Tag Archives: fan

#437805 Video Friday: Quadruped Robot HyQ ...

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!):

RSS 2020 – July 12-16, 2020 – [Virtual Conference]
CLAWAR 2020 – August 24-26, 2020 – [Virtual Conference]
ICUAS 2020 – September 1-4, 2020 – Athens, Greece
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
IROS 2020 – October 25-29, 2020 – Las Vegas, Nevada
ICSR 2020 – November 14-16, 2020 – Golden, Colorado
Let us know if you have suggestions for next week, and enjoy today’s videos.

Four-legged HyQ balancing on two legs. Nice results from the team at IIT’s Dynamic Legged Systems Lab. And we can’t wait to see the “ninja walk,” currently shown in simulation, implemented with the real robot!

The development of balance controllers for legged robots with point feet remains a challenge when they have to traverse extremely constrained environments. We present a balance controller that has the potential to achieve line walking for quadruped robots. Our initial experiments show the 90-kg robot HyQ balancing on two feet and recovering from external pushes, as well as some changes in posture achieved without losing balance.

[ IIT ]

Thanks Victor!

Ava Robotics’ telepresence robot has been beheaded by MIT, and it now sports a coronavirus-destroying UV array.

UV-C light has proven to be effective at killing viruses and bacteria on surfaces and aerosols, but it’s unsafe for humans to be exposed. Fortunately, Ava’s telepresence robot doesn’t require any human supervision. Instead of the telepresence top, the team subbed in a UV-C array for disinfecting surfaces. Specifically, the array uses short-wavelength ultraviolet light to kill microorganisms and disrupt their DNA in a process called ultraviolet germicidal irradiation. The complete robot system is capable of mapping the space — in this case, GBFB’s warehouse — and navigating between waypoints and other specified areas. In testing the system, the team used a UV-C dosimeter, which confirmed that the robot was delivering the expected dosage of UV-C light predicted by the model.

[ MIT ]

While it’s hard enough to get quadrupedal robots to walk in complex environments, this work from the Robotic Systems Lab at ETH Zurich shows some impressive whole body planning that allows ANYmal to squeeze its body through small or weirdly shaped spaces.

[ RSL ]

Engineering researchers at North Carolina State University and Temple University have developed soft robots inspired by jellyfish that can outswim their real-life counterparts. More practically, the new jellyfish-bots highlight a technique that uses pre-stressed polymers to make soft robots more powerful.

The researchers also used the technique to make a fast-moving robot that resembles a larval insect curling its body, then jumping forward as it quickly releases its stored energy. Lastly, the researchers created a three-pronged gripping robot – with a twist. Most grippers hang open when “relaxed,” and require energy to hold on to their cargo as it is lifted and moved from point A to point B. But this claw’s default position is clenched shut. Energy is required to open the grippers, but once they’re in position, the grippers return to their “resting” mode – holding their cargo tight.

[ NC State ]

As control skills increase, we are more and more impressed by what a Cassie bipedal robot can do. Those who have been following our channel, know that we always show the limitations of our work. So while there is still much to do, you gotta like the direction things are going. Later this year, you will see this controller integrated with our real-time planner and perception system. Autonomy with agility! Watch out for us!

[ University of Michigan ]

GITAI’s S1 arm is a little less exciting than their humanoid torso, but it looks like this one might actually be going to the ISS next year.

Here’s how the humanoid would handle a similar task:

[ GITAI ]

Thanks Fan!

If you need a robot that can lift 250 kg at 10 m/s across a workspace of a thousand cubic meters, here’s your answer.

[ Fraunhofer ]

Penn engineers with funding from the National Science Foundation, have nanocardboard plates able to levitate when bright light is shone on them. This fleet of tiny aircraft could someday explore the skies of other worlds, including Mars. The thinner atmosphere there would give the flyers a boost, enabling them to carry payloads ten times as massive as they are, making them an efficient, light-weight alternative to the Mars helicopter.

[ UPenn ]

Erin Sparks, assistant professor in Plant and Soil Sciences, dreamed of a robot she could use in her research. A perfect partnership was formed when Adam Stager, then a mechanical engineering Ph.D. student, reached out about a robot he had a gut feeling might be useful in agriculture. The pair moved forward with their research with corn at the UD Farm, using the robot to capture dynamic phenotyping information of brace roots over time.

[ Sparks Lab ]

This is a video about robot spy turtles but OMG that bird drone landing gear.

[ PBS ]

If you have a DJI Mavic, you now have something new to worry about.

[ DroGone ]

I was able to spot just one single person in the warehouse footage in this video.

[ Berkshire Grey ]

Flyability has partnered with the ROBINS Project to help fill gaps in the technology used in ship inspections. Watch this video to learn more about the ROBINS project and how Flyability’s drones for confined spaces are helping make inspections on ships safer, cheaper, and more efficient.

[ Flyability ]

In this video, a mission of the Alpha Aerial Scout of Team CERBERUS during the DARPA Subterranean Challenge Urban Circuit event is presented. The Alpha Robot operates inside the Satsop Abandoned Power Plant and performs autonomous exploration. This deployment took place during the 3rd field trial of team CERBERUS during the Urban Circuit event of the DARPA Subterranean Challenge.

[ ARL ]

More excellent talks from the remote Legged Robots ICRA workshop- we’ve posted three here, but there are several other good talks this week as well.

[ ICRA 2020 Legged Robots Workshop ] Continue reading

Posted in Human Robots

#437789 Video Friday: Robotic Glove Features ...

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!):

RSS 2020 – July 12-16, 2020 – [Virtual Conference]
CLAWAR 2020 – August 24-26, 2020 – [Virtual Conference]
ICUAS 2020 – September 1-4, 2020 – Athens, Greece
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
IROS 2020 – October 25-29, 2020 – Las Vegas, Nevada
ICSR 2020 – November 14-16, 2020 – Golden, Colorado
Let us know if you have suggestions for next week, and enjoy today’s videos.

Evidently, the folks at Unitree were paying attention to last week’s Video Friday.

[ Unitree ]

RoboSoft 2020 was a virtual conference this year (along with everything else), but they still held a soft robots contest, and here are four short vids—you can watch the rest of them here.

[ RoboSoft 2020 ]

If you were wondering why SoftBank bought Aldebaran Robotics and Boston Dynamics, here’s the answer.

I am now a Hawks fan. GO HAWKS!

[ Softbank Hawks ] via [ RobotStart ]

Scientists at the University of Liverpool have developed a fully autonomous mobile robot to assist them in their research. Using a type of AI, the robot has been designed to work uninterrupted for weeks at a time, allowing it to analyse data and make decisions on what to do next. Using a flexible arm with customised gripper it can be calibrated to interact with most standard lab equipment and machinery as well as navigate safely around human co-workers and obstacles.

[ Nature ]

Oregon State’s Cassie has been on break for a couple of months, but it’s back in the lab and moving alarmingly quickly.

[ DRL ]

The current situation linked to COVID-19 sadly led to the postponing of this year RoboCup 2020 at Bordeaux. As an official sponsor of The RoboCup, SoftBank Robotics wanted to take this opportunity to thank all RoboCupers and The RoboCup Federation for their support these past 13 years. We invite you to take a look at NAO’s adventure at The RoboCup as the official robot of the Standard Platform League. See you in Bordeaux 2021!

[ RoboCup 2021 ]

Miniature SAW robot crawling inside the intestines of a pig. You’re welcome.

[ Zarrouk Lab ]

The video demonstrates fast autonomous flight experiments in cluttered unknown environments, with the support of a robust and perception-aware replanning framework called RAPTOR. The associated paper is submitted to TRO.

[ HKUST ]

Since we haven’t gotten autonomy quite right yet, there’s a lot of telepresence going on for robots that operate in public spaces. Usually, you’ve got one remote human managing multiple robots, so it would be nice to make that interface a little more friendly, right?

[ HCI Lab ]

Arguable whether or not this is a robot, but it’s cool enough to spend a minute watching.

[ Ishikawa Lab ]

Communication is critical to collaboration; however, too much of it can degrade performance. Motivated by the need for effective use of a robot’s communication modalities, in this work, we present a computational framework that decides if, when, and what to communicate during human-robot collaboration.

[ Interactive Robotics ]

Robotiq has released the next generation of the grippers for collaborative robots: the 2F-85 and 2F-140. Both models gain greater robustness, safety, and customizability while retaining the same key benefits that have inspired thousands of manufacturers to choose them since their launch 6 years ago.

[ Robotiq ]

ANYmal C, the autonomous legged robot designed for industrial challenging environments, provides the mobility, autonomy and inspection intelligence to enable safe and efficient inspection operations. In this virtual showcase, discover how ANYmal climbs stairs, recovers from a fall, performs an autonomous mission and avoids obstacles, docks to charge by itself, digitizes analogue sensors and monitors the environment.

[ ANYbotics ]

At Waymo, we are committed to addressing inequality, and we believe listening is a critical first step toward driving positive change. Earlier this year, five Waymonauts sat down to share their thoughts on equity at work, challenging the status quo, and more. This is what they had to say.

[ Waymo ]

Nice of ABB to take in old robots and upgrade them to turn them into new robots again. Robots forever!

[ ABB ]

It’s nice seeing the progress being made by GITAI, one of the teams competing in the ANA Avatar XPRIZE Challenge, and also meet the humans behind the robots.

[ GITAI ] via [ XPRIZE ]

One more talk from the ICRA Legged Robotics Workshop: Jingyu Liu from DeepRobotics and Qiuguo Zhu from Zhejiang University.

[ Deep Robotics ] Continue reading

Posted in Human Robots

#437695 Video Friday: Even Robots Know That You ...

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!):

CLAWAR 2020 – August 24-26, 2020 – [Online Conference]
Other Than Human – September 3-10, 2020 – Stockholm, Sweden
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online Conference]
IROS 2020 – October 25-29, 2020 – Las Vegas, Nev., USA
CYBATHLON 2020 – November 13-14, 2020 – [Online Event]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today's videos.

From the Robotics and Perception Group at UZH comes Flightmare, a simulation environment for drones that combines a slick rendering engine with a robust physics engine that can run as fast as your system can handle.

Flightmare is composed of two main components: a configurable rendering engine built on Unity and a flexible physics engine for dynamics simulation. Those two components are totally decoupled and can run independently from each other. Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene; (ii) an API for reinforcement learning which can simulate hundreds of quadrotors in parallel; and (iii) an integration with a virtual-reality headset for interaction with the simulated environment. Flightmare can be used for various applications, including path-planning, reinforcement learning, visual-inertial odometry, deep learning, human-robot interaction, etc.

[ Flightmare ]

Quadruped robots yelling at people to maintain social distancing is really starting to become a thing, for better or worse.

We introduce a fully autonomous surveillance robot based on a quadruped platform that can promote social distancing in complex urban environments. Specifically, to achieve autonomy, we mount multiple cameras and a 3D LiDAR on the legged robot. The robot then uses an onboard real-time social distancing detection system to track nearby pedestrian groups. Next, the robot uses a crowd-aware navigation algorithm to move freely in highly dynamic scenarios. The robot finally uses a crowd aware routing algorithm to effectively promote social distancing by using human-friendly verbal cues to send suggestions to overcrowded pedestrians.

[ Project ]

Thanks Fan!

The Personal Robotics Group at Oregon State University is looking at UV germicidal irradiation for surface disinfection with a Fetch Manipulator Robot.

Fetch Robot disinfecting dance party woo!

[ Oregon State ]

How could you not take a mask from this robot?

[ Reachy ]

This work presents the design, development and autonomous navigation of the alpha-version of our Resilient Micro Flyer, a new type of collision-tolerant small aerial robot tailored to traversing and searching within highly confined environments including manhole-sized tubes. The robot is particularly lightweight and agile, while it implements a rigid collision-tolerant design which renders it resilient during forcible interaction with the environment. Furthermore, the design of the system is enhanced through passive flaps ensuring smoother and more compliant collision which was identified to be especially useful in very confined settings.

[ ARL ]

Pepper can make maps and autonomously navigate, which is interesting, but not as interesting as its posture when it's wandering around.

Dat backing into the charging dock tho.

[ Pepper ]

RatChair a strategy for displacing big objects by attaching relatively small vibration sources. After learning how several random bursts of vibration affect its pose, an optimization algorithm discovers the optimal sequence of vibration patterns required to (slowly but surely) move the object to a specified position.

This is from 2015, why isn't all of my furniture autonomous yet?!

[ KAIST ]

The new SeaDrone Pro is designed to be the underwater equivalent of a quadrotor. This video is a rendering, but we've been assured that it does actually exist.

[ SeaDrone ]

Thanks Eduardo!

Porous Loops is a lightweight composite facade panel that shows the potential of 3D printing of mineral foams for building scale applications.

[ ETH ]

Thanks Fan!

Here's an interesting idea for a robotic gripper- it's what appears to be a snap bracelet coupled to a pneumatic actuator that allows the snap bracelet to be reset.

[ Georgia Tech ]

Graze is developing a commercial robotic lawnmower. They're also doing a sort of crowdfunded investment thing, which probably explains the painfully overproduced nature of the following video:

A couple things about this: the hard part, which the video skips over almost entirely, is the mapping, localization, and understanding where to mow and where not to mow. The pitch deck seems to suggest that this is mostly done through computer vision, a thing that's perhaps easy to do under controlled ideal conditions, but difficult to apply to a world full lawns that are all different. The commercial aspect is interesting because golf courses are likely as standardized as you can get, but the emphasis here on how much money they can make without really addressing any of the technical stuff makes me raise an eyebrow or two.

[ Graze ]

The record & playback X-series arm demo allows the user to record the arm's movements while motors are torqued off. Then, the user may torque the motor's on and watch the movements they just made playback!

[ Interbotix ]

Shadow Robot has a new teleop system for its hand. I'm guessing that it's even trickier to use than it looks.

[ Shadow Robot ]

Quanser Interactive Labs is a collection of virtual hardware-based laboratory activities that supplement traditional or online courses. Same as working with physical systems in the lab, students work with virtual twins of Quanser's most popular plants, develop their mathematical models, implement and simulate the dynamic behavior of these systems, design controllers, and validate them on a high-fidelity 3D real-time virtual models. The virtual systems not only look like the real ones, they also behave, can be manipulated, measured, and controlled like real devices. And finally, when students go to the lab, they can deploy their virtually-validated designs on actual physical equipment.

[ Quanser ]

This video shows robot-assisted heart surgery. It's amazing to watch if you haven't seen this sort of thing before, but be aware that there is a lot of blood.

This video demonstrates a fascinating case of robotic left atrial myxoma excision, narrated by Joel Dunning, Middlesbrough, UK. The Robotic platform provides superior visualisation and enhanced dexterity, through keyhole incisions. Robotic surgery is an integral part of our Minimally Invasive Cardiothoracic Surgery Program.

[ Tristan D. Yan ]

Thanks Fan!

In this talk, we present our work on learning control policies directly in simulation that are deployed onto real drones without any fine tuning. The presentation covers autonomous drone racing, drone acrobatics, and uncertainty estimation in deep networks.

[ RPG ] Continue reading

Posted in Human Robots

#437303 The Deck Is Not Rigged: Poker and the ...

Tuomas Sandholm, a computer scientist at Carnegie Mellon University, is not a poker player—or much of a poker fan, in fact—but he is fascinated by the game for much the same reason as the great game theorist John von Neumann before him. Von Neumann, who died in 1957, viewed poker as the perfect model for human decision making, for finding the balance between skill and chance that accompanies our every choice. He saw poker as the ultimate strategic challenge, combining as it does not just the mathematical elements of a game like chess but the uniquely human, psychological angles that are more difficult to model precisely—a view shared years later by Sandholm in his research with artificial intelligence.

“Poker is the main benchmark and challenge program for games of imperfect information,” Sandholm told me on a warm spring afternoon in 2018, when we met in his offices in Pittsburgh. The game, it turns out, has become the gold standard for developing artificial intelligence.

Tall and thin, with wire-frame glasses and neat brow hair framing a friendly face, Sandholm is behind the creation of three computer programs designed to test their mettle against human poker players: Claudico, Libratus, and most recently, Pluribus. (When we met, Libratus was still a toddler and Pluribus didn’t yet exist.) The goal isn’t to solve poker, as such, but to create algorithms whose decision making prowess in poker’s world of imperfect information and stochastic situations—situations that are randomly determined and unable to be predicted—can then be applied to other stochastic realms, like the military, business, government, cybersecurity, even health care.

While the first program, Claudico, was summarily beaten by human poker players—“one broke-ass robot,” an observer called it—Libratus has triumphed in a series of one-on-one, or heads-up, matches against some of the best online players in the United States.

Libratus relies on three main modules. The first involves a basic blueprint strategy for the whole game, allowing it to reach a much faster equilibrium than its predecessor. It includes an algorithm called the Monte Carlo Counterfactual Regret Minimization, which evaluates all future actions to figure out which one would cause the least amount of regret. Regret, of course, is a human emotion. Regret for a computer simply means realizing that an action that wasn’t chosen would have yielded a better outcome than one that was. “Intuitively, regret represents how much the AI regrets having not chosen that action in the past,” says Sandholm. The higher the regret, the higher the chance of choosing that action next time.

It’s a useful way of thinking—but one that is incredibly difficult for the human mind to implement. We are notoriously bad at anticipating our future emotions. How much will we regret doing something? How much will we regret not doing something else? For us, it’s an emotionally laden calculus, and we typically fail to apply it in quite the right way. For a computer, it’s all about the computation of values. What does it regret not doing the most, the thing that would have yielded the highest possible expected value?

The second module is a sub-game solver that takes into account the mistakes the opponent has made so far and accounts for every hand she could possibly have. And finally, there is a self-improver. This is the area where data and machine learning come into play. It’s dangerous to try to exploit your opponent—it opens you up to the risk that you’ll get exploited right back, especially if you’re a computer program and your opponent is human. So instead of attempting to do that, the self-improver lets the opponent’s actions inform the areas where the program should focus. “That lets the opponent’s actions tell us where [they] think they’ve found holes in our strategy,” Sandholm explained. This allows the algorithm to develop a blueprint strategy to patch those holes.

It’s a very human-like adaptation, if you think about it. I’m not going to try to outmaneuver you head on. Instead, I’m going to see how you’re trying to outmaneuver me and respond accordingly. Sun-Tzu would surely approve. Watch how you’re perceived, not how you perceive yourself—because in the end, you’re playing against those who are doing the perceiving, and their opinion, right or not, is the only one that matters when you craft your strategy. Overnight, the algorithm patches up its overall approach according to the resulting analysis.

There’s one final thing Libratus is able to do: play in situations with unknown probabilities. There’s a concept in game theory known as the trembling hand: There are branches of the game tree that, under an optimal strategy, one should theoretically never get to; but with some probability, your all-too-human opponent’s hand trembles, they take a wrong action, and you’re suddenly in a totally unmapped part of the game. Before, that would spell disaster for the computer: An unmapped part of the tree means the program no longer knows how to respond. Now, there’s a contingency plan.

Of course, no algorithm is perfect. When Libratus is playing poker, it’s essentially working in a zero-sum environment. It wins, the opponent loses. The opponent wins, it loses. But while some real-life interactions really are zero-sum—cyber warfare comes to mind—many others are not nearly as straightforward: My win does not necessarily mean your loss. The pie is not fixed, and our interactions may be more positive-sum than not.

What’s more, real-life applications have to contend with something that a poker algorithm does not: the weights that are assigned to different elements of a decision. In poker, this is a simple value-maximizing process. But what is value in the human realm? Sandholm had to contend with this before, when he helped craft the world’s first kidney exchange. Do you want to be more efficient, giving the maximum number of kidneys as quickly as possible—or more fair, which may come at a cost to efficiency? Do you want as many lives as possible saved—or do some take priority at the cost of reaching more? Is there a preference for the length of the wait until a transplant? Do kids get preference? And on and on. It’s essential, Sandholm says, to separate means and the ends. To figure out the ends, a human has to decide what the goal is.

“The world will ultimately become a lot safer with the help of algorithms like Libratus,” Sandholm told me. I wasn’t sure what he meant. The last thing that most people would do is call poker, with its competition, its winners and losers, its quest to gain the maximum edge over your opponent, a haven of safety.

“Logic is good, and the AI is much better at strategic reasoning than humans can ever be,” he explained. “It’s taking out irrationality, emotionality. And it’s fairer. If you have an AI on your side, it can lift non-experts to the level of experts. Naïve negotiators will suddenly have a better weapon. We can start to close off the digital divide.”

It was an optimistic note to end on—a zero-sum, competitive game yielding a more ultimately fair and rational world.

I wanted to learn more, to see if it was really possible that mathematics and algorithms could ultimately be the future of more human, more psychological interactions. And so, later that day, I accompanied Nick Nystrom, the chief scientist of the Pittsburgh Supercomputing Center—the place that runs all of Sandholm’s poker-AI programs—to the actual processing center that make undertakings like Libratus possible.

A half-hour drive found us in a parking lot by a large glass building. I’d expected something more futuristic, not the same square, corporate glass squares I’ve seen countless times before. The inside, however, was more promising. First the security checkpoint. Then the ride in the elevator — down, not up, to roughly three stories below ground, where we found ourselves in a maze of corridors with card readers at every juncture to make sure you don’t slip through undetected. A red-lit panel formed the final barrier, leading to a small sliver of space between two sets of doors. I could hear a loud hum coming from the far side.

“Let me tell you what you’re going to see before we walk in,” Nystrom told me. “Once we get inside, it will be too loud to hear.”

I was about to witness the heart of the supercomputing center: 27 large containers, in neat rows, each housing multiple processors with speeds and abilities too great for my mind to wrap around. Inside, the temperature is by turns arctic and tropic, so-called “cold” rows alternating with “hot”—fans operate around the clock to cool the processors as they churn through millions of giga, mega, tera, peta and other ever-increasing scales of data bytes. In the cool rows, robotic-looking lights blink green and blue in orderly progression. In the hot rows, a jumble of multicolored wires crisscrosses in tangled skeins.

In the corners stood machines that had outlived their heyday. There was Sherlock, an old Cray model, that warmed my heart. There was a sad nameless computer, whose anonymity was partially compensated for by the Warhol soup cans adorning its cage (an homage to Warhol’s Pittsburghian origins).

And where does Libratus live, I asked? Which of these computers is Bridges, the computer that runs the AI Sandholm and I had been discussing?

Bridges, it turned out, isn’t a single computer. It’s a system with processing power beyond comprehension. It takes over two and a half petabytes to run Libratus. A single petabyte is a million gigabytes: You could watch over 13 years of HD video, store 10 billion photos, catalog the contents of the entire Library of Congress word for word. That’s a whole lot of computing power. And that’s only to succeed at heads-up poker, in limited circumstances.

Yet despite the breathtaking computing power at its disposal, Libratus is still severely limited. Yes, it beat its opponents where Claudico failed. But the poker professionals weren’t allowed to use many of the tools of their trade, including the opponent analysis software that they depend on in actual online games. And humans tire. Libratus can churn for a two-week marathon, where the human mind falters.

But there’s still much it can’t do: play more opponents, play live, or win every time. There’s more humanity in poker than Libratus has yet conquered. “There’s this belief that it’s all about statistics and correlations. And we actually don’t believe that,” Nystrom explained as we left Bridges behind. “Once in a while correlations are good, but in general, they can also be really misleading.”

Two years later, the Sandholm lab will produce Pluribus. Pluribus will be able to play against five players—and will run on a single computer. Much of the human edge will have evaporated in a short, very short time. The algorithms have improved, as have the computers. AI, it seems, has gained by leaps and bounds.

So does that mean that, ultimately, the algorithmic can indeed beat out the human, that computation can untangle the web of human interaction by discerning “the little tactics of deception, of asking yourself what is the other man going to think I mean to do,” as von Neumann put it?

Long before I’d spoken to Sandholm, I’d met Kevin Slavin, a polymath of sorts whose past careers have including founding a game design company and an interactive art space and launching the Playful Systems group at MIT’s Media Lab. Slavin has a decidedly different view from the creators of Pluribus. “On the one hand, [von Neumann] was a genius,” Kevin Slavin reflects. “But the presumptuousness of it.”

Slavin is firmly on the side of the gambler, who recognizes uncertainty for what it is and thus is able to take calculated risks when necessary, all the while tampering confidence at the outcome. The most you can do is put yourself in the path of luck—but to think you can guess with certainty the actual outcome is a presumptuousness the true poker player foregoes. For Slavin, the wonder of computers is “That they can generate this fabulous, complex randomness.” His opinion of the algorithmic assaults on chance? “This is their moment,” he said. “But it’s the exact opposite of what’s really beautiful about a computer, which is that it can do something that’s actually unpredictable. That, to me, is the magic.”

Will they actually succeed in making the unpredictable predictable, though? That’s what I want to know. Because everything I’ve seen tells me that absolute success is impossible. The deck is not rigged.

“It’s an unbelievable amount of work to get there. What do you get at the end? Let’s say they’re successful. Then we live in a world where there’s no God, agency, or luck,” Slavin responded.

“I don’t want to live there,’’ he added “I just don’t want to live there.”

Luckily, it seems that for now, he won’t have to. There are more things in life than are yet written in the algorithms. We have no reliable lie detection software—whether in the face, the skin, or the brain. In a recent test of bluffing in poker, computer face recognition failed miserably. We can get at discomfort, but we can’t get at the reasons for that discomfort: lying, fatigue, stress—they all look much the same. And humans, of course, can also mimic stress where none exists, complicating the picture even further.

Pluribus may turn out to be powerful, but von Neumann’s challenge still stands: The true nature of games, the most human of the human, remains to be conquered.

This article was originally published on Undark. Read the original article.

Image Credit: José Pablo Iglesias / Unsplash Continue reading

Posted in Human Robots

#437265 This Russian Firm’s Star Designer Is ...

Imagine discovering a new artist or designer—whether visual art, fashion, music, or even writing—and becoming a big fan of her work. You follow her on social media, eagerly anticipate new releases, and chat about her talent with your friends. It’s not long before you want to know more about this creative, inspiring person, so you start doing some research. It’s strange, but there doesn’t seem to be any information about the artist’s past online; you can’t find out where she went to school or who her mentors were.

After some more digging, you find out something totally unexpected: your beloved artist is actually not a person at all—she’s an AI.

Would you be amused? Annoyed? Baffled? Impressed? Probably some combination of all these. If you wanted to ask someone who’s had this experience, you could talk to clients of the biggest multidisciplinary design company in Russia, Art.Lebedev Studio (I know, the period confused me at first too). The studio passed off an AI designer as human for more than a year, and no one caught on.

They gave the AI a human-sounding name—Nikolay Ironov—and it participated in more than 20 different projects that included designing brand logos and building brand identities. According to the studio’s website, several of the logos the AI made attracted “considerable public interest, media attention, and discussion in online communities” due to their unique style.

So how did an AI learn to create such buzz-worthy designs? It was trained using hand-drawn vector images each associated with one or more themes. To start a new design, someone enters a few words describing the client, such as what kind of goods or services they offer. The AI uses those words to find associated images and generate various starter designs, which then go through another series of algorithms that “touch them up.” A human designer then selects the best options to present to the client.

“These systems combined together provide users with the experience of instantly converting a client’s text brief into a corporate identity design pack archive. Within seconds,” said Sergey Kulinkovich, the studio’s art director. He added that clients liked Nikolay Ironov’s work before finding out he was an AI (and liked the media attention their brands got after Ironov’s identity was revealed even more).

Ironov joins a growing group of AI “artists” that are starting to raise questions about the nature of art and creativity. Where do creative ideas come from? What makes a work of art truly great? And when more than one person is involved in making art, who should own the copyright?

Art.Lebedev is far from the first design studio to employ artificial intelligence; Mailchimp is using AI to let businesses design multi-channel marketing campaigns without human designers, and Adobe is marketing its new Sensei product as an AI design assistant.

While art made by algorithms can be unique and impressive, though, there’s one caveat that’s important to keep in mind when we worry about human creativity being rendered obsolete. Here’s the thing: AIs still depend on people to not only program them, but feed them a set of training data on which their intelligence and output are based. Depending on the size and nature of an AI’s input data, its output will look pretty different from that of a similar system, and a big part of the difference will be due to the people that created and trained the AIs.

Admittedly, Nikolay Ironov does outshine his human counterparts in a handful of ways; as the studio’s website points out, he can handle real commercial tasks effectively, he doesn’t sleep, get sick, or have “crippling creative blocks,” and he can complete tasks in a matter of seconds.

Given these superhuman capabilities, then, why even keep human designers on staff? As detailed above, it will be a while before creative firms really need to consider this question on a large scale; for now, it still takes a hard-working creative human to make a fast-producing creative AI.

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