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#438779 Meet Catfish Charlie, the CIA’s ...
Photo: CIA Museum
CIA roboticists designed Catfish Charlie to take water samples undetected. Why they wanted a spy fish for such a purpose remains classified.
In 1961, Tom Rogers of the Leo Burnett Agency created Charlie the Tuna, a jive-talking cartoon mascot and spokesfish for the StarKist brand. The popular ad campaign ran for several decades, and its catchphrase “Sorry, Charlie” quickly hooked itself in the American lexicon.
When the CIA’s Office of Advanced Technologies and Programs started conducting some fish-focused research in the 1990s, Charlie must have seemed like the perfect code name. Except that the CIA’s Charlie was a catfish. And it was a robot.
More precisely, Charlie was an unmanned underwater vehicle (UUV) designed to surreptitiously collect water samples. Its handler controlled the fish via a line-of-sight radio handset. Not much has been revealed about the fish’s construction except that its body contained a pressure hull, ballast system, and communications system, while its tail housed the propulsion. At 61 centimeters long, Charlie wouldn’t set any biggest-fish records. (Some species of catfish can grow to 2 meters.) Whether Charlie reeled in any useful intel is unknown, as details of its missions are still classified.
For exploring watery environments, nothing beats a robot
The CIA was far from alone in its pursuit of UUVs nor was it the first agency to do so. In the United States, such research began in earnest in the 1950s, with the U.S. Navy’s funding of technology for deep-sea rescue and salvage operations. Other projects looked at sea drones for surveillance and scientific data collection.
Aaron Marburg, a principal electrical and computer engineer who works on UUVs at the University of Washington’s Applied Physics Laboratory, notes that the world’s oceans are largely off-limits to crewed vessels. “The nature of the oceans is that we can only go there with robots,” he told me in a recent Zoom call. To explore those uncharted regions, he said, “we are forced to solve the technical problems and make the robots work.”
Image: Thomas Wells/Applied Physics Laboratory/University of Washington
An oil painting commemorates SPURV, a series of underwater research robots built by the University of Washington’s Applied Physics Lab. In nearly 400 deployments, no SPURVs were lost.
One of the earliest UUVs happens to sit in the hall outside Marburg’s office: the Self-Propelled Underwater Research Vehicle, or SPURV, developed at the applied physics lab beginning in the late ’50s. SPURV’s original purpose was to gather data on the physical properties of the sea, in particular temperature and sound velocity. Unlike Charlie, with its fishy exterior, SPURV had a utilitarian torpedo shape that was more in line with its mission. Just over 3 meters long, it could dive to 3,600 meters, had a top speed of 2.5 m/s, and operated for 5.5 hours on a battery pack. Data was recorded to magnetic tape and later transferred to a photosensitive paper strip recorder or other computer-compatible media and then plotted using an IBM 1130.
Over time, SPURV’s instrumentation grew more capable, and the scope of the project expanded. In one study, for example, SPURV carried a fluorometer to measure the dispersion of dye in the water, to support wake studies. The project was so successful that additional SPURVs were developed, eventually completing nearly 400 missions by the time it ended in 1979.
Working on underwater robots, Marburg says, means balancing technical risks and mission objectives against constraints on funding and other resources. Support for purely speculative research in this area is rare. The goal, then, is to build UUVs that are simple, effective, and reliable. “No one wants to write a report to their funders saying, ‘Sorry, the batteries died, and we lost our million-dollar robot fish in a current,’ ” Marburg says.
A robot fish called SoFi
Since SPURV, there have been many other unmanned underwater vehicles, of various shapes and sizes and for various missions, developed in the United States and elsewhere. UUVs and their autonomous cousins, AUVs, are now routinely used for scientific research, education, and surveillance.
At least a few of these robots have been fish-inspired. In the mid-1990s, for instance, engineers at MIT worked on a RoboTuna, also nicknamed Charlie. Modeled loosely on a blue-fin tuna, it had a propulsion system that mimicked the tail fin of a real fish. This was a big departure from the screws or propellers used on UUVs like SPURV. But this Charlie never swam on its own; it was always tethered to a bank of instruments. The MIT group’s next effort, a RoboPike called Wanda, overcame this limitation and swam freely, but never learned to avoid running into the sides of its tank.
Fast-forward 25 years, and a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) unveiled SoFi, a decidedly more fishy robot designed to swim next to real fish without disturbing them. Controlled by a retrofitted Super Nintendo handset, SoFi could dive more than 15 meters, control its own buoyancy, and swim around for up to 40 minutes between battery charges. Noting that SoFi’s creators tested their robot fish in the gorgeous waters off Fiji, IEEE Spectrum’s Evan Ackerman noted, “Part of me is convinced that roboticists take on projects like these…because it’s a great way to justify a trip somewhere exotic.”
SoFi, Wanda, and both Charlies are all examples of biomimetics, a term coined in 1974 to describe the study of biological mechanisms, processes, structures, and substances. Biomimetics looks to nature to inspire design.
Sometimes, the resulting technology proves to be more efficient than its natural counterpart, as Richard James Clapham discovered while researching robotic fish for his Ph.D. at the University of Essex, in England. Under the supervision of robotics expert Huosheng Hu, Clapham studied the swimming motion of Cyprinus carpio, the common carp. He then developed four robots that incorporated carplike swimming, the most capable of which was iSplash-II. When tested under ideal conditions—that is, a tank 5 meters long, 2 meters wide, and 1.5 meters deep—iSpash-II obtained a maximum velocity of 11.6 body lengths per second (or about 3.7 m/s). That’s faster than a real carp, which averages a top velocity of 10 body lengths per second. But iSplash-II fell short of the peak performance of a fish darting quickly to avoid a predator.
Of course, swimming in a test pool or placid lake is one thing; surviving the rough and tumble of a breaking wave is another matter. The latter is something that roboticist Kathryn Daltorio has explored in depth.
Daltorio, an assistant professor at Case Western Reserve University and codirector of the Center for Biologically Inspired Robotics Research there, has studied the movements of cockroaches, earthworms, and crabs for clues on how to build better robots. After watching a crab navigate from the sandy beach to shallow water without being thrown off course by a wave, she was inspired to create an amphibious robot with tapered, curved feet that could dig into the sand. This design allowed her robot to withstand forces up to 138 percent of its body weight.
Photo: Nicole Graf
This robotic crab created by Case Western’s Kathryn Daltorio imitates how real crabs grab the sand to avoid being toppled by waves.
In her designs, Daltorio is following architect Louis Sullivan’s famous maxim: Form follows function. She isn’t trying to imitate the aesthetics of nature—her robot bears only a passing resemblance to a crab—but rather the best functionality. She looks at how animals interact with their environments and steals evolution’s best ideas.
And yet, Daltorio admits, there is also a place for realistic-looking robotic fish, because they can capture the imagination and spark interest in robotics as well as nature. And unlike a hyperrealistic humanoid, a robotic fish is unlikely to fall into the creepiness of the uncanny valley.
In writing this column, I was delighted to come across plenty of recent examples of such robotic fish. Ryomei Engineering, a subsidiary of Mitsubishi Heavy Industries, has developed several: a robo-coelacanth, a robotic gold koi, and a robotic carp. The coelacanth was designed as an educational tool for aquariums, to present a lifelike specimen of a rarely seen fish that is often only known by its fossil record. Meanwhile, engineers at the University of Kitakyushu in Japan created Tai-robot-kun, a credible-looking sea bream. And a team at Evologics, based in Berlin, came up with the BOSS manta ray.
Whatever their official purpose, these nature-inspired robocreatures can inspire us in return. UUVs that open up new and wondrous vistas on the world’s oceans can extend humankind’s ability to explore. We create them, and they enhance us, and that strikes me as a very fair and worthy exchange.
This article appears in the March 2021 print issue as “Catfish, Robot, Swimmer, Spy.”
About the Author
Allison Marsh is an associate professor of history at the University of South Carolina and codirector of the university’s Ann Johnson Institute for Science, Technology & Society. Continue reading
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#438774 The World’s First 3D Printed School ...
3D printed houses have been popping up all over the map. Some are hive-shaped, some can float, some are up for sale. Now this practical, cost-cutting technology is being employed for another type of building: a school.
Located on the island of Madagascar, the project is a collaboration between San Francisco-based architecture firm Studio Mortazavi and Thinking Huts, a nonprofit whose mission is to increase global access to education through 3D printing. The school will be built on the campus of a university in Fianarantsoa, a city in the south central area of the island nation.
According to the World Economic Forum, lack of physical infrastructure is one of the biggest barriers to education. Building schools requires not only funds, human capital, and building materials, but also community collaboration and ongoing upkeep and maintenance. For people to feel good about sending their kids to school each day, the buildings should be conveniently located, appealing, comfortable to spend several hours in, and of course safe. All of this is harder to accomplish than you might think, especially in low-income areas.
Because of its comparatively low cost and quick turnaround time, 3D printing has been lauded as a possible solution to housing shortages and a tool to aid in disaster relief. Cost details of the Madagascar school haven’t been released, but if 3D printed houses can go up in a day for under $10,000 or list at a much lower price than their non-3D-printed neighbors, it’s safe to say that 3D printing a school is likely substantially cheaper than building it through traditional construction methods.
The school’s modular design resembles a honeycomb, where as few or as many nodes as needed can be linked together. Each node consists of a room with two bathrooms, a closet, and a front and rear entrance. The Fianarantsoa school with just have one node to start with, but as local technologists will participate in the building process, they’ll learn the 3D printing ins and outs and subsequently be able to add new nodes or build similar schools in other areas.
Artist rendering of the completed school. Image Credit: Studio Mortazavi/Thinking Huts
The printer for the project is coming from Hyperion Robotics, a Finnish company that specializes in 3D printing solutions for reinforced concrete. The building’s walls will be made of layers of a special cement mixture that Thinking Huts says emits less carbon dioxide than traditional concrete. The roof, doors, and windows will be sourced locally, and the whole process can be completed in less than a week, another major advantage over traditional building methods.
“We can build these schools in less than a week, including the foundation and all the electrical and plumbing work that’s involved,” said Amir Mortazavi, lead architect on the project. “Something like this would typically take months, if not even longer.”
The roof of the building will be equipped with solar panels to provide the school with power, and in a true melding of modern technology and traditional design, the pattern of its walls is based on Malagasy textiles.
Thinking Huts considered seven different countries for its first school, and ended up choosing Madagascar for the pilot based on its need for education infrastructure, stable political outlook, opportunity for growth, and renewable energy potential. However, the team is hoping the pilot will be the first of many similar projects across multiple countries. “We can use this as a case study,” Mortazavi said. “Then we can go to other countries around the world and train the local technologists to use the 3D printer and start a nonprofit there to be able to build schools.”
Construction of the school will take place in the latter half of this year, with hopes of getting students into the classroom as soon as the pandemic is no longer a major threat to the local community’s health.
Image Credit: Studio Mortazavi/Thinking Huts Continue reading
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#437964 How Explainable Artificial Intelligence ...
The field of artificial intelligence has created computers that can drive cars, synthesize chemical compounds, fold proteins, and detect high-energy particles at a superhuman level.
However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation.
Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations.
Learning From Experience
One field of AI, called reinforcement learning, studies how computers can learn from their own experiences. In reinforcement learning, an AI explores the world, receiving positive or negative feedback based on its actions.
This approach has led to algorithms that have independently learned to play chess at a superhuman level and prove mathematical theorems without any human guidance. In my work as an AI researcher, I use reinforcement learning to create AI algorithms that learn how to solve puzzles such as the Rubik’s Cube.
Through reinforcement learning, AIs are independently learning to solve problems that even humans struggle to figure out. This has got me and many other researchers thinking less about what AI can learn and more about what humans can learn from AI. A computer that can solve the Rubik’s Cube should be able to teach people how to solve it, too.
Peering Into the Black Box
Unfortunately, the minds of superhuman AIs are currently out of reach to us humans. AIs make terrible teachers and are what we in the computer science world call “black boxes.”
AI simply spits out solutions without giving reasons for its solutions. Computer scientists have been trying for decades to open this black box, and recent research has shown that many AI algorithms actually do think in ways that are similar to humans. For example, a computer trained to recognize animals will learn about different types of eyes and ears and will put this information together to correctly identify the animal.
The effort to open up the black box is called explainable AI. My research group at the AI Institute at the University of South Carolina is interested in developing explainable AI. To accomplish this, we work heavily with the Rubik’s Cube.
The Rubik’s Cube is basically a pathfinding problem: Find a path from point A—a scrambled Rubik’s Cube—to point B—a solved Rubik’s Cube. Other pathfinding problems include navigation, theorem proving and chemical synthesis.
My lab has set up a website where anyone can see how our AI algorithm solves the Rubik’s Cube; however, a person would be hard-pressed to learn how to solve the cube from this website. This is because the computer cannot tell you the logic behind its solutions.
Solutions to the Rubik’s Cube can be broken down into a few generalized steps—the first step, for example, could be to form a cross while the second step could be to put the corner pieces in place. While the Rubik’s Cube itself has over 10 to the 19th power possible combinations, a generalized step-by-step guide is very easy to remember and is applicable in many different scenarios.
Approaching a problem by breaking it down into steps is often the default manner in which people explain things to one another. The Rubik’s Cube naturally fits into this step-by-step framework, which gives us the opportunity to open the black box of our algorithm more easily. Creating AI algorithms that have this ability could allow people to collaborate with AI and break down a wide variety of complex problems into easy-to-understand steps.
A step-by-step refinement approach can make it easier for humans to understand why AIs do the things they do. Forest Agostinelli, CC BY-ND
Collaboration Leads to Innovation
Our process starts with using one’s own intuition to define a step-by-step plan thought to potentially solve a complex problem. The algorithm then looks at each individual step and gives feedback about which steps are possible, which are impossible and ways the plan could be improved. The human then refines the initial plan using the advice from the AI, and the process repeats until the problem is solved. The hope is that the person and the AI will eventually converge to a kind of mutual understanding.
Currently, our algorithm is able to consider a human plan for solving the Rubik’s Cube, suggest improvements to the plan, recognize plans that do not work and find alternatives that do. In doing so, it gives feedback that leads to a step-by-step plan for solving the Rubik’s Cube that a person can understand. Our team’s next step is to build an intuitive interface that will allow our algorithm to teach people how to solve the Rubik’s Cube. Our hope is to generalize this approach to a wide range of pathfinding problems.
People are intuitive in a way unmatched by any AI, but machines are far better in their computational power and algorithmic rigor. This back and forth between man and machine utilizes the strengths from both. I believe this type of collaboration will shed light on previously unsolved problems in everything from chemistry to mathematics, leading to new solutions, intuitions and innovations that may have, otherwise, been out of reach.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Image Credit: Serg Antonov / Unsplash Continue reading
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