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#439100 Video Friday: Robotic Eyeball Camera

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!):

RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
RoboCup 2021 – June 22-28, 2021 – [Online Event]
DARPA SubT Finals – September 21-23, 2021 – Louisville, KY, USA
WeRobot 2021 – September 23-25, 2021 – Coral Gables, FL, USA
Let us know if you have suggestions for next week, and enjoy today's videos.

What if seeing devices looked like us? Eyecam is a prototype exploring the potential future design of sensing devices. Eyecam is a webcam shaped like a human eye that can see, blink, look around and observe us.

And it's open source, so you can build your own!

[ Eyecam ]

Looks like Festo will be turning some of its bionic robots into educational kits, which is a pretty cool idea.

[ Bionics4Education ]

Underwater soft robots are challenging to model and control because of their high degrees of freedom and their intricate coupling with water. In this paper, we present a method that leverages the recent development in differentiable simulation coupled with a differentiable, analytical hydrodynamic model to assist with the modeling and control of an underwater soft robot. We apply this method to Starfish, a customized soft robot design that is easy to fabricate and intuitive to manipulate.

[ MIT CSAIL ]

Rainbow Robotics, the company who made HUBO, has a new collaborative robot arm.

[ Rainbow Robotics ]

Thanks Fan!

We develop an integrated robotic platform for advanced collaborative robots and demonstrates an application of multiple robots collaboratively transporting an object to different positions in a factory environment. The proposed platform integrates a drone, a mobile manipulator robot, and a dual-arm robot to work autonomously, while also collaborating with a human worker. The platform also demonstrates the potential of a novel manufacturing process, which incorporates adaptive and collaborative intelligence to improve the efficiency of mass customization for the factory of the future.

[ Paper ]

Thanks Poramate!

In Sevastopol State University the team of the Laboratory of Underwater Robotics and Control Systems and Research and Production Association “Android Technika” performed tests of an underwater anropomorphic manipulator robot.

[ Sevastopol State ]

Thanks Fan!

Taiwanese company TCI Gene created a COVID test system based on their fully automated and enclosed gene testing machine QVS-96S. The system includes two ABB robots and carries out 1800 tests per day, operating 24/7. Every hour 96 virus samples tests are made with an accuracy of 99.99%.

[ ABB ]

A short video showing how a Halodi Robotics can be used in a commercial guarding application.

[ Halodi ]

During the past five years, under the NASA Early Space Innovations program, we have been developing new design optimization methods for underactuated robot hands, aiming to achieve versatile manipulation in highly constrained environments. We have prototyped hands for NASA’s Astrobee robot, an in-orbit assistive free flyer for the International Space Station.

[ ROAM Lab ]

The new, improved OTTO 1500 is a workhorse AMR designed to move heavy payloads through demanding environments faster than any other AMR on the market, with zero compromise to safety.

[ ROAM Lab ]

Very, very high performance sensing and actuation to pull this off.

[ Ishikawa Group ]

We introduce a conversational social robot designed for long-term in-home use to help with loneliness. We present a novel robot behavior design to have simple self-reflection conversations with people to improve wellness, while still being feasible, deployable, and safe.

[ HCI Lab ]

We are one of the 5 winners of the Start-up Challenge. This video illustrates what we achieved during the Swisscom 5G exploration week. Our proof-of-concept tele-excavation system is composed of a Menzi Muck M545 walking excavator automated & customized by Robotic Systems Lab and IBEX motion platform as the operator station. The operator and remote machine are connected for the first time via a 5G network infrastructure which was brought to our test field by Swisscom.

[ RSL ]

This video shows LOLA balancing on different terrain when being pushed in different directions. The robot is technically blind, not using any camera-based or prior information on the terrain (hard ground is assumed).

[ TUM ]

Autonomous driving when you cannot see the road at all because it's buried in snow is some serious autonomous driving.

[ Norlab ]

A hierarchical and robust framework for learning bipedal locomotion is presented and successfully implemented on the 3D biped robot Digit. The feasibility of the method is demonstrated by successfully transferring the learned policy in simulation to the Digit robot hardware, realizing sustained walking gaits under external force disturbances and challenging terrains not included during the training process.

[ OSU ]

This is a video summary of the Center for Robot-Assisted Search and Rescue's deployments under the direction of emergency response agencies to more than 30 disasters in five countries from 2001 (9/11 World Trade Center) to 2018 (Hurricane Michael). It includes the first use of ground robots for a disaster (WTC, 2001), the first use of small unmanned aerial systems (Hurricane Katrina 2005), and the first use of water surface vehicles (Hurricane Wilma, 2005).

[ CRASAR ]

In March, a team from the Oxford Robotics Institute collected a week of epic off-road driving data, as part of the Sense-Assess-eXplain (SAX) project.

[ Oxford Robotics ]

As a part of the AAAI 2021 Spring Symposium Series, HEBI Robotics was invited to present an Industry Talk on the symposium's topic: Machine Learning for Mobile Robot Navigation in the Wild. Included in this presentation was a short case study on one of our upcoming mobile robots that is being designed to successfully navigate unstructured environments where today's robots struggle.

[ HEBI Robotics ]

Thanks Hardik!

This Lockheed Martin Robotics Seminar is from Chad Jenkins at the University of Michigan, on “Semantic Robot Programming… and Maybe Making the World a Better Place.”

I will present our efforts towards accessible and general methods of robot programming from the demonstrations of human users. Our recent work has focused on Semantic Robot Programming (SRP), a declarative paradigm for robot programming by demonstration that builds on semantic mapping. In contrast to procedural methods for motion imitation in configuration space, SRP is suited to generalize user demonstrations of goal scenes in workspace, such as for manipulation in cluttered environments. SRP extends our efforts to crowdsource robot learning from demonstration at scale through messaging protocols suited to web/cloud robotics. With such scaling of robotics in mind, prospects for cultivating both equal opportunity and technological excellence will be discussed in the context of broadening and strengthening Title IX and Title VI.

[ UMD ] Continue reading

Posted in Human Robots

#439095 DARPA Prepares for the Subterranean ...

The DARPA Subterranean Challenge Final Event is scheduled to take place at the Louisville Mega Cavern in Louisville, Kentucky, from September 21 to 23. We’ve followed SubT teams as they’ve explored their way through abandoned mines, unfinished nuclear reactors, and a variety of caves, and now everything comes together in one final course where the winner of the Systems Track will take home the $2 million first prize.

It’s a fitting reward for teams that have been solving some of the hardest problems in robotics, but winning isn’t going to be easy, and we’ll talk with SubT Program Manager Tim Chung about what we have to look forward to.

Since we haven’t talked about SubT in a little while (what with the unfortunate covid-related cancellation of the Systems Track Cave Circuit), here’s a quick refresher of where we are: the teams have made it through the Tunnel Circuit, the Urban Circuit, and a virtual version of the Cave Circuit, and some of them have been testing in caves of their own. The Final Event will include all of these environments, and the teams of robots will have 60 minutes to autonomously map the course, locating artifacts to score points. Since I’m not sure where on Earth there’s an underground location that combines tunnels and caves with urban structures, DARPA is going to have to get creative, and the location in which they’ve chosen to do that is Louisville, Kentucky.

The Louisville Mega Cavern is a former limestone mine, most of which is under the Louisville Zoo. It’s not all that deep, mostly less than 30 meters under the surface, but it’s enormous: with 370,000 square meters of rooms and passages, the cavern currently hosts (among other things) a business park, a zipline course, and mountain bike trails, because why not. While DARPA is keeping pretty quiet on the details, I’m guessing that they’ll be taking over a chunk of the cavern and filling it with features representing as many of the environmental challenges as they can.

To learn more about how the SubT Final Event is going to go, we spoke with SubT Program Manager Tim Chung. But first, we talked about Tim’s perspective on the success of the Urban Circuit, and how teams have been managing without an in-person Cave Circuit.

IEEE Spectrum: How did the SubT Urban Circuit go?

Tim Chung: On a couple fronts, Urban Circuit was really exciting. We were in this unfinished nuclear power plant—I’d be surprised if any of the competitors had prior experience in such a facility, or anything like it. I think that was illuminating both from an experiential point of view for the competitors, but also from a technology point of view, too.

One thing that I thought was really interesting was that we, DARPA, didn't need to make the venue more challenging. The real world is really that hard. There are places that were just really heinous for these robots to have to navigate through in order to look in every nook and cranny for artifacts. There were corners and doorways and small corridors and all these kind of things that really forced the teams to have to work hard, and the feedback was, why did DARPA have to make it so hard? But we didn’t, and in fact there were places that for the safety of the robots and personnel, we had to ensure the robots couldn’t go.

It sounds like some teams thought this course was on the more difficult side—do you think you tuned it to just the right amount of DARPA-hard?

Our calibration worked quite well. We were able to tease out and help refine and better understand what technologies are both useful and critical and also those technologies that might not necessarily get you the leap ahead capability. So as an example, the Urban Circuit really emphasized verticality, where you have to be able to sense, understand, and maneuver in three dimensions. Being able to capitalize on their robot technologies to address that verticality really stratified the teams, and showed how critical those capabilities are.

We saw teams that brought a lot of those capabilities do very well, and teams that brought baseline capabilities do what they could on the single floor that they were able to operate on. And so I think we got the Goldilocks solution for Urban Circuit that combined both difficulty and ambition.

Photos: Evan Ackerman/IEEE Spectrum

Two SubT Teams embedded networking equipment in balls that they could throw onto the course.

One of the things that I found interesting was that two teams independently came up with throwable network nodes. What was DARPA’s reaction to this? Is any solution a good solution, or was it more like the teams were trying to game the system?

You mean, do we want teams to game the rules in any way so as to get a competitive advantage? I don't think that's what the teams were doing. I think they were operating not only within the bounds of the rules, which permitted such a thing as throwable sensors where you could stand at the line and see how far you could chuck these things—not only was that acceptable by the rules, but anticipated. Behind the scenes, we tried to do exactly what these teams are doing and think through different approaches, so we explicitly didn't forbid such things in our rules because we thought it's important to have as wide an aperture as possible.

With these comms nodes specifically, I think they’re pretty clever. They were in some cases hacked together with a variety of different sports paraphernalia to see what would provide the best cushioning. You know, a lot of that happens in the field, and what it captured was that sometimes you just need to be up at two in the morning and thinking about things in a slightly different way, and that's when some nuggets of innovation can arise, and we see this all the time with operators in the field as well. They might only have duct tape or Styrofoam or whatever the case may be and that's when they come up with different ways to solve these problems. I think from DARPA’s perspective, and certainly from my perspective, wherever innovation can strike, we want to try to encourage and inspire those opportunities. I thought it was great, and it’s all part of the challenge.

Is there anything you can tell us about what your original plan had been for the Cave Circuit?

I can say that we’ve had the opportunity to go through a number of these caves scattered all throughout the country, and engage with caving communities—cavers clubs, speleologists that conduct research, and then of course the cave rescue community. The single biggest takeaway
is that every cave, and there are tens of thousands of them in the US alone, every cave has its own personality, and a lot of that personality is quite hidden from humans, because we can’t explore or access all of the cave. This led us to a number of different caves that were intriguing from a DARPA perspective but also inspirational for our Cave Circuit Virtual Competition.

How do you feel like the tuning was for the Virtual Cave Circuit?

The Virtual Competition, as you well know, was exciting in the sense that we could basically combine eight worlds into one competition, whereas the systems track competition really didn’t give us that opportunity. Even if we were able have held the Cave Circuit Systems Competition in person, it would have been at one site, and it would have been challenging to represent the level of diversity that we could with the Virtual Competition. So I think from that perspective, it’s clearly an advantage in terms of calibration—diversity gets you the ability to aggregate results to capture those that excel across all worlds as well as those that do well in one world or some worlds and not the others. I think the calibration was great in the sense that we were able to see the gamut of performance. Those that did well, did quite well, and those that have room to grow showed where those opportunities are for them as well.

We had to find ways to capture that diversity and that representativeness, and I think one of the fun ways we did that was with the different cave world tiles that we were able to combine in a variety of different ways. We also made use of a real world data set that we were able to take from a laser scan. Across the board, we had a really great chance to illustrate why virtual testing and simulation still plays such a dominant role in robotics technology development, and why I think it will continue to play an increasing role for developing these types of autonomy solutions.

Photo: Team CSIRO Data 61

How can systems track teams learn from their testing in whatever cave is local to them and effectively apply that to whatever cave environment is part of the final considering what the diversity of caves is?

I think that hits the nail on the head for what we as technologists are trying to discover—what are the transferable generalizable insights and how does that inform our technology development? As roboticists we want to optimize our systems to perform well at the tasks that they were designed to do, and oftentimes that means specialization because we get increased performance at the expense of being a generalist robot. I think in the case of SubT, we want to have our cake and eat it too—we want robots that perform well and reliably, but we want them to do so not just in one environment, which is how we tend to think about robot performance, but we want them to operate well in many environments, many of which have yet to be faced.

And I think that's kind of the nuance here, that we want robot systems to be generalists for the sake of being able to handle the unknown, namely the real world, but still achieve a high level of performance and perhaps they do that to their combined use of different technologies or advances in autonomy or perception approaches or novel mechanisms or mobility, but somehow they're still able, at least in aggregate, to achieve high performance.

We know these teams eagerly await any type of clue that DARPA can provide like about the SubT environments. From the environment previews for Tunnel, Urban, and even Cave, the teams were pivoting around and thinking a little bit differently. The takeaway, however, was that they didn't go to a clean sheet design—their systems were flexible enough that they could incorporate some of those specialist trends while still maintaining the notion of a generalist framework.

Looking ahead to the SubT Final, what can you tell us about the Louisville Mega Cavern?

As always, I’ll keep you in suspense until we get you there, but I can say that from the beginning of the SubT Challenge we had always envisioned teams of robots that are able to address not only the uncertainty of what's right in front of them, but also the uncertainty of what comes next. So I think the teams will be advantaged by thinking through subdomain awareness, or domain awareness if you want to generalize it, whether that means tuning multi-purpose robots, or deploying different robots, or employing your team of robots differently. Knowing which subdomain you are in is likely to be helpful, because then you can take advantage of those unique lessons learned through all those previous experiences then capitalize on that.

As far as specifics, I think the Mega Cavern offers many of the features important to what it means to be underground, while giving DARPA a pretty blank canvas to realize our vision of the SubT Challenge.

The SubT Final will be different from the earlier circuits in that there’s just one 60-minute run, rather than two. This is going to make things a lot more stressful for teams who have experienced bad robot days—why do it this way?

The preliminary round has two 30-minute runs, and those two runs are very similar to how we have done it during the circuits, of a single run per configuration per course. Teams will have the opportunity to show that their systems can face the obstacles in the final course, and it's the sum of those scores much like we did during the circuits, to help mitigate some of the concerns that you mentioned of having one robot somehow ruin their chances at a prize.

The prize round does give DARPA as well as the community a chance to focus on the top six teams from the preliminary round, and allows us to understand how they came to be at the top of the pack while emphasizing their technological contributions. The prize round will be one and done, but all of these teams we anticipate will be putting their best robot forward and will show the world why they deserve to win the SubT Challenge.

We’ve always thought that when called upon these robots need to operate in really challenging environments, and in the context of real world operations, there is no second chance. I don't think it's actually that much of a departure from our interests and insistence on bringing reliable technologies to the field, and those teams that might have something break here and there, that's all part of the challenge, of being resilient. Many teams struggled with robots that were debilitated on the course, and they still found ways to succeed and overcome that in the field, so maybe the rules emphasize that desire for showing up and working on game day which is consistent, I think, with how we've always envisioned it. This isn’t to say that these systems have to work perfectly, they just have to work in a way such that the team is resilient enough to tackle anything that they face.

It’s not too late for teams to enter for both the Virtual Track and the Systems Track to compete in the SubT Final, right?

Yes, that's absolutely right. Qualifications are still open, we are eager to welcome new teams to join in along with our existing competitors. I think any dark horse competitors coming into the Finals may be able to bring something that we haven't seen before, and that would be really exciting. I think it'll really make for an incredibly vibrant and illuminating final event.

The final event qualification deadline for the Systems Competition is April 21, and the qualification deadline for the Virtual Competition is June 29. More details here. Continue reading

Posted in Human Robots

#439066 Video Friday: Festo’s BionicSwift

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!):

RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
DARPA SubT Finals – September 21-23, 2021 – Louisville, KY, USA
WeRobot 2021 – September 23-25, 2021 – Coral Gables, FL, USA
Let us know if you have suggestions for next week, and enjoy today's videos.

Festo's Bionic Learning Network for 2021 presents a flock of BionicSwifts.

To execute the flight maneuvers as true to life as possible, the wings are modeled on the plumage of birds. The individual lamellae are made of an ultralight, flexible but very robust foam and lie on top of each other like shingles. Connected to a carbon quill, they are attached to the actual hand and arm wings as in the natural model.

During the wing upstroke, the individual lamellae fan out so that air can flow through the wing. This means that the birds need less force to pull the wing up. During the downstroke, the lamellae close up so that the birds can generate more power to fly. Due to this close-to-nature replica of the wings, the BionicSwifts have a better flight profile than previous wing-beating drives.

[ Festo ]

While we've seen a wide variety of COVID-motivated disinfecting robots, they're usually using either ultraviolet light or a chemical fog. This isn't the way that humans clean—we wipe stuff down, which gets rid of surface dirt and disinfects at the same time. Fraunhofer has been working on a mobile manipulator that can clean in the same ways that we do.

It's quite the technical challenge, but it has the potential to be both more efficient and more effective.

[ Fraunhofer ]

In recent years, robots have gained artificial vision, touch, and even smell. “Researchers have been giving robots human-like perception,” says MIT Associate Professor Fadel Adib. In a new paper, Adib’s team is pushing the technology a step further. “We’re trying to give robots superhuman perception,” he says. The researchers have developed a robot that uses radio waves, which can pass through walls, to sense occluded objects. The robot, called RF-Grasp, combines this powerful sensing with more traditional computer vision to locate and grasp items that might otherwise be blocked from view.

[ MIT ]

Ingenuity is now scheduled to fly on April 11.

[ JPL ]

The legendary Zenta is back after a two year YouTube hiatus with “a kind of freaky furry hexapod bunny creature.”

[ Zenta ]

It is with great pride and excitement that the South Australia Police announce a new expansion to their kennel by introducing three new Police Dog (PD) recruits. These dogs have been purposely targeted to bring a whole new range of dog operational capabilities known as the ‘small area urban search and guided evacuation’ dogs. Police have been working closely with specialist vets and dog trainers to ascertain if the lightweight dogs could be transported safely by drones and released into hard-to-access areas where at the moment the larger PDs just simply cannot get in due to their size.

[ SA Police ]

SoftBank may not have Spot cheerleading robots for their baseball team anymore, but they've more than made up for it with a full century of Peppers. And one dude doing the robot.

[ SoftBank ]

MAB Robotics is a Polish company developing walking robots for inspection, and here's a prototype they've been working on.

[ MAB Robotics ]

Thanks Jakub!

DoraNose: Smell your way to a better tomorrow.

[ Dorabot ]

Our robots need to learn how to cope with their new neighbors, and we have just the solution for this, the egg detector! Using cutting-edge AI, it provides incredible precision in detecting a vast variety of eggs. We have deployed this new feature on Boston Dynamics Spot, one of our fleet's robots. It can now detect eggs with its cameras and avoid them on his autonomous missions.

[ Energy Robotics ]

When dropping a squishy robot from an airplane 1,000 feet up, make sure that you land as close to people's cars as you can.

Now do it from orbit!

[ Squishy Robotics ]

An autonomous robot that is able to physically guide humans through narrow and cluttered spaces could be a big boon to the visually-impaired. Most prior robotic guiding systems are based on wheeled platforms with large bases with actuated rigid guiding canes. The large bases and the actuated arms limit these prior approaches from operating in narrow and cluttered environments. We propose a method that introduces a quadrupedal robot with a leash to enable the robot-guiding-human system to change its intrinsic dimension (by letting the leash go slack) in order to fit into narrow spaces.

[ Hybrid Robotics ]

How to prove that your drone is waterproof.

[ UNL ]

Well this ought to be pretty good once it gets out of simulation.

[ Hybrid Robotics ]

MIDAS is Aurora’s AI-enabled, multi-rotor sUAV outfitted with optical sensors and a customized payload that can defeat multiple small UAVs per flight with low-collateral effects.

[ Aurora ]

The robots​ of the DFKI have the advantage of being able to reach extreme environments: they can be used for decontamination purposes in high-risk areas or inspect and maintain underwater​ structures, for which they are tested in the North Sea near Heligoland​.

[ DFKI ]

After years of trying, 60 Minutes cameras finally get a peek inside the workshop at Boston Dynamics, where robots move in ways once only thought possible in movies. Anderson Cooper reports.

[ 60 Minutes ]

In 2007, Noel Sharky stated that “we are sleepwalking into a brave new world where robots decide who, where and when to kill.” Since then thousands of AI and robotics researchers have joined his calls to regulate “killer robots.” But sometime this year, Turkey will deploy fully autonomous home-built kamikaze drones on its border with Syria. What are the ethical choices we need to consider? Will we end up in an episode of Black Mirror? Or is the UN listening to calls and starting the process of regulating this space? Prof. Toby Walsh will discuss this important issue, consider where we are at and where we need to go.

[ ICRA 2020 ]

In the second session of HAI's spring conference, artists and technologists discussed how technology can enhance creativity, reimagine meaning, and support racial and social justice. The conference, called “Intelligence Augmentation: AI Empowering People to Solve Global Challenges,” took place on 25 March 2021.

[ Stanford HAI ]

This spring 2021 GRASP SFI comes from Monroe Kennedy III at Stanford University, on “Considerations for Human-Robot Collaboration.”

The field of robotics has evolved over the past few decades. We’ve seen robots progress from the automation of repetitive tasks in manufacturing to the autonomy of mobilizing in unstructured environments to the cooperation of swarm robots that are centralized or decentralized. These abilities have required advances in robotic hardware, modeling, and artificial intelligence. The next frontier is robots collaborating in complex tasks with human teammates, in environments traditionally configured for humans. While solutions to this challenge must utilize all the advances of robotics, the human element adds a unique aspect that must be addressed. Collaborating with a human teammate means that the robot must have a contextual understanding of the task as well as all participant’s roles. We will discuss what constitutes an effective teammate and how we can capture this behavior in a robotic collaborator.

[ UPenn ] Continue reading

Posted in Human Robots

#439023 In ‘Klara and the Sun,’ We Glimpse ...

In a store in the center of an unnamed city, humanoid robots are displayed alongside housewares and magazines. They watch the fast-moving world outside the window, anxiously awaiting the arrival of customers who might buy them and take them home. Among them is Klara, a particularly astute robot who loves the sun and wants to learn as much as possible about humans and the world they live in.

So begins Kazuo Ishiguro’s new novel Klara and the Sun, published earlier this month. The book, told from Klara’s perspective, portrays an eerie future society in which intelligent machines and other advanced technologies have been integrated into daily life, but not everyone is happy about it.

Technological unemployment, the progress of artificial intelligence, inequality, the safety and ethics of gene editing, increasing loneliness and isolation—all of which we’re grappling with today—show up in Ishiguro’s world. It’s like he hit a fast-forward button, mirroring back to us how things might play out if we don’t approach these technologies with caution and foresight.

The wealthy genetically edit or “lift” their children to set them up for success, while the poor have to make do with the regular old brains and bodies bequeathed them by evolution. Lifted and unlifted kids generally don’t mix, and this is just one of many sinister delineations between a new breed of haves and have-nots.

There’s anger about robots’ steady infiltration into everyday life, and questions about how similar their rights should be to those of humans. “First they take the jobs. Then they take the seats at the theater?” one woman fumes.

References to “changes” and “substitutions” allude to an economy where automation has eliminated millions of jobs. While “post-employed” people squat in abandoned buildings and fringe communities arm themselves in preparation for conflict, those whose livelihoods haven’t been destroyed can afford to have live-in housekeepers and buy Artificial Friends (or AFs) for their lonely children.

“The old traditional model that we still live with now—where most of us can get some kind of paid work in exchange for our services or the goods we make—has broken down,” Ishiguro said in a podcast discussion of the novel. “We’re not talking just about the difference between rich and poor getting bigger. We’re talking about a gap appearing between people who participate in society in an obvious way and people who do not.”

He has a point; as much as techno-optimists claim that the economic changes brought by automation and AI will give us all more free time, let us work less, and devote time to our passion projects, how would that actually play out? What would millions of “post-employed” people receiving basic income actually do with their time and energy?

In the novel, we don’t get much of a glimpse of this side of the equation, but we do see how the wealthy live. After a long wait, just as the store manager seems ready to give up on selling her, Klara is chosen by a 14-year-old girl named Josie, the daughter of a woman who wears “high-rank clothes” and lives in a large, sunny home outside the city. Cheerful and kind, Josie suffers from an unspecified illness that periodically flares up and leaves her confined to her bed for days at a time.

Her life seems somewhat bleak, the need for an AF clear. In this future world, the children of the wealthy no longer go to school together, instead studying alone at home on their digital devices. “Interaction meetings” are set up for them to learn to socialize, their parents carefully eavesdropping from the next room and trying not to intervene when there’s conflict or hurt feelings.

Klara does her best to be a friend, aide, and confidante to Josie while continuing to learn about the world around her and decode the mysteries of human behavior. We surmise that she was programmed with a basic ability to understand emotions, which evolves along with her other types of intelligence. “I believe I have many feelings. The more I observe, the more feelings become available to me,” she explains to one character.

Ishiguro does an excellent job of representing Klara’s mind: a blend of pre-determined programming, observation, and continuous learning. Her narration has qualities both robotic and human; we can tell when something has been programmed in—she “Gives Privacy” to the humans around her when that’s appropriate, for example—and when she’s figured something out for herself.

But the author maintains some mystery around Klara’s inner emotional life. “Does she actually understand human emotions, or is she just observing human emotions and simulating them within herself?” he said. “I suppose the question comes back to, what are our emotions as human beings? What do they amount to?”

Klara is particularly attuned to human loneliness, since she essentially was made to help prevent it. It is, in her view, peoples’ biggest fear, and something they’ll go to great lengths to avoid, yet can never fully escape. “Perhaps all humans are lonely,” she says.

Warding off loneliness through technology isn’t a futuristic idea, it’s something we’ve been doing for a long time, with the technologies at hand growing more and more sophisticated. Products like AFs already exist. There’s XiaoIce, a chatbot that uses “sentiment analysis” to keep its 660 million users engaged, and Azuma Hikari, a character-based AI designed to “bring comfort” to users whose lives lack emotional connection with other humans.

The mere existence of these tools would be sinister if it wasn’t for their widespread adoption; when millions of people use AIs to fill a void in their lives, it raises deeper questions about our ability to connect with each other and whether technology is building it up or tearing it down.

This isn’t the only big question the novel tackles. An overarching theme is one we’ve been increasingly contemplating as computers start to acquire more complex capabilities, like the beginnings of creativity or emotional awareness: What is it that truly makes us human?

“Do you believe in the human heart?” one character asks. “I don’t mean simply the organ, obviously. I’m speaking in the poetic sense. The human heart. Do you think there is such a thing? Something that makes each of us special and individual?”

The alternative, at least in the story, is that people don’t have a unique essence, but rather we’re all a blend of traits and personalities that can be reduced to strings of code. Our understanding of the brain is still elementary, but at some level, doesn’t all human experience boil down to the firing of billions of neurons between our ears? Will we one day—in a future beyond that painted by Ishiguro, but certainly foreshadowed by it—be able to “decode” our humanity to the point that there’s nothing mysterious left about it? “A human heart is bound to be complex,” Klara says. “But it must be limited.”

Whether or not you agree, Klara and the Sun is worth the read. It’s both a marvelous, engaging story about what it means to love and be human, and a prescient warning to approach technological change with caution and nuance. We’re already living in a world where AI keeps us company, influences our behavior, and is wreaking various forms of havoc. Ishiguro’s novel is a snapshot of one of our possible futures, told through the eyes of a robot who keeps you rooting for her to the end.

Image Credit: Marion Wellmann from Pixabay Continue reading

Posted in Human Robots

#438982 Quantum Computing and Reinforcement ...

Deep reinforcement learning is having a superstar moment.

Powering smarter robots. Simulating human neural networks. Trouncing physicians at medical diagnoses and crushing humanity’s best gamers at Go and Atari. While far from achieving the flexible, quick thinking that comes naturally to humans, this powerful machine learning idea seems unstoppable as a harbinger of better thinking machines.

Except there’s a massive roadblock: they take forever to run. Because the concept behind these algorithms is based on trial and error, a reinforcement learning AI “agent” only learns after being rewarded for its correct decisions. For complex problems, the time it takes an AI agent to try and fail to learn a solution can quickly become untenable.

But what if you could try multiple solutions at once?

This week, an international collaboration led by Dr. Philip Walther at the University of Vienna took the “classic” concept of reinforcement learning and gave it a quantum spin. They designed a hybrid AI that relies on both quantum and run-of-the-mill classic computing, and showed that—thanks to quantum quirkiness—it could simultaneously screen a handful of different ways to solve a problem.

The result is a reinforcement learning AI that learned over 60 percent faster than its non-quantum-enabled peers. This is one of the first tests that shows adding quantum computing can speed up the actual learning process of an AI agent, the authors explained.

Although only challenged with a “toy problem” in the study, the hybrid AI, once scaled, could impact real-world problems such as building an efficient quantum internet. The setup “could readily be integrated within future large-scale quantum communication networks,” the authors wrote.

The Bottleneck
Learning from trial and error comes intuitively to our brains.

Say you’re trying to navigate a new convoluted campground without a map. The goal is to get from the communal bathroom back to your campsite. Dead ends and confusing loops abound. We tackle the problem by deciding to turn either left or right at every branch in the road. One will get us closer to the goal; the other leads to a half hour of walking in circles. Eventually, our brain chemistry rewards correct decisions, so we gradually learn the correct route. (If you’re wondering…yeah, true story.)

Reinforcement learning AI agents operate in a similar trial-and-error way. As a problem becomes more complex, the number—and time—of each trial also skyrockets.

“Even in a moderately realistic environment, it may simply take too long to rationally respond to a given situation,” explained study author Dr. Hans Briegel at the Universität Innsbruck in Austria, who previously led efforts to speed up AI decision-making using quantum mechanics. If there’s pressure that allows “only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all,” he wrote.

Many attempts have tried speeding up reinforcement learning. Giving the AI agent a short-term “memory.” Tapping into neuromorphic computing, which better resembles the brain. In 2014, Briegel and colleagues showed that a “quantum brain” of sorts can help propel an AI agent’s decision-making process after learning. But speeding up the learning process itself has eluded our best attempts.

The Hybrid AI
The new study went straight for that previously untenable jugular.

The team’s key insight was to tap into the best of both worlds—quantum and classical computing. Rather than building an entire reinforcement learning system using quantum mechanics, they turned to a hybrid approach that could prove to be more practical. Here, the AI agent uses quantum weirdness as it’s trying out new approaches—the “trial” in trial and error. The system then passes the baton to a classical computer to give the AI its reward—or not—based on its performance.

At the heart of the quantum “trial” process is a quirk called superposition. Stay with me. Our computers are powered by electrons, which can represent only two states—0 or 1. Quantum mechanics is far weirder, in that photons (particles of light) can simultaneously be both 0 and 1, with a slightly different probability of “leaning towards” one or the other.

This noncommittal oddity is part of what makes quantum computing so powerful. Take our reinforcement learning example of navigating a new campsite. In our classic world, we—and our AI—need to decide between turning left or right at an intersection. In a quantum setup, however, the AI can (in a sense) turn left and right at the same time. So when searching for the correct path back to home base, the quantum system has a leg up in that it can simultaneously explore multiple routes, making it far faster than conventional, consecutive trail and error.

“As a consequence, an agent that can explore its environment in superposition will learn significantly faster than its classical counterpart,” said Briegel.

It’s not all theory. To test out their idea, the team turned to a programmable chip called a nanophotonic processor. Think of it as a CPU-like computer chip, but it processes particles of light—photons—rather than electricity. These light-powered chips have been a long time in the making. Back in 2017, for example, a team from MIT built a fully optical neural network into an optical chip to bolster deep learning.

The chips aren’t all that exotic. Nanophotonic processors act kind of like our eyeglasses, which can carry out complex calculations that transform light that passes through them. In the glasses case, they let people see better. For a light-based computer chip, it allows computation. Rather than using electrical cables, the chips use “wave guides” to shuttle photons and perform calculations based on their interactions.

The “error” or “reward” part of the new hardware comes from a classical computer. The nanophotonic processor is coupled to a traditional computer, where the latter provides the quantum circuit with feedback—that is, whether to reward a solution or not. This setup, the team explains, allows them to more objectively judge any speed-ups in learning in real time.

In this way, a hybrid reinforcement learning agent alternates between quantum and classical computing, trying out ideas in wibbly-wobbly “multiverse” land while obtaining feedback in grounded, classic physics “normality.”

A Quantum Boost
In simulations using 10,000 AI agents and actual experimental data from 165 trials, the hybrid approach, when challenged with a more complex problem, showed a clear leg up.

The key word is “complex.” The team found that if an AI agent has a high chance of figuring out the solution anyway—as for a simple problem—then classical computing works pretty well. The quantum advantage blossoms when the task becomes more complex or difficult, allowing quantum mechanics to fully flex its superposition muscles. For these problems, the hybrid AI was 63 percent faster at learning a solution compared to traditional reinforcement learning, decreasing its learning effort from 270 guesses to 100.

Now that scientists have shown a quantum boost for reinforcement learning speeds, the race for next-generation computing is even more lit. Photonics hardware required for long-range light-based communications is rapidly shrinking, while improving signal quality. The partial-quantum setup could “aid specifically in problems where frequent search is needed, for example, network routing problems” that’s prevalent for a smooth-running internet, the authors wrote. With a quantum boost, reinforcement learning may be able to tackle far more complex problems—those in the real world—than currently possible.

“We are just at the beginning of understanding the possibilities of quantum artificial intelligence,” said lead author Walther.

Image Credit: Oleg Gamulinskiy from Pixabay Continue reading

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