Tag Archives: win
#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
#439036 Video Friday: Shadow Plays Jenga, and ...
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.
The Shadow Robot team couldn't resist! Our Operator, Joanna, is using the Shadow Teleoperation System which, fun and games aside, can help those in difficult, dangerous and distant jobs.
Shadow could challenge this MIT Jenga-playing robot, but I bet they wouldn't win:
[ Shadow Robot ]
Digit is gradually stomping the Agility Robotics logo into a big grassy field fully autonomously.
[ Agility Robotics ]
This is a pretty great and very short robotic magic show.
[ Mario the Magician ]
A research team at the Georgia Institute of Technology has developed a modular solution for drone delivery of larger packages without the need for a complex fleet of drones of varying sizes. By allowing teams of small drones to collaboratively lift objects using an adaptive control algorithm, the strategy could allow a wide range of packages to be delivered using a combination of several standard-sized vehicles.
[ GA Tech ]
I've seen this done using vision before, but Flexiv's Rizon 4s can keep a ball moving along a specific trajectory using only force sensing and control.
[ Flexiv ]
Thanks Yunfan!
This combination of a 3D aerial projection system and a sensing interface can be used as an interactive and intuitive control system for things like robot arms, but in this case, it's being used to make simulated pottery. Much less messy than the traditional way of doing it.
More details on Takafumi Matsumaru's work at the Bio-Robotics & Human-Mechatronics Laboratory at Waseda University at the link below.
[ BLHM ]
U.S. Vice President Kamala Harris called astronauts Shannon Walker and Kate Rubins on the ISS, and they brought up Astrobee, at which point Shannon reaches over and rips Honey right off of her charging dock to get her on camera.
[ NASA ]
Here's a quick three minute update on Perseverance and Ingenuity from JPL.
[ Mars 2020 ]
Rigid grippers used in existing aerial manipulators require precise positioning to achieve successful grasps and transmit large contact forces that may destabilize the drone. This limits the speed during grasping and prevents “dynamic grasping,” where the drone attempts to grasp an object while moving. On the other hand, biological systems (e.g. birds) rely on compliant and soft parts to dampen contact forces and compensate for grasping inaccuracy, enabling impressive feats. This paper presents the first prototype of a soft drone—a quadrotor where traditional (i.e. rigid) landing gears are replaced with a soft tendon-actuated gripper to enable aggressive grasping.
[ MIT ]
In this video we present results from a field deployment inside the Løkken Mine underground pyrite mine in Norway. The Løkken mine was operative from 1654 to 1987 and contains narrow but long corridors, alongside vast rooms and challenging vertical stopes. In this field study we evaluated selected autonomous exploration and visual search capabilities of a subset of the aerial robots of Team CERBERUS towards the goal of complete subterranean autonomy.
[ Team CERBERUS ]
What you can do with a 1,000 FPS projector with a high speed tracking system.
[ Ishikawa Group ]
ANYbotics’ collaboration with BASF, one of the largest global chemical manufacturers, displays the efficiency, quality, and scalability of robotic inspection and data-collection capabilities in complex industrial environments.
[ ANYbotics ]
Does your robot arm need a stylish jacket?
[ Fraunhofer ]
Trossen Robotics unboxes a Unitree A1, and it's actually an unboxing where they have to figure out everything from scratch.
[ Trossen ]
Robots have learned to drive cars, assist in surgeries―and vacuum our floors. But can they navigate the unwritten rules of a busy sidewalk? Until they can, robotics experts Leila Takayama and Chris Nicholson believe, robots won’t be able to fulfill their immense potential. In this conversation, Chris and Leila explore the future of robotics and the role open source will play in it.
[ Red Hat ]
Christoph Bartneck's keynote at the 6th Joint UAE Symposium on Social Robotics, focusing on what roles robots can play during the Covid crisis and why so many social robots fail in the market.
[ HIT Lab ]
Decision-making based on arbitrary criteria is legal in some contexts, such as employment, and not in others, such as criminal sentencing. As algorithms replace human deciders, HAI-EIS fellow Kathleen Creel argues arbitrariness at scale is morally and legally problematic. In this HAI seminar, she explains how the heart of this moral issue relates to domination and a lack of sufficient opportunity for autonomy. It relates in interesting ways to the moral wrong of discrimination. She proposes technically informed solutions that can lessen the impact of algorithms at scale and so mitigate or avoid the moral harm identified.
[ Stanford HAI ]
Sawyer B. Fuller speaks on Autonomous Insect-Sized Robots at the UC Berkeley EECS Colloquium series.
Sub-gram (insect-sized) robots have enormous potential that is largely untapped. From a research perspective, their extreme size, weight, and power (SWaP) constraints also forces us to reimagine everything from how they compute their control laws to how they are fabricated. These questions are the focus of the Autonomous Insect Robotics Laboratory at the University of Washington. I will discuss potential applications for insect robots and recent advances from our group. These include the first wireless flights of a sub-gram flapping-wing robot that weighs barely more than a toothpick. I will describe efforts to expand its capabilities, including the first multimodal ground-flight locomotion, the first demonstration of steering control, and how to find chemical plume sources by integrating the smelling apparatus of a live moth. I will also describe a backpack for live beetles with a steerable camera and conceptual design of robots that could scale all the way down to the “gnat robots” first envisioned by Flynn & Brooks in the ‘80s.
[ UC Berkeley ]
Thanks Fan!
Joshua Vander Hook, Computer Scientist, NIAC Fellow, and Technical Group Supervisor at NASA JPL, presents an overview of the AI Group(s) at JPL, and recent work on single and multi-agent autonomous systems supporting space exploration, Earth science, NASA technology development, and national defense programs.
[ UMD ] Continue reading →
Posted in Human Robots
#438801 This AI Thrashes the Hardest Atari Games ...
Learning from rewards seems like the simplest thing. I make coffee, I sip coffee, I’m happy. My brain registers “brewing coffee” as an action that leads to a reward.
That’s the guiding insight behind deep reinforcement learning, a family of algorithms that famously smashed most of Atari’s gaming catalog and triumphed over humans in strategy games like Go. Here, an AI “agent” explores the game, trying out different actions and registering ones that let it win.
Except it’s not that simple. “Brewing coffee” isn’t one action; it’s a series of actions spanning several minutes, where you’re only rewarded at the very end. By just tasting the final product, how do you learn to fine-tune grind coarseness, water to coffee ratio, brewing temperature, and a gazillion other factors that result in the reward—tasty, perk-me-up coffee?
That’s the problem with “sparse rewards,” which are ironically very abundant in our messy, complex world. We don’t immediately get feedback from our actions—no video-game-style dings or points for just grinding coffee beans—yet somehow we’re able to learn and perform an entire sequence of arm and hand movements while half-asleep.
This week, researchers from UberAI and OpenAI teamed up to bestow this talent on AI.
The trick is to encourage AI agents to “return” to a previous step, one that’s promising for a winning solution. The agent then keeps a record of that state, reloads it, and branches out again to intentionally explore other solutions that may have been left behind on the first go-around. Video gamers are likely familiar with this idea: live, die, reload a saved point, try something else, repeat for a perfect run-through.
The new family of algorithms, appropriately dubbed “Go-Explore,” smashed notoriously difficult Atari games like Montezuma’s Revenge that were previously unsolvable by its AI predecessors, while trouncing human performance along the way.
It’s not just games and digital fun. In a computer simulation of a robotic arm, the team found that installing Go-Explore as its “brain” allowed it to solve a challenging series of actions when given very sparse rewards. Because the overarching idea is so simple, the authors say, it can be adapted and expanded to other real-world problems, such as drug design or language learning.
Growing Pains
How do you reward an algorithm?
Rewards are very hard to craft, the authors say. Take the problem of asking a robot to go to a fridge. A sparse reward will only give the robot “happy points” if it reaches its destination, which is similar to asking a baby, with no concept of space and danger, to crawl through a potential minefield of toys and other obstacles towards a fridge.
“In practice, reinforcement learning works very well, if you have very rich feedback, if you can tell, ‘hey, this move is good, that move is bad, this move is good, that move is bad,’” said study author Joost Huinzinga. However, in situations that offer very little feedback, “rewards can intentionally lead to a dead end. Randomly exploring the space just doesn’t cut it.”
The other extreme is providing denser rewards. In the same robot-to-fridge example, you could frequently reward the bot as it goes along its journey, essentially helping “map out” the exact recipe to success. But that’s troubling as well. Over-holding an AI’s hand could result in an extremely rigid robot that ignores new additions to its path—a pet, for example—leading to dangerous situations. It’s a deceptive AI solution that seems effective in a simple environment, but crashes in the real world.
What we need are AI agents that can tackle both problems, the team said.
Intelligent Exploration
The key is to return to the past.
For AI, motivation usually comes from “exploring new or unusual situations,” said Huizinga. It’s efficient, but comes with significant downsides. For one, the AI agent could prematurely stop going back to promising areas because it thinks it had already found a good solution. For another, it could simply forget a previous decision point because of the mechanics of how it probes the next step in a problem.
For a complex task, the end result is an AI that randomly stumbles around towards a solution while ignoring potentially better ones.
“Detaching from a place that was previously visited after collecting a reward doesn’t work in difficult games, because you might leave out important clues,” Huinzinga explained.
Go-Explore solves these problems with a simple principle: first return, then explore. In essence, the algorithm saves different approaches it previously tried and loads promising save points—once more likely to lead to victory—to explore further.
Digging a bit deeper, the AI stores screen caps from a game. It then analyzes saved points and groups images that look alike as a potential promising “save point” to return to. Rinse and repeat. The AI tries to maximize its final score in the game, and updates its save points when it achieves a new record score. Because Atari doesn’t usually allow people to revisit any random point, the team used an emulator, which is a kind of software that mimics the Atari system but with custom abilities such as saving and reloading at any time.
The trick worked like magic. When pitted against 55 Atari games in the OpenAI gym, now commonly used to benchmark reinforcement learning algorithms, Go-Explore knocked out state-of-the-art AI competitors over 85 percent of the time.
It also crushed games previously unbeatable by AI. Montezuma’s Revenge, for example, requires you to move Pedro, the blocky protagonist, through a labyrinth of underground temples while evading obstacles such as traps and enemies and gathering jewels. One bad jump could derail the path to the next level. It’s a perfect example of sparse rewards: you need a series of good actions to get to the reward—advancing onward.
Go-Explore didn’t just beat all levels of the game, a first for AI. It also scored higher than any previous record for reinforcement learning algorithms at lower levels while toppling the human world record.
Outside a gaming environment, Go-Explore was also able to boost the performance of a simulated robot arm. While it’s easy for humans to follow high-level guidance like “put the cup on this shelf in a cupboard,” robots often need explicit training—from grasping the cup to recognizing a cupboard, moving towards it while avoiding obstacles, and learning motions to not smash the cup when putting it down.
Here, similar to the real world, the digital robot arm was only rewarded when it placed the cup onto the correct shelf, out of four possible shelves. When pitted against another algorithm, Go-Explore quickly figured out the movements needed to place the cup, while its competitor struggled with even reliably picking the cup up.
Combining Forces
By itself, the “first return, then explore” idea behind Go-Explore is already powerful. The team thinks it can do even better.
One idea is to change the mechanics of save points. Rather than reloading saved states through the emulator, it’s possible to train a neural network to do the same, without needing to relaunch a saved state. It’s a potential way to make the AI even smarter, the team said, because it can “learn” to overcome one obstacle once, instead of solving the same problem again and again. The downside? It’s much more computationally intensive.
Another idea is to combine Go-Explore with an alternative form of learning, called “imitation learning.” Here, an AI observes human behavior and mimics it through a series of actions. Combined with Go-Explore, said study author Adrien Ecoffet, this could make more robust robots capable of handling all the complexity and messiness in the real world.
To the team, the implications go far beyond Go-Explore. The concept of “first return, then explore” seems to be especially powerful, suggesting “it may be a fundamental feature of learning in general.” The team said, “Harnessing these insights…may be essential…to create generally intelligent agents.”
Image Credit: Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, and Jeff Clune Continue reading →
Posted in Human Robots
#437357 Algorithms Workers Can’t See Are ...
“I’m sorry, Dave. I’m afraid I can’t do that.” HAL’s cold, if polite, refusal to open the pod bay doors in 2001: A Space Odyssey has become a defining warning about putting too much trust in artificial intelligence, particularly if you work in space.
In the movies, when a machine decides to be the boss (or humans let it) things go wrong. Yet despite myriad dystopian warnings, control by machines is fast becoming our reality.
Algorithms—sets of instructions to solve a problem or complete a task—now drive everything from browser search results to better medical care.
They are helping design buildings. They are speeding up trading on financial markets, making and losing fortunes in micro-seconds. They are calculating the most efficient routes for delivery drivers.
In the workplace, self-learning algorithmic computer systems are being introduced by companies to assist in areas such as hiring, setting tasks, measuring productivity, evaluating performance, and even terminating employment: “I’m sorry, Dave. I’m afraid you are being made redundant.”
Giving self‐learning algorithms the responsibility to make and execute decisions affecting workers is called “algorithmic management.” It carries a host of risks in depersonalizing management systems and entrenching pre-existing biases.
At an even deeper level, perhaps, algorithmic management entrenches a power imbalance between management and worker. Algorithms are closely guarded secrets. Their decision-making processes are hidden. It’s a black-box: perhaps you have some understanding of the data that went in, and you see the result that comes out, but you have no idea of what goes on in between.
Algorithms at Work
Here are a few examples of algorithms already at work.
At Amazon’s fulfillment center in south-east Melbourne, they set the pace for “pickers,” who have timers on their scanners showing how long they have to find the next item. As soon as they scan that item, the timer resets for the next. All at a “not quite walking, not quite running” speed.
Or how about AI determining your success in a job interview? More than 700 companies have trialed such technology. US developer HireVue says its software speeds up the hiring process by 90 percent by having applicants answer identical questions and then scoring them according to language, tone, and facial expressions.
Granted, human assessments during job interviews are notoriously flawed. Algorithms,however, can also be biased. The classic example is the COMPAS software used by US judges, probation, and parole officers to rate a person’s risk of re-offending. In 2016 a ProPublica investigation showed the algorithm was heavily discriminatory, incorrectly classifying black subjects as higher risk 45 percent of the time, compared with 23 percent for white subjects.
How Gig Workers Cope
Algorithms do what their code tells them to do. The problem is this code is rarely available. This makes them difficult to scrutinize, or even understand.
Nowhere is this more evident than in the gig economy. Uber, Lyft, Deliveroo, and other platforms could not exist without algorithms allocating, monitoring, evaluating, and rewarding work.
Over the past year Uber Eats’ bicycle couriers and drivers, for instance, have blamed unexplained changes to the algorithm for slashing their jobs, and incomes.
Rider’s can’t be 100 percent sure it was all down to the algorithm. But that’s part of the problem. The fact those who depend on the algorithm don’t know one way or the other has a powerful influence on them.
This is a key result from our interviews with 58 food-delivery couriers. Most knew their jobs were allocated by an algorithm (via an app). They knew the app collected data. What they didn’t know was how data was used to award them work.
In response, they developed a range of strategies (or guessed how) to “win” more jobs, such as accepting gigs as quickly as possible and waiting in “magic” locations. Ironically, these attempts to please the algorithm often meant losing the very flexibility that was one of the attractions of gig work.
The information asymmetry created by algorithmic management has two profound effects. First, it threatens to entrench systemic biases, the type of discrimination hidden within the COMPAS algorithm for years. Second, it compounds the power imbalance between management and worker.
Our data also confirmed others’ findings that it is almost impossible to complain about the decisions of the algorithm. Workers often do not know the exact basis of those decisions, and there’s no one to complain to anyway. When Uber Eats bicycle couriers asked for reasons about their plummeting income, for example, responses from the company advised them “we have no manual control over how many deliveries you receive.”
Broader Lessons
When algorithmic management operates as a “black box” one of the consequences is that it is can become an indirect control mechanism. Thus far under-appreciated by Australian regulators, this control mechanism has enabled platforms to mobilize a reliable and scalable workforce while avoiding employer responsibilities.
“The absence of concrete evidence about how the algorithms operate”, the Victorian government’s inquiry into the “on-demand” workforce notes in its report, “makes it hard for a driver or rider to complain if they feel disadvantaged by one.”
The report, published in June, also found it is “hard to confirm if concern over algorithm transparency is real.”
But it is precisely the fact it is hard to confirm that’s the problem. How can we start to even identify, let alone resolve, issues like algorithmic management?
Fair conduct standards to ensure transparency and accountability are a start. One example is the Fair Work initiative, led by the Oxford Internet Institute. The initiative is bringing together researchers with platforms, workers, unions, and regulators to develop global principles for work in the platform economy. This includes “fair management,” which focuses on how transparent the results and outcomes of algorithms are for workers.
Understandings about impact of algorithms on all forms of work is still in its infancy. It demands greater scrutiny and research. Without human oversight based on agreed principles we risk inviting HAL into our workplaces.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Image Credit: PickPik 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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