Tag Archives: collaborative

#432467 Dungeons and Dragons, Not Chess and Go: ...

Everyone had died—not that you’d know it, from how they were laughing about their poor choices and bad rolls of the dice. As a social anthropologist, I study how people understand artificial intelligence (AI) and our efforts towards attaining it; I’m also a life-long fan of Dungeons and Dragons (D&D), the inventive fantasy roleplaying game. During a recent quest, when I was playing an elf ranger, the trainee paladin (or holy knight) acted according to his noble character, and announced our presence at the mouth of a dragon’s lair. The results were disastrous. But while success in D&D means “beating the bad guy,” the game is also a creative sandbox, where failure can count as collective triumph so long as you tell a great tale.

What does this have to do with AI? In computer science, games are frequently used as a benchmark for an algorithm’s “intelligence.” The late Robert Wilensky, a professor at the University of California, Berkeley and a leading figure in AI, offered one reason why this might be. Computer scientists “looked around at who the smartest people were, and they were themselves, of course,” he told the authors of Compulsive Technology: Computers as Culture (1985). “They were all essentially mathematicians by training, and mathematicians do two things—they prove theorems and play chess. And they said, hey, if it proves a theorem or plays chess, it must be smart.” No surprise that demonstrations of AI’s “smarts” have focused on the artificial player’s prowess.

Yet the games that get chosen—like Go, the main battlefield for Google DeepMind’s algorithms in recent years—tend to be tightly bounded, with set objectives and clear paths to victory or defeat. These experiences have none of the open-ended collaboration of D&D. Which got me thinking: do we need a new test for intelligence, where the goal is not simply about success, but storytelling? What would it mean for an AI to “pass” as human in a game of D&D? Instead of the Turing test, perhaps we need an elf ranger test?

Of course, this is just a playful thought experiment, but it does highlight the flaws in certain models of intelligence. First, it reveals how intelligence has to work across a variety of environments. D&D participants can inhabit many characters in many games, and the individual player can “switch” between roles (the fighter, the thief, the healer). Meanwhile, AI researchers know that it’s super difficult to get a well-trained algorithm to apply its insights in even slightly different domains—something that we humans manage surprisingly well.

Second, D&D reminds us that intelligence is embodied. In computer games, the bodily aspect of the experience might range from pressing buttons on a controller in order to move an icon or avatar (a ping-pong paddle; a spaceship; an anthropomorphic, eternally hungry, yellow sphere), to more recent and immersive experiences involving virtual-reality goggles and haptic gloves. Even without these add-ons, games can still produce biological responses associated with stress and fear (if you’ve ever played Alien: Isolation you’ll understand). In the original D&D, the players encounter the game while sitting around a table together, feeling the story and its impact. Recent research in cognitive science suggests that bodily interactions are crucial to how we grasp more abstract mental concepts. But we give minimal attention to the embodiment of artificial agents, and how that might affect the way they learn and process information.

Finally, intelligence is social. AI algorithms typically learn through multiple rounds of competition, in which successful strategies get reinforced with rewards. True, it appears that humans also evolved to learn through repetition, reward and reinforcement. But there’s an important collaborative dimension to human intelligence. In the 1930s, the psychologist Lev Vygotsky identified the interaction of an expert and a novice as an example of what became called “scaffolded” learning, where the teacher demonstrates and then supports the learner in acquiring a new skill. In unbounded games, this cooperation is channelled through narrative. Games of It among small children can evolve from win/lose into attacks by terrible monsters, before shifting again to more complex narratives that explain why the monsters are attacking, who is the hero, and what they can do and why—narratives that aren’t always logical or even internally compatible. An AI that could engage in social storytelling is doubtless on a surer, more multifunctional footing than one that plays chess; and there’s no guarantee that chess is even a step on the road to attaining intelligence of this sort.

In some ways, this failure to look at roleplaying as a technical hurdle for intelligence is strange. D&D was a key cultural touchstone for technologists in the 1980s and the inspiration for many early text-based computer games, as Katie Hafner and Matthew Lyon point out in Where Wizards Stay up Late: The Origins of the Internet (1996). Even today, AI researchers who play games in their free time often mention D&D specifically. So instead of beating adversaries in games, we might learn more about intelligence if we tried to teach artificial agents to play together as we do: as paladins and elf rangers.

This article was originally published at Aeon and has been republished under Creative Commons.

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#431907 The Future of Cancer Treatment Is ...

In an interview at Singularity University’s Exponential Medicine in San Diego, Richard Wender, chief cancer control officer at the American Cancer Society, discussed how technology has changed cancer care and treatment in recent years.
Just a few years ago, microscopes were the primary tool used in cancer diagnoses, but we’ve come a long way since.
“We still look at a microscope, we still look at what organ the cancer started in,” Wender said. “But increasingly we’re looking at the molecular signature. It’s not just the genomics, and it’s not just the genes. It’s also the cellular environment around that cancer. We’re now targeting our therapies to the mutations that are found in that particular cancer.”
Cancer treatments in the past have been largely reactionary, but they don’t need to be. Most cancer is genetic, which means that treatment can be preventative. This is one reason why newer cancer treatment techniques are searching for actionable targets in the specific gene before the cancer develops.

When asked how artificial intelligence and machine learning technologies are reshaping clinical trials, Wender acknowledged that how clinical trials have been run in the past won’t work moving forward.
“Our traditional ways of learning about cancer were by finding a particular cancer type and conducting a long clinical trial that took a number of years enrolling patients from around the country. That is not how we’re going to learn to treat individual patients in the future.”
Instead, Wender emphasized the need for gathering as much data as possible, and from as many individual patients as possible. This data should encompass clinical, pathological, and molecular data and should be gathered from a patient all the way through their final outcome. “Literally every person becomes a clinical trial of one,” Wender said.
For the best cancer treatment and diagnostics, Wender says the answer is to make the process collaborative by pulling in resources from organizations and companies that are both established and emerging.
It’s no surprise to hear that the best solutions come from pairing together uncommon partners to innovate.
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#431900 Artificial muscles power up with new ...

Scientists are one step closer to artificial muscles. Orthotics have come a long way since their initial wood and strap designs, yet innovation lapsed when it came to compensating for muscle power—until now. A collaborative research team has designed a wearable robot to support a person's hip joint while walking. The team, led by Minoru Hashimoto, a professor of textile science and technology at Shinshu University in Japan, published the details of their prototype in Smart Materials and Structures, a journal published by the Institute of Physics. Continue reading

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#431733 Why Humanoid Robots Are Still So Hard to ...

Picture a robot. In all likelihood, you just pictured a sleek metallic or chrome-white humanoid. Yet the vast majority of robots in the world around us are nothing like this; instead, they’re specialized for specific tasks. Our cultural conception of what robots are dates back to the coining of the term robots in the Czech play, Rossum’s Universal Robots, which originally envisioned them as essentially synthetic humans.
The vision of a humanoid robot is tantalizing. There are constant efforts to create something that looks like the robots of science fiction. Recently, an old competitor in this field returned with a new model: Toyota has released what they call the T-HR3. As humanoid robots go, it appears to be pretty dexterous and have a decent grip, with a number of degrees of freedom making the movements pleasantly human.
This humanoid robot operates mostly via a remote-controlled system that allows the user to control the robot’s limbs by exerting different amounts of pressure on a framework. A VR headset completes the picture, allowing the user to control the robot’s body and teleoperate the machine. There’s no word on a price tag, but one imagines a machine with a control system this complicated won’t exactly be on your Christmas list, unless you’re a billionaire.

Toyota is no stranger to robotics. They released a series of “Partner Robots” that had a bizarre affinity for instrument-playing but weren’t often seen doing much else. Given that they didn’t seem to have much capability beyond the automaton that Leonardo da Vinci made hundreds of years ago, they promptly vanished. If, as the name suggests, the T-HR3 is a sequel to these robots, which came out shortly after ASIMO back in 2003, it’s substantially better.
Slightly less humanoid (and perhaps the more useful for it), Toyota’s HSR-2 is a robot base on wheels with a simple mechanical arm. It brings to mind earlier machines produced by dream-factory startup Willow Garage like the PR-2. The idea of an affordable robot that could simply move around on wheels and pick up and fetch objects, and didn’t harbor too-lofty ambitions to do anything else, was quite successful.
So much so that when Robocup, the international robotics competition, looked for a platform for their robot-butler competition @Home, they chose HSR-2 for its ability to handle objects. HSR-2 has been deployed in trial runs to care for the elderly and injured, but has yet to be widely adopted for these purposes five years after its initial release. It’s telling that arguably the most successful multi-purpose humanoid robot isn’t really humanoid at all—and it’s curious that Toyota now seems to want to return to a more humanoid model a decade after they gave up on the project.
What’s unclear, as is often the case with humanoid robots, is what, precisely, the T-HR3 is actually for. The teleoperation gets around the complex problem of control by simply having the machine controlled remotely by a human. That human then handles all the sensory perception, decision-making, planning, and manipulation; essentially, the hardest problems in robotics.
There may not be a great deal of autonomy for the T-HR3, but by sacrificing autonomy, you drastically cut down the uses of the robot. Since it can’t act alone, you need a convincing scenario where you need a teleoperated humanoid robot that’s less precise and vastly more expensive than just getting a person to do the same job. Perhaps someday more autonomy will be developed for the robot, and the master maneuvering system that allows humans to control it will only be used in emergencies to control the robot if it gets stuck.
Toyota’s press release says it is “a platform with capabilities that can safely assist humans in a variety of settings, such as the home, medical facilities, construction sites, disaster-stricken areas and even outer space.” In reality, it’s difficult to see such a robot being affordable or even that useful in the home or in medical facilities (unless it’s substantially stronger than humans). Equally, it certainly doesn’t seem robust enough to be deployed in disaster zones or outer space. These tasks have been mooted for robots for a very long time and few have proved up to the challenge.
Toyota’s third generation humanoid robot, the T-HR3. Image Credit: Toyota
Instead, the robot seems designed to work alongside humans. Its design, standing 1.5 meters tall, weighing 75 kilograms, and possessing 32 degrees of freedom in its body, suggests it is built to closely mimic a person, rather than a robot like ATLAS which is robust enough that you can imagine it being useful in a war zone. In this case, it might be closer to the model of the collaborative robots or co-bots developed by Rethink Robotics, whose tons of safety features, including force-sensitive feedback for the user, reduce the risk of terrible PR surrounding killer robots.
Instead the emphasis is on graceful precision engineering: in the promo video, the robot can be seen balancing on one leg before showing off a few poised, yoga-like poses. This perhaps suggests that an application in elderly care, which Toyota has ventured into before and which was the stated aim of their simple HSR-2, might be more likely than deployment to a disaster zone.
The reason humanoid robots remain so elusive and so tempting is probably because of a simple cognitive mistake. We make two bad assumptions. First, we assume that if you build a humanoid robot, give its joints enough flexibility, throw in a little AI and perhaps some pre-programmed behaviors, then presto, it will be able to do everything humans can. When you see a robot that moves well and looks humanoid, it seems like the hardest part is done; surely this robot could do anything. The reality is never so simple.

We also make the reverse assumption: we assume that when we are finally replaced, it will be by perfect replicas of our own bodies and brains that can fulfill all the functions we used to fulfill. Perhaps, in reality, the future of robots and AI is more like its present: piecemeal, with specialized algorithms and specialized machines gradually learning to outperform humans at every conceivable task without ever looking convincingly human.
It may well be that the T-HR3 is angling towards this concept of machine learning as a platform for future research. Rather than trying to program an omni-capable robot out of the box, it will gradually learn from its human controllers. In this way, you could see the platform being used to explore the limits of what humans can teach robots to do simply by having them mimic sequences of our bodies’ motion, in the same way the exploitation of neural networks is testing the limits of training algorithms on data. No one machine will be able to perform everything a human can, but collectively, they will vastly outperform us at anything you’d want one to do.
So when you see a new android like Toyota’s, feel free to marvel at its technical abilities and indulge in the speculation about whether it’s a PR gimmick or a revolutionary step forward along the road to human replacement. Just remember that, human-level bots or not, we’re already strolling down that road.
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#431181 Workspace Sentry collaborative robotics ...

PRINCETON, NJ September 13, 2017 – – ST Robotics announces the availability of its Workspace Sentry collaborative robotics safety system, specifically designed to meet the International Organization for Standardization (ISO)/Technical Specification (TS) 15066 on collaborative operation. The new ISO/TS 15066, a game changer for the robotics industry, provides guidelines for the design and implementation of a collaborative workspace that reduces risks to people.

The ST Robotics Workspace Sentry robot and area safety system are based on a small module that sends infrared beams across the workspace. If the user puts his hand (or any other object) in the workspace, the robot stops using programmable emergency deceleration. Each module has three beams at different angles and the distance a beam reaches is adjustable. Two or more modules can be daisy chained to watch a wider area.
Photo Credit: ST Robotics – www.robot.md
“A robot that is tuned to stop on impact may not be safe. Robots where the trip torque can be set at low thresholds are too slow for any practical industrial application. The best system is where the work area has proximity detectors so the robot stops before impact and that is the approach ST Robotics has taken,” states President and CEO of ST Robotics David Sands.

ST Robotics, widely known for ‘robotics within reach’, has offices in Princeton, New Jersey and Cambridge, England, as well as in Asia. One of the first manufacturers of bench-top robot arms, ST Robotics has been providing the lowest-priced, easy-to-program boxed robots for the past 30 years. ST’s robots are utilized the world over by companies and institutions such as Lockheed-Martin, Motorola, Honeywell, MIT, NASA, Pfizer, Sony and NXP. The numerous applications for ST’s robots benefit the manufacturing, nuclear, pharmaceutical, laboratory and semiconductor industries.

For additional information on ST Robotics, contact:
sales1@strobotics.com
(609) 584 7522
www.strobotics.com

For press inquiries, contact:
Joanne Pransky
World’s First Robotic Psychiatrist®
drjoanne@robot.md
(650) ROBOT-MD

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