dynamic synchronization was achieved by (i) scaling the core components of human locomotion data to robot proportions in real time and (ii) applying feedback forces to the operator that are proportional to the relative velocity between human and robot.
Little HERMES is now taking its first steps, quite literally, but the researchers say they hope to use robotic legs with similar design as part of a more advanced humanoid. One possibility they’ve envisioned is a fast-moving quadruped robot that could run through various kinds of terrain and then transform into a bipedal robot that would use its hands to perform dexterous manipulations. This could involve merging some of the robots the MIT researchers have built in their lab, possibly creating hybrids between Cheetah and HERMES, or Mini Cheetah and Little HERMES. We can’t wait to see what the resulting robots will look like.
[ Science Robotics ] Continue reading
Posted in Human Robots