"Stochastic" is another way of saying random, and stochastic robots are robots that harness the powers of randomness to construct themselves. It's a fairly simple idea that can result in fairly complex objects: you've got some number of different modules, which can come together to form a robot. Instead of putting the modules together and building the robot directly, you instead just toss all of the modules and shake it really really hard. As the modules randomly run into each other, each is programed to latch on if it happens to bump into a module that it's supposed to be next to in the final design. And if you do this for long enough, eventually you'll end up with a fully assembled robot. Or that's the basic idea, anyway.
The following video demonstrates an interesting application of this concept. Along with lots of assembling modules come a few disassembling modules, whose job is to break up the assembled robots. This creates a system that's sort of a robotic chemical reaction, and by adjusting how long the disassembling bots take to recharge themselves, the overall number of functional robots can be controlled:
One application for these types of robots might be in the medical field, where building a robot inside someone's body could prove to be much more effective than building one outside. All you have to do is inject a bunch of little modules into the bloodstream, they'd randomly whirl about and run into each other and grab on where appropriate, and in a little bit you'd have your robot. You could even program the modules not to assemble themselves until they reached a certain place in the body, and while such precision might take a while (or a whole bunch of injections), the potential is there for extremely precise treatments and repairs.
Next time you need heart surgery, this little snakebot is going to make himself right at home deep inside your chest via a small hole in your solar plexus. It's CardioARM, and don't panic, he's here to help. Developed by CMU's Howie Choset, CardioARM has 102 joints (plus a camera for a head) and can be directed to slither around your vital organs with the utmost precision, making it unnecessary to 'crack open your chest,' which is apparently what they normally do when your ticker needs an overhaul.
Last February, CardioARM was successfully tested on a human for the first time, performing a diagnostic heart mapping procedure, which sounds like it was probably a pile o' fun for everyone involved. Dr. Choset has bigger plans for his snakebots, though:
"He hopes to test the device in other surgeries, such as ablation—which involves burning away a small amount of heart muscle to correct an abnormal beat."
Burning? Burning, you say? What, with lasers? We're giving these flesh-burrowing robot snakes lasers now? What else?!
“We’re hoping to use a remote-controlled robot to go through small caves in Egypt,” [Choset] says, “and find remains of ancient Egyptian tombs.”
Snakebots. Lasers. Ancient Egyptian tombs. Wow, I smell a blockbuster...
After testing iPhone, iPad and an eye-tracking device as possible user interfaces to maneuver our research car, named "MadeInGermany," we now also use Brain Power. The "BrainDriver" application is of course a demonstration and not roadworthy yet, but in the long run human-machine interfaces like this could bear huge potential in combination with autonomous driving.
To record brain activity, the researchers use an Emotiv "neuroheadset," an electroencephalography, or EEG, sensor by San Francisco-based company Emotiv, which design it for gaming. After a few rounds of "mental training," the driver learns to move virtual objects only by thinking. Each action corresponds to a different brain activity pattern, and the BrainDriver software associates the patterns to specific commands -- turn left, turn right, accelerate etc. The researchers then feed these commands to the drive-by-wire system of the vehicle, a modified Volkswagen Passat Variant 3c. Now the driver's thoughts can control the engine, brakes, and steering.
To road test their brain-controlled car, the Germans headed out to the former airport in Berlin Tempelhof. The video below shows a driver thought-controlling the car, Yoda-style. "Don't try this at home," the narration says, only half-jokingly.
The researchers caution that the BrainDriver application is still a demonstration and is not ready for the road. But they say that future human-machine interfaces like this have huge potential to improve driving, especially in combination with autonomous vehicles. As an example, they mention an autonomous cab ride, where the passenger could decide, only by thinking, which route to take when more than one possibility exist.
This type of non-invasive brain interface could also allow disabled and paralyzed people to gain more mobility in the future, similarly to what is already happening in applications such as robotic exoskeletons and advanced prosthetics.
Watson, the Jeopardy supercomputer created by IBM, defeated its human opponents, Ken Jennings [above] and Brad Rutter, in the final round of the challenge.
The Jeopardy-IBM challenge has ended, and silicon prevailed over gray matter. Watson, the Jeopardy-playing supercomputer designed by IBM, defeated its human competitors, finishing in first by a wide margin.
As one of the contestants, Jeopardy super champion Ken Jennings, put it, "I for one welcome our new computer overlords."
In this third and final round, Watson's performance was much more impressive than its lackluster debut, but not as enthralling as in Day 2, when it completely dominated the game, except for its now-infamous "What is Toronto?????" mistake. This was one of a few errors that proved embarrassing for IBM, but perhaps entertaining for viewers. Watch what happened:
The final game started with Jennings, Watson, and the third contestant, Brad Rutter, another Jeopardy champion, each getting a bunch of answers right. It was funny to see that one of the categories was "Also On Your Computer Keys." You'd expect that Watson, a computer, would know about computer keys. Not! When this clue, "It's an abbreviation for Grand Prix auto racing" came up, Watson's choices (shown to viewers on the TV screen) were "gpc," "NASCAR," and "QED." Apparently Watson doesn't know about the "F1" key.
The computer seemed to struggle with other categories, like "Actors Who Direct," and Ken didn't waste the opportunity. A streak of correct answers put him in the lead of Day 3, his score reaching $8600 against Watson's $4800 and Brad's $2400. But in the second half of the show, Watson, with its massively parallel hardware (2880 POWER7 processing cores and 16 terabytes of RAM), fired up its DeepQA algorithms and pulled ahead, winning some high-value questions and making only two mistakes.
The Final Jeopardy category was "19th Century Novelists," and the clue, "William Wilkinson's 'an account of the principalities of Wallachia and Moldavia' inspired this authors's most famous novel." The score at this point: Watson with $23,440, Ken with $18,200, and Brad with $5,600.
Both humans got it right, answering: "Who is Bram Stoker?" Brad's total score from this round plus the previous rounds was $21,600. Ken finished just a little ahead with $24,000. What about Watson? No flubs this time. The computer got the answer right and finished with a commanding total of $77,147.
It might be worth to note that if Ken had wagered all he had, he'd have ended up with a final total of $41,200, and if Watson had wagered all it had but got it wrong, its final total would've been $35,734 -- in this scenario, Ken would have won! So why didn't Ken take the chance? Did he think the odds of Watson making such a big mistake were too slim? Indeed, Watson was very confident in its answer: It wagered the exact amount ($17,973) that, even if it had gotten the answer wrong, would keep its score ahead of Ken's; in this case, Watson would end up with $41,201 and Ken with $41,200. A $1 dollar difference! Now that would've been great television.
By winning the challenge, Watson not only helps IBM advance its master plan of making humanity obsolete but it also earns the company the $1 million top prize. The money, though, will go to charity. Sorry, Watson, no CPU upgrade for you!
So what does Watson's victory mean for the future of AI? Will it help advance the field or is it just a publicity stunt that will benefit IBM's image but have limited practical applications, much like the Deep Blue vs. Kasparov matches?
IBM insists that Watson "changes the paradigm in which we work with computers" and will "transform many industries." As examples, they say Watson could help clinicians trying to diagnose a hard case, or lawyers sifting through mountains of evidence material, or governments and companies managing natural resources like water.
From a more fundamental point of view, Watson is part of a shift in AI that has put emphasis on systems that are not programmed to solve problems but rather programmed to learn how to solve problems. Peter Norvig, Google's director of research, recently wrote about the promises of this new direction in AI:
This approach of relying on examples — on massive amounts of data — rather than on cleverly composed rules, is a pervasive theme in modern A.I. work. It has been applied to closely related problems like speech recognition and to very different problems like robot navigation. IBM’s Watson system also relies on massive amounts of data, spread over hundreds of computers, as well as a sophisticated mechanism for combining evidence from multiple sources.
The current decade is a very exciting time for A.I. development because the economics of computer hardware has just recently made it possible to address many problems that would have been prohibitively expensive in the past. In addition, the development of wireless and cellular data networks means that these exciting new applications are no longer locked up in research labs, they are more likely to be available to everyone as services on the web.
So maybe in a few years we'll all be carrying a Watson app on our smartphones. It will destroy us on a "Jeopardy!" game, but help us mine data, connect dots, and solve some of our hardest problems. Do you agree? What do you think Watson means for the future of computers -- and humanity?
Having lots of furniture is a terribly inefficient way to live, considering that most of your furniture is not actually in use most of the time. A much better way to do it would be to just have one single piece of furniture that manages to be, say, a chair, a table, and a bed whenever you need it to be. You know, like my couch. But if you need more specific functionality, you may soon be able to get it using Roombots, little modular robots that can configure themselves into all kinds of different objects.
One Roombot is a fairly simple (and therefore relatively cheap) modular robot with lots of connectors and a hinge in the middle. By itself, it's not good for much, but when it gets together with a bunch of its friends, they can autonomously combine to turn themselves into all sorts of different pieces of furniture. They'd be able to move around on command, and when you don't need them anymore, they'd stack themselves neatly against the wall.
In a hypothetical near future, I can see myself getting out of bed in the morning and taking a shower. My bed, meanwhile, turns itself into a breakfast table and chair. After I eat, the table turns into a desk, but I decide I'd rather work on the couch today, so it turns into a couch instead. Each piece of furniture would be infinitely flexible, too, so I could ask my desk to reposition itself higher or lower and it would obey, or I could even ask for a bit more space and some extra bots would come over and stick themselves on to augment the desktop.
This stuff sounds pretty far out, but it's not too terribly complicated. This is one of the big advantages of modular robotics: lots of simple robots with clever programming can get together and team up to do complex tasks, like building me a couch with an integrated desk that I'll never, ever have to move from.
The game started with Monday's score: Brad Rutter tied with Watson for first with $5000, and Ken Jennings last with $2000.
Ken was first to pick a category, but after host Alex Trebek read the clue, Watson buzzed faster. From then on, the computer just kept on going, buzzing and answering correctly seven times in a row, amassing $21,035. Ken and Brad stood there, hopeless. The IBMers in the audience grinned and clapped.
Which brings me to my first question about this whole thing: How does Watson ring the buzzer? Was something implemented to make the buzzing fairer to the human competitors, who are not electrically wired to the game hardware? Update: Here's how Watson receives the clue and rings in the buzzer: It receives the clue as a text file at the moment that the clue appears on the stage screen, so in principle at the same time the clue "hits Brad Rutter’s and Ken Jennings’ retinas." To buzz in, Watson receives a signal when a "buzzer enable" light turns on, and then it can activate a robotic finger to press the buzzer. Though some may disagree, IBM claims this is a fair design to compete with human contestants.
Anyway, after the seventh correct answer, the category was "The Art of the Steal" and an interesting clue came up. Watch what happened:
Clearly, Watson didn't quite understand the clue, which called for an art
period, not an artist, as answer. Curiously, the computer had the correct answer listed among its choices, but with a low probability. The humans had no problem understanding the question -- but they got the art period wrong.
Watson's confusion didn't last, though. Soon, the machine was again dominating the game, this time getting six straight correct answers and expanding its lead. Ken and Brad would occasionally get an answer right, but it was a Watson show.
The highlight of the night came at the end, during the Final Jeopardy round, when contestants can wager a certain amount (up to their total score) and then they see the final clue. The category was "U.S. cities," and Watson had $36,681, Rutter $5400, and Jennings $2400. Watch:
Toronto????? Ooohhh. You can hear the IBMers gasping, terrified that this humiliating mistake is going to cost Watson everything. But nope. The smarty-pants (or smarty-racks) machine didn't go all in, its wagering-strategy algorithm deciding to bet just $947. (Here's how IBM explains the flub.)
So the night ended with Jennings with $4800, Brad with $10,400, and Watson with $35,734. The LCD-faced machine, with its HAL 9000 voice, vastly outperformed the best brains at this game. A massacre.
Which brings me to my second question: What is Watson good for other than playing Jeopardy? Will it help advance AI for real or is this just an entertaining challenge, much like the Deep Blue vs. Kasparov matches?
IBM, wise about this PR opportunity, made sure to include a video segment in which its execs and scientists brag about Watson's potential "to transform many industries." Their comments, however, were vague -- things like "Life is about questions and answers," or "This changes the paradigm in which we work with computers" -- and the most concrete example they gave was using Watson to help clinicians diagnose a hard case involving lots of data.
The whole thing looks like a giant commercial for IBM, but hey, I'm not complaining; I was very entertained and feel like I want to learn more about how Watson works. And I'm looking forward to tonight's round. Do Watson's mistakes mean there's hope for Ken and Brad? What do you think will happen tonight?
Meka Robotics is unveiling this week its Meka M1 Mobile Manipulator, a humanoid system equipped with two dexterous arms, a head with Microsoft Kinect sensor, and an omnidirectional wheel base. The robot runs Meka's real-time control software with ROS extensions.
Meka, a San Francisco-based start-up founded by MIT roboticists, says the M1 is designed to work in human environments and combines "mobility, dexterity, and compliant force-control." It seems Meka is targeting first research applications, whereas other companies developing similar manipulators -- like pi4 robotics in Germany and Rodney Brooks' Heartland Robotics -- are focusing on industrial uses.
The M1-Standard [image, right] comes with a preconfigured set of manipulators: Meka's compliant manipulators with 6 axis force-torque sensors at the wrist and compliant grippers. The pan-tilt head comes with a Kinect 3D camera and 5 megapixel Ethernet camera. And the base is a small-footprint omni platform with prismatic lift.
The robot's computer runs the Meka M3 and ROS software stacks. Meka says they're "pushing on deeper ROS integration" and expect upcoming versions of their M3 control software to "integrate many of the great packages that the ROS community is generating."
It looks like an amazing robot, but it doesn't come cheap. The M1-Standard is priced at US $340,000.
The M1-Custom [image, top], as the name suggests, allows customers to choose different sensors, hands, and head to build the robot they want (pricetag will vary accordingly). Meka says the first M1-Custom, seen in the video below, shipped last month.
Meka has been working on all the robots subsystems and ROS integration for some time. Inspiration for the M1, the company says, came in part from another robot, Georgia Tech's Cody, which uses Meka arms. With the M1, Meka has finally combined all the subsystems into a single, integrated robot.
This is an amazing time for robotics. So much is happening. If only we could turbocharge this blog.
Guess what? We're doing exactly that. Starting this week, Automaton and BotJunkie, two of the world's leading robots blogs, are teaming up to create a monster robotics news machine. Well, we're still more human than machine, but we'll be churning out lots of great stuff -- daily stories, in-depth articles, product reviews, interviews, exclusive videos, and more.
The reason we're merging is simple. Evan Ackerman, the creator of BotJunkie, and I believe that together (along with other contributors) we can do a much better job covering all the cool news and happenings in robotics and AI. Did I mention there's a lot happening? (Read Evan's note on the merger.)
So if you're already an Automaton reader, expect even greater robotics content. If you're a BotJunkie reader migrating over here, welcome!
As Evan said, we really like what he's been doing at BotJunkie and we don't want that to change. Evan will be posting daily stories about the same types of things and with the same style and tone as he'd been doing over at BotJunkie. But he'll also be doing more -- traveling to conferences, reviewing more products, and occasionally becoming a robot himself. (He's so dedicated to his readership that, even though he'll be out on vacation for a couple of weeks, he prepared a bunch of posts to be published while he's away.)
As in any merger, there are bumps along the way, and several loyal BotJunkie readers have asked for a better RSS feed and web design for Automaton. We hear you. We agree with you. And we'll do all we can to make those things happen.
It may sound corny, but as Evan nicely put it, we love writing about robots, but it's you, our readers, who really make it worthwhile. We love the e-mails, the tips, the comments, the tweets. Keep those coming. You can reach us by e-mail -- e.guizzo (at) ieee (dot) org and evan (at) botjunkie (dot) com -- or on Twitter (AutomatonBlog and BotJunkie) and Facebook.
This is HOAP-2, and it likes to clean. It doesn't really know how to clean, but that's okay, because it does know how to learn. A human can move HOAP-2's arms in different cleaning patterns, and the bot will remember and then be able to clean by itself later on. Take a look:
The cool thing here is, of course, that HOAP is learning to erase instead of being programmed to erase. Robot learning is the focus of tons of research today. Now, in the case of HOAP, some people would argue that this is a waste of time, because robots should be able to detect marks on a whiteboard and erase them autonomously. And that's true, but it's also not the point.
If you're a teacher with a bunch of dirty whiteboards and no naughty kids and someone hands you a robot, you don't want to have to worry about whether your whiteboards are the right shade of white or the right size or whatever... And what if you have chalkboards instead? It really makes much more sense to have a robot be a generalist, and to be an effective generalist a robot has to be adaptable, something that (for now at least) robots are notoriously bad at. But robots are notoriously good at following instructions, so robots that can learn new tasks from humans on the fly have the potential to be much more effective, and much less frustrating for their users.
You can relax: so far, we humans aren't completely redundant, as IBM's Watson artificial intelligence system managed to not answer every single question instantly and correctly in the first round of a three day Jeopardy exhibition match with past champions Ken Jennings and Brad Rutter.
After round one (which was as far as today's show got), Watson was tied with Brad for first with $5000, and Ken Jennings was in third with $2000. It's worth mentioning that Watson led for most of the round, with a few incorrect questions setting it back significantly towards the end. Watson definitely showed more 'common sense' than I was expecting, but it still seemed burdened with a fair amount of the 'so smart it's kinda dumb' that computers are known for. It definitely didn't get everything right, displaying a significant number of low-certainty answers (below its buzzing threshold) as well as buzzing in with several answers that seemed pretty far off. It also managed to give an incorrect answer to a question that Ken had incorrectly answered with the same answer moments before, which is a bug that I bet is getting resolved as we speak.
While I was honestly hoping that Watson would do slightly better, I'm relieved that it at least ended up tied for first. Irrespective of expectations, I feel like Watson turned in an impressive performance, and I think that's one of the most important aspects of this exhibition... In my experience, the overall perception that the general public has of the current state of robotics and artificial intelligence is some mix of Roomba, the Terminator, and Commander Data, which is (to put it mildly) somewhat inaccurate and unrealistic. So, it's good to see a state-of-the-art AI system put on a credible public performance, complete with some fallability to keep us feeling comfortably in control.
The next segment (featuring the the second round plus Final Jeopardy) airs on ABC tonight, with the final match on Wednesday.