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#437753 iRobot’s New Education Robot Makes ...

iRobot has been on a major push into education robots recently. They acquired Root Robotics in 2019, and earlier this year, launched an online simulator and associated curriculum designed to work in tandem with physical Root robots. The original Root was intended to be a classroom robot, with one of its key features being the ability to stick to (and operate on) magnetic virtual surfaces, like whiteboards. And as a classroom robot, at $200, it’s relatively affordable, if you can buy one or two and have groups of kids share them.

For kids who are more focused on learning at home, though, $200 is a lot for a robot that doesn't even keep your floors clean. And as nice as it is to have a free simulator, any kid will tell you that it’s way cooler to have a real robot to mess around with. Today, iRobot is announcing a new version of Root that’s been redesigned for home use, with a $129 price that makes it significantly more accessible to folks outside of the classroom.

The Root rt0 is a second version of the Root robot—the more expensive, education-grade Root rt1 is still available. To bring the cost down, the rt0 is missing some features that you can still find in the rt1. Specifically, you don’t get the internal magnets to stick the robot to vertical surfaces, there are no cliff sensors, and you don’t get a color scanner or an eraser. But for home use, the internal magnets are probably not necessary anyway, and the rest of that stuff seems like a fair compromise for a cost reduction of 30 percent.

Photo: iRobot

One of the new accessories for the iRobot Root rt0 is a “Brick Top” that snaps onto the upper face the robot via magnets. The accessory can be used with LEGOs and other LEGO-compatible bricks, opening up an enormous amount of customization.

It’s not all just taking away, though. There’s also a new $20 accessory, a LEGO-ish “Brick Top” that snaps onto the upper face of Root (either version) via magnets. The plate can be used with LEGO bricks and other LEGO-compatible things. This opens up an enormous amount of customization, and it’s for more than just decoration, since Root rt0 has the ability to interact with whatever’s on top of it via its actuated marker. Root can move the marker up and down, the idea being that you can programmatically turn lines on and off. By replacing the marker with a plastic thingy that sticks up through the body of the robot, the marker up/down command can be used to actuate something on the brick top. In the video, that’s what triggers the catapult.

Photo: iRobot

By attaching a marker, you can program Root to draw. The robot has a motor that can move the marker up and down.

This less expensive version of Root still has access to the online simulator, as well as the multi-level coding interface that allows kids to seamlessly transition through multiple levels of coding complexity, from graphical to text. There’s a new Android app coming out today, and you can access everything through web-based apps on Chrome OS, Windows and macOS, as well as on iOS. iRobot tells us that they’ve also recently expanded their online learning library full of Root-based educational activities. In particular, they’ve added a new category on “Social Emotional Learning,” the goal of which is to help kids develop things like social awareness, self-management, decision making, and relationship skills. We’re not quite sure how you teach those things with a little hexagonal robot, but we like that iRobot is giving it a try.

Root coding robots are designed for kids age 6 and up, ships for free, and is available now.

[ iRobot Root ] Continue reading

Posted in Human Robots

#437709 iRobot Announces Major Software Update, ...

Since the release of the very first Roomba in 2002, iRobot’s long-term goal has been to deliver cleaner floors in a way that’s effortless and invisible. Which sounds pretty great, right? And arguably, iRobot has managed to do exactly this, with its most recent generation of robot vacuums that make their own maps and empty their own dustbins. For those of us who trust our robots, this is awesome, but iRobot has gradually been realizing that many Roomba users either don’t want this level of autonomy, or aren’t ready for it.

Today, iRobot is announcing a major new update to its app that represents a significant shift of its overall approach to home robot autonomy. Humans are being brought back into the loop through software that tries to learn when, where, and how you clean so that your Roomba can adapt itself to your life rather than the other way around.

To understand why this is such a shift for iRobot, let’s take a very brief look back at how the Roomba interface has evolved over the last couple of decades. The first generation of Roomba had three buttons on it that allowed (or required) the user to select whether the room being vacuumed was small or medium or large in size. iRobot ditched that system one generation later, replacing the room size buttons with one single “clean” button. Programmable scheduling meant that users no longer needed to push any buttons at all, and with Roombas able to find their way back to their docking stations, all you needed to do was empty the dustbin. And with the most recent few generations (the S and i series), the dustbin emptying is also done for you, reducing direct interaction with the robot to once a month or less.

Image: iRobot

iRobot CEO Colin Angle believes that working toward more intelligent human-robot collaboration is “the brave new frontier” of AI. “This whole journey has been earning the right to take this next step, because a robot can’t be responsive if it’s incompetent,” he says. “But thinking that autonomy was the destination was where I was just completely wrong.”

The point that the top-end Roombas are at now reflects a goal that iRobot has been working toward since 2002: With autonomy, scheduling, and the clean base to empty the bin, you can set up your Roomba to vacuum when you’re not home, giving you cleaner floors every single day without you even being aware that the Roomba is hard at work while you’re out. It’s not just hands-off, it’s brain-off. No noise, no fuss, just things being cleaner thanks to the efforts of a robot that does its best to be invisible to you. Personally, I’ve been completely sold on this idea for home robots, and iRobot CEO Colin Angle was as well.

“I probably told you that the perfect Roomba is the Roomba that you never see, you never touch, you just come home everyday and it’s done the right thing,” Angle told us. “But customers don’t want that—they want to be able to control what the robot does. We started to hear this a couple years ago, and it took a while before it sunk in, but it made sense.”

How? Angle compares it to having a human come into your house to clean, but you weren’t allowed to tell them where or when to do their job. Maybe after a while, you’ll build up the amount of trust necessary for that to work, but in the short term, it would likely be frustrating. And people get frustrated with their Roombas for this reason. “The desire to have more control over what the robot does kept coming up, and for me, it required a pretty big shift in my view of what intelligence we were trying to build. Autonomy is not intelligence. We need to do something more.”

That something more, Angle says, is a partnership as opposed to autonomy. It’s an acknowledgement that not everyone has the same level of trust in robots as the people who build them. It’s an understanding that people want to have a feeling of control over their homes, that they have set up the way that they want, and that they’ve been cleaning the way that they want, and a robot shouldn’t just come in and do its own thing.

This change in direction also represents a substantial shift in resources for iRobot, and the company has pivoted two-thirds of its engineering organization to focus on software-based collaborative intelligence rather than hardware.

“Until the robot proves that it knows enough about your home and about the way that you want your home cleaned,” Angle says, “you can’t move forward.” He adds that this is one of those things that seem obvious in retrospect, but even if they’d wanted to address the issue before, they didn’t have the technology to solve the problem. Now they do. “This whole journey has been earning the right to take this next step, because a robot can’t be responsive if it’s incompetent,” Angle says. “But thinking that autonomy was the destination was where I was just completely wrong.”

The previous iteration of the iRobot app (and Roombas themselves) are built around one big fat CLEAN button. The new approach instead tries to figure out in much more detail where the robot should clean, and when, using a mixture of autonomous technology and interaction with the user.

Where to Clean
Knowing where to clean depends on your Roomba having a detailed and accurate map of its environment. For several generations now, Roombas have been using visual mapping and localization (VSLAM) to build persistent maps of your home. These maps have been used to tell the Roomba to clean in specific rooms, but that’s about it. With the new update, Roombas with cameras will be able to recognize some objects and features in your home, including chairs, tables, couches, and even countertops. The robots will use these features to identify where messes tend to happen so that they can focus on those areas—like around the dining room table or along the front of the couch.

We should take a minute here to clarify how the Roomba is using its camera. The original (primary?) purpose of the camera was for VSLAM, where the robot would take photos of your home, downsample them into QR-code-like patterns of light and dark, and then use those (with the assistance of other sensors) to navigate. Now the camera is also being used to take pictures of other stuff around your house to make that map more useful.

Photo: iRobot

The robots will now try to fit into the kinds of cleaning routines that many people already have established. For example, the app may suggest an “after dinner” routine that cleans just around the kitchen and dining room table.

This is done through machine learning using a library of images of common household objects from a floor perspective that iRobot had to develop from scratch. Angle clarified for us that this is all done via a neural net that runs on the robot, and that “no recognizable images are ever stored on the robot or kept, and no images ever leave the robot.” Worst case, if all the data iRobot has about your home gets somehow stolen, the hacker would only know that (for example) your dining room has a table in it and the approximate size and location of that table, because the map iRobot has of your place only stores symbolic representations rather than images.

Another useful new feature is intended to help manage the “evil Roomba places” (as Angle puts it) that every home has that cause Roombas to get stuck. If the place is evil enough that Roomba has to call you for help because it gave up completely, Roomba will now remember, and suggest that either you make some changes or that it stops cleaning there, which seems reasonable.

When to Clean
It turns out that the primary cause of mission failure for Roombas is not that they get stuck or that they run out of battery—it’s user cancellation, usually because the robot is getting in the way or being noisy when you don’t want it to be. “If you kill a Roomba’s job because it annoys you,” points out Angle, “how is that robot being a good partner? I think it’s an epic fail.” Of course, it’s not the robot’s fault, because Roombas only clean when we tell them to, which Angle says is part of the problem. “People actually aren’t very good at making their own schedules—they tend to oversimplify, and not think through what their schedules are actually about, which leads to lots of [figurative] Roomba death.”

To help you figure out when the robot should actually be cleaning, the new app will look for patterns in when you ask the robot to clean, and then recommend a schedule based on those patterns. That might mean the robot cleans different areas at different times every day of the week. The app will also make scheduling recommendations that are event-based as well, integrated with other smart home devices. Would you prefer the Roomba to clean every time you leave the house? The app can integrate with your security system (or garage door, or any number of other things) and take care of that for you.

More generally, Roomba will now try to fit into the kinds of cleaning routines that many people already have established. For example, the app may suggest an “after dinner” routine that cleans just around the kitchen and dining room table. The app will also, to some extent, pay attention to the environment and season. It might suggest increasing your vacuuming frequency if pollen counts are especially high, or if it’s pet shedding season and you have a dog. Unfortunately, Roomba isn’t (yet?) capable of recognizing dogs on its own, so the app has to cheat a little bit by asking you some basic questions.

A Smarter App

Image: iRobot

The previous iteration of the iRobot app (and Roombas themselves) are built around one big fat CLEAN button. The new approach instead tries to figure out in much more detail where the robot should clean, and when, using a mixture of autonomous technology and interaction with the user.

The app update, which should be available starting today, is free. The scheduling and recommendations will work on every Roomba model, although for object recognition and anything related to mapping, you’ll need one of the more recent and fancier models with a camera. Future app updates will happen on a more aggressive schedule. Major app releases should happen every six months, with incremental updates happening even more frequently than that.

Angle also told us that overall, this change in direction also represents a substantial shift in resources for iRobot, and the company has pivoted two-thirds of its engineering organization to focus on software-based collaborative intelligence rather than hardware. “It’s not like we’re done doing hardware,” Angle assured us. “But we do think about hardware differently. We view our robots as platforms that have longer life cycles, and each platform will be able to support multiple generations of software. We’ve kind of decoupled robot intelligence from hardware, and that’s a change.”

Angle believes that working toward more intelligent collaboration between humans and robots is “the brave new frontier of artificial intelligence. I expect it to be the frontier for a reasonable amount of time to come,” he adds. “We have a lot of work to do to create the type of easy-to-use experience that consumer robots need.” Continue reading

Posted in Human Robots

#437579 Disney Research Makes Robotic Gaze ...

While it’s not totally clear to what extent human-like robots are better than conventional robots for most applications, one area I’m personally comfortable with them is entertainment. The folks over at Disney Research, who are all about entertainment, have been working on this sort of thing for a very long time, and some of their animatronic attractions are actually quite impressive.

The next step for Disney is to make its animatronic figures, which currently feature scripted behaviors, to perform in an interactive manner with visitors. The challenge is that this is where you start to get into potential Uncanny Valley territory, which is what happens when you try to create “the illusion of life,” which is what Disney (they explicitly say) is trying to do.

In a paper presented at IROS this month, a team from Disney Research, Caltech, University of Illinois at Urbana-Champaign, and Walt Disney Imagineering is trying to nail that illusion of life with a single, and perhaps most important, social cue: eye gaze.

Before you watch this video, keep in mind that you’re watching a specific character, as Disney describes:

The robot character plays an elderly man reading a book, perhaps in a library or on a park bench. He has difficulty hearing and his eyesight is in decline. Even so, he is constantly distracted from reading by people passing by or coming up to greet him. Most times, he glances at people moving quickly in the distance, but as people encroach into his personal space, he will stare with disapproval for the interruption, or provide those that are familiar to him with friendly acknowledgment.

What, exactly, does “lifelike” mean in the context of robotic gaze? The paper abstract describes the goal as “[seeking] to create an interaction which demonstrates the illusion of life.” I suppose you could think of it like a sort of old-fashioned Turing test focused on gaze: If the gaze of this robot cannot be distinguished from the gaze of a human, then victory, that’s lifelike. And critically, we’re talking about mutual gaze here—not just a robot gazing off into the distance, but you looking deep into the eyes of this robot and it looking right back at you just like a human would. Or, just like some humans would.

The approach that Disney is using is more animation-y than biology-y or psychology-y. In other words, they’re not trying to figure out what’s going on in our brains to make our eyes move the way that they do when we’re looking at other people and basing their control system on that, but instead, Disney just wants it to look right. This “visual appeal” approach is totally fine, and there’s been an enormous amount of human-robot interaction (HRI) research behind it already, albeit usually with less explicitly human-like platforms. And speaking of human-like platforms, the hardware is a “custom Walt Disney Imagineering Audio-Animatronics bust,” which has DoFs that include neck, eyes, eyelids, and eyebrows.

In order to decide on gaze motions, the system first identifies a person to target with its attention using an RGB-D camera. If more than one person is visible, the system calculates a curiosity score for each, currently simplified to be based on how much motion it sees. Depending on which person that the robot can see has the highest curiosity score, the system will choose from a variety of high level gaze behavior states, including:

Read: The Read state can be considered the “default” state of the character. When not executing another state, the robot character will return to the Read state. Here, the character will appear to read a book located at torso level.

Glance: A transition to the Glance state from the Read or Engage states occurs when the attention engine indicates that there is a stimuli with a curiosity score […] above a certain threshold.

Engage: The Engage state occurs when the attention engine indicates that there is a stimuli […] to meet a threshold and can be triggered from both Read and Glance states. This state causes the robot to gaze at the person-of-interest with both the eyes and head.

Acknowledge: The Acknowledge state is triggered from either Engage or Glance states when the person-of-interest is deemed to be familiar to the robot.

Running underneath these higher level behavior states are lower level motion behaviors like breathing, small head movements, eye blinking, and saccades (the quick eye movements that occur when people, or robots, look between two different focal points). The term for this hierarchical behavioral state layering is a subsumption architecture, which goes all the way back to Rodney Brooks’ work on robots like Genghis in the 1980s and Cog and Kismet in the ’90s, and it provides a way for more complex behaviors to emerge from a set of simple, decentralized low-level behaviors.

“25 years on Disney is using my subsumption architecture for humanoid eye control, better and smoother now than our 1995 implementations on Cog and Kismet.”
—Rodney Brooks, MIT emeritus professor

Brooks, an emeritus professor at MIT and, most recently, cofounder and CTO of Robust.ai, tweeted about the Disney project, saying: “People underestimate how long it takes to get from academic paper to real world robotics. 25 years on Disney is using my subsumption architecture for humanoid eye control, better and smoother now than our 1995 implementations on Cog and Kismet.”

From the paper:

Although originally intended for control of mobile robots, we find that the subsumption architecture, as presented in [17], lends itself as a framework for organizing animatronic behaviors. This is due to the analogous use of subsumption in human behavior: human psychomotor behavior can be intuitively modeled as layered behaviors with incoming sensory inputs, where higher behavioral levels are able to subsume lower behaviors. At the lowest level, we have involuntary movements such as heartbeats, breathing and blinking. However, higher behavioral responses can take over and control lower level behaviors, e.g., fight-or-flight response can induce faster heart rate and breathing. As our robot character is modeled after human morphology, mimicking biological behaviors through the use of a bottom-up approach is straightforward.

The result, as the video shows, appears to be quite good, although it’s hard to tell how it would all come together if the robot had more of, you know, a face. But it seems like you don’t necessarily need to have a lifelike humanoid robot to take advantage of this architecture in an HRI context—any robot that wants to make a gaze-based connection with a human could benefit from doing it in a more human-like way.

“Realistic and Interactive Robot Gaze,” by Matthew K.X.J. Pan, Sungjoon Choi, James Kennedy, Kyna McIntosh, Daniel Campos Zamora, Gunter Niemeyer, Joohyung Kim, Alexis Wieland, and David Christensen from Disney Research, California Institute of Technology, University of Illinois at Urbana-Champaign, and Walt Disney Imagineering, was presented at IROS 2020. You can find the full paper, along with a 13-minute video presentation, on the IROS on-demand conference website.

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Posted in Human Robots

#437460 This Week’s Awesome Tech Stories From ...

ARTIFICIAL INTELLIGENCE
A Radical New Technique Lets AI Learn With Practically No Data
Karen Hao | MIT Technology Review
“Shown photos of a horse and a rhino, and told a unicorn is something in between, [children] can recognize the mythical creature in a picture book the first time they see it. …Now a new paper from the University of Waterloo in Ontario suggests that AI models should also be able to do this—a process the researchers call ‘less than one’-shot, or LO-shot, learning.”

FUTURE
Artificial General Intelligence: Are We Close, and Does It Even Make Sense to Try?
Will Douglas Heaven | MIT Technology Review
“A machine that could think like a person has been the guiding vision of AI research since the earliest days—and remains its most divisive idea. …So why is AGI controversial? Why does it matter? And is it a reckless, misleading dream—or the ultimate goal?”

HEALTH
The Race for a Super-Antibody Against the Coronavirus
Apoorva Mandavilli | The New York Times
“Dozens of companies and academic groups are racing to develop antibody therapies. …But some scientists are betting on a dark horse: Prometheus, a ragtag group of scientists who are months behind in the competition—and yet may ultimately deliver the most powerful antibody.”

SPACE
How to Build a Spacecraft to Save the World
Daniel Oberhaus | Wired
“The goal of the Double Asteroid Redirection Test, or DART, is to slam the [spacecraft] into a small asteroid orbiting a larger asteroid 7 million miles from Earth. …It should be able to change the asteroid’s orbit just enough to be detectable from Earth, demonstrating that this kind of strike could nudge an oncoming threat out of Earth’s way. Beyond that, everything is just an educated guess, which is exactly why NASA needs to punch an asteroid with a robot.”

TRANSPORTATION
Inside Gravity’s Daring Mission to Make Jetpacks a Reality
Oliver Franklin-Wallis | Wired
“The first time someone flies a jetpack, a curious thing happens: just as their body leaves the ground, their legs start to flail. …It’s as if the vestibular system can’t quite believe what’s happening. This isn’t natural. Then suddenly, thrust exceeds weight, and—they’re aloft. …It’s that moment, lift-off, that has given jetpacks an enduring appeal for over a century.”

FUTURE OF FOOD
Inside Singapore’s Huge Bet on Vertical Farming
Megan Tatum | MIT Technology Review
“…to cram all [of Singapore’s] gleaming towers and nearly 6 million people into a land mass half the size of Los Angeles, it has sacrificed many things, including food production. Farms make up no more than 1% of its total land (in the United States it’s 40%), forcing the small city-state to shell out around $10 billion each year importing 90% of its food. Here was an example of technology that could change all that.”

COMPUTING
The Effort to Build the Mathematical Library of the Future
Kevin Hartnett | Quanta
“Digitizing mathematics is a longtime dream. The expected benefits range from the mundane—computers grading students’ homework—to the transcendent: using artificial intelligence to discover new mathematics and find new solutions to old problems.”

Image credit: Kevin Mueller / Unsplash 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.

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