Tag Archives: reporting
#438014 Meet Blueswarm, a Smart School of ...
Anyone who’s seen an undersea nature documentary has marveled at the complex choreography that schooling fish display, a darting, synchronized ballet with a cast of thousands.
Those instinctive movements have inspired researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and the Wyss Institute for Biologically Inspired Engineering. The results could improve the performance and dependability of not just underwater robots, but other vehicles that require decentralized locomotion and organization, such as self-driving cars and robotic space exploration.
The fish collective called Blueswarm was created by a team led by Radhika Nagpal, whose lab is a pioneer in self-organizing systems. The oddly adorable robots can sync their movements like biological fish, taking cues from their plastic-bodied neighbors with no external controls required. Nagpal told IEEE Spectrum that this marks a milestone, demonstrating complex 3D behaviors with implicit coordination in underwater robots.
“Insights from this research will help us develop future miniature underwater swarms that can perform environmental monitoring and search in visually-rich but fragile environments like coral reefs,” Nagpal said. “This research also paves a way to better understand fish schools, by synthetically recreating their behavior.”
The research is published in Science Robotics, with Florian Berlinger as first author. Berlinger said the “Bluedot” robots integrate a trio of blue LED lights, a lithium-polymer battery, a pair of cameras, a Raspberry Pi computer and four controllable fins within a 3D-printed hull. The fish-lens cameras detect LED’s of their fellow swimmers, and apply a custom algorithm to calculate distance, direction and heading.
Based on that simple production and detection of LED light, the team proved that Blueswarm could self-organize behaviors, including aggregation, dispersal and circle formation—basically, swimming in a clockwise synchronization. Researchers also simulated a successful search mission, an autonomous Finding Nemo. Using their dispersion algorithm, the robot school spread out until one could detect a red light in the tank. Its blue LEDs then flashed, triggering the aggregation algorithm to gather the school around it. Such a robot swarm might prove valuable in search-and-rescue missions at sea, covering miles of open water and reporting back to its mates.
“Each Bluebot implicitly reacts to its neighbors’ positions,” Berlinger said. The fish—RoboCod, perhaps?—also integrate a Wifi module to allow uploading new behaviors remotely. The lab’s previous efforts include a 1,000-strong army of “Kilobots,” and a robotic construction crew inspired by termites. Both projects operated in two-dimensional space. But a 3D environment like air or water posed a tougher challenge for sensing and movement.
In nature, Berlinger notes, there’s no scaly CEO to direct the school’s movements. Nor do fish communicate their intentions. Instead, so-called “implicit coordination” guides the school’s collective behavior, with individual members executing high-speed moves based on what they see their neighbors doing. That decentralized, autonomous organization has long fascinated scientists, including in robotics.
“In these situations, it really benefits you to have a highly autonomous robot swarm that is self-sufficient. By using implicit rules and 3D visual perception, we were able to create a system with a high degree of autonomy and flexibility underwater where things like GPS and WiFi are not accessible.”
Berlinger adds the research could one day translate to anything that requires decentralized robots, from self-driving cars and Amazon warehouse vehicles to exploration of faraway planets, where poor latency makes it impossible to transmit commands quickly. Today’s semi-autonomous cars face their own technical hurdles in reliably sensing and responding to their complex environments, including when foul weather obscures onboard sensors or road markers, or when they can’t fix position via GPS. An entire subset of autonomous-car research involves vehicle-to-vehicle (V2V) communications that could give cars a hive mind to guide individual or collective decisions— avoiding snarled traffic, driving safely in tight convoys, or taking group evasive action during a crash that’s beyond their sensory range.
“Once we have millions of cars on the road, there can’t be one computer orchestrating all the traffic, making decisions that work for all the cars,” Berlinger said.
The miniature robots could also work long hours in places that are inaccessible to humans and divers, or even large tethered robots. Nagpal said the synthetic swimmers could monitor and collect data on reefs or underwater infrastructure 24/7, and work into tiny places without disturbing fragile equipment or ecosystems.
“If we could be as good as fish in that environment, we could collect information and be non-invasive, in cluttered environments where everything is an obstacle,” Nagpal said. Continue reading
Posted in Human Robots
#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
#435769 The Ultimate Optimization Problem: How ...
Lucas Joppa thinks big. Even while gazing down into his cup of tea in his modest office on Microsoft’s campus in Redmond, Washington, he seems to see the entire planet bobbing in there like a spherical tea bag.
As Microsoft’s first chief environmental officer, Joppa came up with the company’s AI for Earth program, a five-year effort that’s spending US $50 million on AI-powered solutions to global environmental challenges.
The program is not just about specific deliverables, though. It’s also about mindset, Joppa told IEEE Spectrum in an interview in July. “It’s a plea for people to think about the Earth in the same way they think about the technologies they’re developing,” he says. “You start with an objective. So what’s our objective function for Earth?” (In computer science, an objective function describes the parameter or parameters you are trying to maximize or minimize for optimal results.)
Photo: Microsoft
Lucas Joppa
AI for Earth launched in December 2017, and Joppa’s team has since given grants to more than 400 organizations around the world. In addition to receiving funding, some grantees get help from Microsoft’s data scientists and access to the company’s computing resources.
In a wide-ranging interview about the program, Joppa described his vision of the “ultimate optimization problem”—figuring out which parts of the planet should be used for farming, cities, wilderness reserves, energy production, and so on.
Every square meter of land and water on Earth has an infinite number of possible utility functions. It’s the job of Homo sapiens to describe our overall objective for the Earth. Then it’s the job of computers to produce optimization results that are aligned with the human-defined objective.
I don’t think we’re close at all to being able to do this. I think we’re closer from a technology perspective—being able to run the model—than we are from a social perspective—being able to make decisions about what the objective should be. What do we want to do with the Earth’s surface?
Such questions are increasingly urgent, as climate change has already begun reshaping our planet and our societies. Global sea and air surface temperatures have already risen by an average of 1 degree Celsius above preindustrial levels, according to the Intergovernmental Panel on Climate Change.
Today, people all around the world participated in a “climate strike,” with young people leading the charge and demanding a global transition to renewable energy. On Monday, world leaders will gather in New York for the United Nations Climate Action Summit, where they’re expected to present plans to limit warming to 1.5 degrees Celsius.
Joppa says such summit discussions should aim for a truly holistic solution.
We talk about how to solve climate change. There’s a higher-order question for society: What climate do we want? What output from nature do we want and desire? If we could agree on those things, we could put systems in place for optimizing our environment accordingly. Instead we have this scattered approach, where we try for local optimization. But the sum of local optimizations is never a global optimization.
There’s increasing interest in using artificial intelligence to tackle global environmental problems. New sensing technologies enable scientists to collect unprecedented amounts of data about the planet and its denizens, and AI tools are becoming vital for interpreting all that data.
The 2018 report “Harnessing AI for the Earth,” produced by the World Economic Forum and the consulting company PwC, discusses ways that AI can be used to address six of the world’s most pressing environmental challenges (climate change, biodiversity, and healthy oceans, water security, clean air, and disaster resilience).
Many of the proposed applications involve better monitoring of human and natural systems, as well as modeling applications that would enable better predictions and more efficient use of natural resources.
Joppa says that AI for Earth is taking a two-pronged approach, funding efforts to collect and interpret vast amounts of data alongside efforts that use that data to help humans make better decisions. And that’s where the global optimization engine would really come in handy.
For any location on earth, you should be able to go and ask: What’s there, how much is there, and how is it changing? And more importantly: What should be there?
On land, the data is really only interesting for the first few hundred feet. Whereas in the ocean, the depth dimension is really important.
We need a planet with sensors, with roving agents, with remote sensing. Otherwise our decisions aren’t going to be any good.
AI for Earth isn’t going to create such an online portal within five years, Joppa stresses. But he hopes the projects that he’s funding will contribute to making such a portal possible—eventually.
We’re asking ourselves: What are the fundamental missing layers in the tech stack that would allow people to build a global optimization engine? Some of them are clear, some are still opaque to me.
By the end of five years, I’d like to have identified these missing layers, and have at least one example of each of the components.
Some of the projects that AI for Earth has funded seem to fit that desire. Examples include SilviaTerra, which used satellite imagery and AI to create a map of the 92 billion trees in forested areas across the United States. There’s also OceanMind, a non-profit that detects illegal fishing and helps marine authorities enforce compliance. Platforms like Wildbook and iNaturalist enable citizen scientists to upload pictures of animals and plants, aiding conservation efforts and research on biodiversity. And FarmBeats aims to enable data-driven agriculture with low-cost sensors, drones, and cloud services.
It’s not impossible to imagine putting such services together into an optimization engine that knows everything about the land, the water, and the creatures who live on planet Earth. Then we’ll just have to tell that engine what we want to do about it.
Editor’s note: This story is published in cooperation with more than 250 media organizations and independent journalists that have focused their coverage on climate change ahead of the UN Climate Action Summit. IEEE Spectrum’s participation in the Covering Climate Now partnership builds on our past reporting about this global issue. Continue reading
Posted in Human Robots