Tag Archives: tool

#437851 Boston Dynamics’ Spot Robot Dog ...

Boston Dynamics has been fielding questions about when its robots are going to go on sale and how much they’ll cost for at least a dozen years now. I can say this with confidence, because that’s how long I’ve been a robotics journalist, and I’ve been pestering them about it the entire time. But it’s only relatively recently that the company started to make a concerted push away from developing robots exclusively for the likes of DARPA into platforms with more commercial potential, starting with a compact legged robot called Spot, first introduced in 2016.

Since then, we’ve been following closely as Spot has gone from a research platform to a product, and today, Boston Dynamics is announcing the final step in that process: commercial availability. You can now order a Spot Explorer Kit from the Boston Dynamics online store for US $74,500 (plus tax), shipping included, with delivery in 6 to 8 weeks. FINALLY!

Over the past 10 months or so, Boston Dynamics has leased Spot robots to carefully selected companies, research groups, and even a few individuals as part of their early adopter program—that’s where all of the clips in the video below came from. While there are over 100 Spots out in the world right now, getting one of them has required convincing Boston Dynamics up front that you knew more or less exactly what you wanted to do and how you wanted to do it. If you’re a big construction company or the Jet Propulsion Laboratory or Adam Savage, that’s all well and good, but for other folks who think that a Spot could be useful for them somehow and want to give it a shot, this new availability provides a fewer-strings attached opportunity to do some experimentation with the robot.

There’s a lot of cool stuff going on in that video, but we were told that the one thing that really stood out to the folks at Boston Dynamics was a 2-second clip that you can see on the left-hand side of the screen from 0:19 to 0:21. In it, Spot is somehow managing to walk across a spider web of rebar without getting tripped up, at faster than human speed. This isn’t something that Spot was specifically programmed to do, and in fact the Spot User Guide specifically identifies “rebar mesh” as an unsafe operating environment. But the robot just handles it, and that’s a big part of what makes Spot so useful—its ability to deal with (almost) whatever you can throw at it.

Before you get too excited, Boston Dynamics is fairly explicit that the current license for the robot is intended for commercial use, and the company specifically doesn’t want people to be just using it at home for fun. We know this because we asked (of course we asked), and they told us “we specifically don’t want people to just be using it at home for fun.” Drat. You can still buy one as an individual, but you have to promise that you’ll follow the terms of use and user guidelines, and it sounds like using a robot in your house might be the second-fastest way to invalidate your warranty:

SPOT IS AN AMAZING ROBOT, BUT IS NOT CERTIFIED SAFE FOR IN-HOME USE OR INTENDED FOR USE NEAR CHILDREN OR OTHERS WHO MAY NOT APPRECIATE THE HAZARDS ASSOCIATED WITH ITS OPERATION.

Not being able to get Spot to play with your kids may be disappointing, but for those of you with the sort of kids who are also students, the good news is that Boston Dynamics has carved out a niche for academic institutions, which can buy Spot at a discounted price. And if you want to buy a whole pack of Spots, there’s a bulk discount for Enterprise users as well.

What do you get for $74,500? All this!

Spot robot
Spot battery (2x)
Spot charger
Tablet controller and charger
Robot case for storage and transportation
FREE SHIPPING!

Photo: Boston Dynamics

The basic package includes the robot, two batteries, charger, a tablet controller, and a storage case.

You can view detailed specs here.

So is $75k a lot of money for a robot like Spot, or not all that much? We don’t have many useful points of comparison, partially because it’s not clear to what extent other pre-commercial quadrupedal robots (like ANYmal or Aliengo) share capabilities and features with Spot. For more perspective on Spot’s price tag, we spoke to Michael Perry, vice president of business development at Boston Dynamics.

IEEE Spectrum: Why is Spot so affordable?

Michael Perry: The main goal of selling the robot at this stage is to try to get it into the hands of as many application developers as possible, so that we can learn from the community what the biggest driver of value is for Spot. As a platform, unlocking the value of an ecosystem is our core focus right now.

Spectrum: Why is Spot so expensive?

Perry: Expensive is relative, but compared to the initial prototypes of Spot, we’ve been able to drop down the cost pretty significantly. One key thing has been designing it for robustness—we’ve put hundreds and hundreds of hours on the robot to make sure that it’s able to be successful when it falls, or when it has an electrostatic discharge. We’ve made sure that it’s able to perceive a wide variety of environments that are difficult for traditional vision-based sensors to handle. A lot of that engineering is baked into the core product so that you don’t have to worry about the mobility or robotic side of the equation, you can just focus on application development.

Photos: Boston Dynamics

Accessories for Spot include [clockwise from top left]: Spot GXP with additional ports for payload integration; Spot CAM with panorama camera and advanced comms; Spot CAM+ with pan-tilt-zoom camera for inspections; Spot EAP with lidar to enhance autonomy on large sites; Spot EAP+ with Spot CAM camera plus lidar; and Spot CORE for additional processing power.

The $75k that you’ll pay for the Spot Explorer Kit, it’s important to note, is just the base price for the robot. As with other things that fall into this price range (like a luxury car), there are all kinds of fun ways to drive that cost up with accessories, although for Spot, some of those accessories will be necessary for many (if not most) applications. For example, a couple of expansion ports to make it easier to install your own payloads on Spot will run you $1,275. An additional battery is $4,620. And if you want to really get some work done, the Enhanced Autonomy Package (with 360 cameras, lights, better comms, and a Velodyne VLP-16) will set you back an additional $34,570. If you were hoping for an arm, you’ll have to wait until the end of the year.

Each Spot also includes a year’s worth of software updates and a warranty, although the standard warranty just covers “defects related to materials and workmanship” not “I drove my robot off a cliff” or “I tried to take my robot swimming.” For that sort of thing (user error) to be covered, you’ll need to upgrade to the $12,000 Spot CARE premium service plan to cover your robot for a year as long as you don’t subject it to willful abuse, which both of those examples I just gave probably qualify as.

While we’re on the subject of robot abuse, Boston Dynamics has very sensibly devoted a substantial amount of the Spot User Guide to help new users understand how they should not be using their robot, in order to “lessen the risk of serious injury, death, or robot and other property damage.” According to the guide, some things that could cause Spot to fall include holes, cliffs, slippery surfaces (like ice and wet grass), and cords. Spot’s sensors also get confused by “transparent, mirrored, or very bright obstacles,” and the guide specifically says Spot “may crash into glass doors and windows.” Also this: “Spot cannot predict trajectories of moving objects. Do not operate Spot around moving objects such as vehicles, children, or pets.”

We should emphasize that this is all totally reasonable, and while there are certainly a lot of things to be aware of, it’s frankly astonishing that these are the only things that Boston Dynamics explicitly warns users against. Obviously, not every potentially unsafe situation or thing is described above, but the point is that Boston Dynamics is willing to say to new users, “here’s your robot, go do stuff with it” without feeling the need to hold their hand the entire time.

There’s one more thing to be aware of before you decide to buy a Spot, which is the following:

“All orders will be subject to Boston Dynamics’ Terms and Conditions of Sale which require the beneficial use of its robots.”

Specifically, this appears to mean that you aren’t allowed to (or supposed to) use the robot in a way that could hurt living things, or “as a weapon, or to enable any weapon.” The conditions of sale also prohibit using the robot for “any illegal or ultra-hazardous purpose,” and there’s some stuff in there about it not being cool to use Spot for “nuclear, chemical, or biological weapons proliferation, or development of missile technology,” which seems weirdly specific.

“Once you make a technology more broadly available, the story of it starts slipping out of your hands. Our hope is that ahead of time we’re able to clearly articulate the beneficial uses of the robot in environments where we think the robot has a high potential to reduce the risk to people, rather than potentially causing harm.”
—Michael Perry, Boston Dynamics

I’m very glad that Boston Dynamics is being so upfront about requiring that Spot is used beneficially. However, it does put the company in a somewhat challenging position now that these robots are being sold. Boston Dynamics can (and will) perform some amount of due-diligence before shipping a Spot, but ultimately, once the robots are in someone else’s hands, there’s only so much that BD can do.

Spectrum: Why is beneficial use important to Boston Dynamics?

Perry: One of the key things that we’ve highlighted many times in our license and terms of use is that we don’t want to see the robot being used in any way that inflicts physical harm on people or animals. There are philosophical reasons for that—I think all of us don’t want to see our technology used in a way that would hurt people. But also from a business perspective, robots are really terrible at conveying intention. In order for the robot to be helpful long-term, it has to be trusted as a piece of technology. So rather than looking at a robot and wondering, “is this something that could potentially hurt me,” we want people to think “this is a robot that’s here to help me.” To the extent that people associate Boston Dynamics with cutting edge robots, we think that this is an important stance for the rollout of our first commercial product. If we find out that somebody’s violated our terms of use, their warranty is invalidated, we won’t repair their product, and we have a licensing timeout that would prevent them from accessing their robot after that timeout has expired. It’s a remediation path, but we do think that it’s important to at least provide that as something that helps enforce our position on use of our technology.

It’s very important to keep all of this in context: Spot is a tool. It’s got some autonomy and the appearance of agency, but it’s still just doing what people tell it to do, even if those things might be unsafe. If you read through the user guide, it’s clear how much of an effort Boston Dynamics is making to try to convey the importance of safety to Spot users—and ultimately, barring some unforeseen and catastrophic software or hardware issues, safety is about the users, rather than Boston Dynamics or Spot itself. I bring this up because as we start seeing more and more Spots doing things without Boston Dynamics watching over them quite so closely, accidents are likely inevitable. Spot might step on someone’s foot. It might knock someone over. If Spot was perfectly safe, it wouldn’t be useful, and we have to acknowledge that its impressive capabilities come with some risks, too.

Photo: Boston Dynamics

Each Spot includes a year’s worth of software updates and a warranty, although the standard warranty just covers “defects related to materials and workmanship” not “I drove my robot off a cliff.”

Now that Spot is on the market for real, we’re excited to see who steps up and orders one. Depending on who the potential customer is, Spot could either seem like an impossibly sophisticated piece of technology that they’d never be able to use, or a magical way of solving all of their problems overnight. In reality, it’s of course neither of those things. For the former (folks with an idea but without a lot of robotics knowledge or experience), Spot does a lot out of the box, but BD is happy to talk with people and facilitate connections with partners who might be able to integrate specific software and hardware to get Spot to do a unique task. And for the latter (who may also be folks with an idea but without a lot of robotics knowledge or experience), BD’s Perry offers a reminder Spot is not Rosie the Robot, and would be equally happy to talk about what the technology is actually capable of doing.

Looking forward a bit, we asked Perry whether Spot’s capabilities mean that customers are starting to think beyond using robots to simply replace humans, and are instead looking at them as a way of enabling a completely different way of getting things done.

Spectrum: Do customers interested in Spot tend to think of it as a way of replacing humans at a specific task, or as a system that can do things that humans aren’t able to do?

Perry: There are what I imagine as three levels of people understanding the robot applications. Right now, we’re at level one, where you take a person out of this dangerous, dull job, and put a robot in. That’s the entry point. The second level is, using the robot, can we increase the production of that task? For example, take site documentation on a construction site—right now, people do 360 image capture of a site maybe once a week, and they might do a laser scan of the site once per project. At the second level, the question is, what if you were able to get that data collection every day, or multiple times a day? What kinds of benefits would that add to your process? To continue the construction example, the third level would be, how could we completely redesign this space now that we know that this type of automation is available? To take one example, there are some things that we cannot physically build because it’s too unsafe for people to be a part of that process, but if you were to apply robotics to that process, then you could potentially open up a huge envelope of design that has been inaccessible to people.

To order a Spot of your very own, visit shop.bostondynamics.com.

A version of this post appears in the August 2020 print issue as “$74,500 Will Fetch You a Spot.” Continue reading

Posted in Human Robots

#437826 Video Friday: Skydio 2 Drone Is Back on ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

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

Skydio, which makes what we’re pretty sure is the most intelligent consumer drone (or maybe just drone period) in existence, has been dealing with COVID-19 just like the rest of us. Even so, they’ve managed to push out a major software update, and pre-orders for the Skydio 2 are now open again.

If you think you might want one, read our review, after which you’ll be sure you want one.

[ Skydio ]

Worried about people with COVID entering your workplace? Misty II has your front desk covered, in a way that’s quite a bit friendlier than many other options.

Misty II provides a dynamic and interactive screening experience that delivers a joyful experience in an otherwise depressing moment while also delivering state of the art thermal scanning and health screening. We have already found that employees, customers, and visitors appreciate the novelty of interacting with a clever and personable robot. Misty II engages dynamically, both visually and verbally. Companies appreciate using a solution with a blackbody-referenced thermal camera that provides high accuracy and a short screening process for efficiency. Putting a robot to work in this role shifts not only how people look at the screening process but also how robots can take on useful assignments in business, schools and homes.

[ Misty Robotics ]

Thanks Tim!

I’m definitely the one in the middle.

[ Agility Robotics ]

NASA’s Ingenuity helicopter is traveling to Mars attached to the belly of the Perseverance rover and must safely detach to begin the first attempt at powered flight on another planet. Tests done at NASA’s Jet Propulsion Laboratory and Lockheed Martin Space show the sequence of events that will bring the helicopter down to the Martian surface.

[ JPL ]

Here’s a sequence of videos of Cassie Blue making it (or mostly making it) up a 22-degree slope.

My mood these days is Cassie at 1:09.

[ University of Michigan ]

Thanks Jesse!

This is somewhere on the line between home automation and robotics, but it’s a cool idea: A baby crib that “uses computer vision and machine learning to recognize subtle changes” in an infant’s movement, and proactively bounces them to keep them sleeping peacefully.

It costs $1000, but how much value do you put on 24 months of your own sleep?

[ Cradlewise ]

Thanks Ben!

As captive marine mammal shows have fallen from favor; and the catching, transporting and breeding of marine animals has become more restricted, the marine park industry as a viable business has become more challenging – yet the audience appetite for this type of entertainment and education has remained constant.

Real-time Animatronics provide a way to reinvent the marine entertainment industry with a sustainable, safe, and profitable future. Show venues include aquariums, marine parks, theme parks, fountain shows, cruise lines, resort hotels, shopping malls, museums, and more.

[ EdgeFX ] via [ Gizmodo ]

Robotic cabling is surprisingly complex and kinda cool to watch.

The video shows the sophisticated robot application “Automatic control cabinet cabling”, which Fraunhofer IPA implemented together with the company Rittal. The software pitasc, developed at Fraunhofer IPA, is used for force-controlled assembly processes. Two UR robot arms carry out the task together. The modular pitasc system enables the robot arms to move and rotate in parallel. They work hand in hand, with one robot holding the cable and the second bringing it to the starting position for the cabling. The robots can find, tighten, hold ready, lay, plug in, fix, move freely or immerse cables. They can also perform push-ins and pull tests.

[ Fraunhofer ]

This is from 2018, but the concept is still pretty neat.

We propose to perform a novel investigation into the ability of a propulsively hopping robot to reach targets of high science value on the icy, rugged terrains of Ocean Worlds. The employment of a multi-hop architecture allows for the rapid traverse of great distances, enabling a single mission to reach multiple geologic units within a timespan conducive to system survival in a harsh radiation environment. We further propose that the use of a propulsive hopping technique obviates the need for terrain topographic and strength assumptions and allows for complete terrain agnosticism; a key strength of this concept.

[ NASA ]

Aerial-aquatic robots possess the unique ability of operating in both air and water. However, this capability comes with tremendous challenges, such as communication incompati- bility, increased airborne mass, potentially inefficient operation in each of the environments and manufacturing difficulties. Such robots, therefore, typically have small payloads and a limited operational envelope, often making their field usage impractical. We propose a novel robotic water sampling approach that combines the robust technologies of multirotors and underwater micro-vehicles into a single integrated tool usable for field operations.

[ Imperial ]

Event cameras are bio-inspired vision sensors with microsecond latency resolution, much larger dynamic range and hundred times lower power consumption than standard cameras. This 20-minute talk gives a short tutorial on event cameras and show their applications on computer vision, drones, and cars.

[ UZH ]

We interviewed Paul Newman, Perla Maiolino and Lars Kunze, ORI academics, to hear what gets them excited about robots in the future and any advice they have for those interested in the field.

[ Oxford Robotics Institute ]

Two projects from the Rehabilitation Engineering Lab at ETH Zurich, including a self-stabilizing wheelchair and a soft exoskeleton for grasping assistance.

[ ETH Zurich ]

Silicon Valley Robotics hosted an online conversation about robotics and racism. Moderated by Andra Keay, the panel featured Maynard Holliday, Tom Williams, Monroe Kennedy III, Jasmine Lawrence, Chad Jenkins, and Ken Goldberg.

[ SVR ]

The ICRA Legged Locomotion workshop has been taking place online, and while we’re not getting a robot mosh pit, there are still some great talks. We’ll post two here, but for more, follow the legged robots YouTube channel at the link below.

[ YouTube ] Continue reading

Posted in Human Robots

#437769 Q&A: Facebook’s CTO Is at War With ...

Photo: Patricia de Melo Moreira/AFP/Getty Images

Facebook chief technology officer Mike Schroepfer leads the company’s AI and integrity efforts.

Facebook’s challenge is huge. Billions of pieces of content—short and long posts, images, and combinations of the two—are uploaded to the site daily from around the world. And any tiny piece of that—any phrase, image, or video—could contain so-called bad content.

In its early days, Facebook relied on simple computer filters to identify potentially problematic posts by their words, such as those containing profanity. These automatically filtered posts, as well as posts flagged by users as offensive, went to humans for adjudication.

In 2015, Facebook started using artificial intelligence to cull images that contained nudity, illegal goods, and other prohibited content; those images identified as possibly problematic were sent to humans for further review.

By 2016, more offensive photos were reported by Facebook’s AI systems than by Facebook users (and that is still the case).

In 2018, Facebook CEO Mark Zuckerberg made a bold proclamation: He predicted that within five or ten years, Facebook’s AI would not only look for profanity, nudity, and other obvious violations of Facebook’s policies. The tools would also be able to spot bullying, hate speech, and other misuse of the platform, and put an immediate end to them.

Today, automated systems using algorithms developed with AI scan every piece of content between the time when a user completes a post and when it is visible to others on the site—just fractions of a second. In most cases, a violation of Facebook’s standards is clear, and the AI system automatically blocks the post. In other cases, the post goes to human reviewers for a final decision, a workforce that includes 15,000 content reviewers and another 20,000 employees focused on safety and security, operating out of more than 20 facilities around the world.

In the first quarter of this year, Facebook removed or took other action (like appending a warning label) on more than 9.6 million posts involving hate speech, 8.6 million involving child nudity or exploitation, almost 8 million posts involving the sale of drugs, 2.3 million posts involving bullying and harassment, and tens of millions of posts violating other Facebook rules.

Right now, Facebook has more than 1,000 engineers working on further developing and implementing what the company calls “integrity” tools. Using these systems to screen every post that goes up on Facebook, and doing so in milliseconds, is sucking up computing resources. Facebook chief technology officer Mike Schroepfer, who is heading up Facebook’s AI and integrity efforts, spoke with IEEE Spectrum about the team’s progress on building an AI system that detects bad content.

Since that discussion, Facebook’s policies around hate speech have come under increasing scrutiny, with particular attention on divisive posts by political figures. A group of major advertisers in June announced that they would stop advertising on the platform while reviewing the situation, and civil rights groups are putting pressure on others to follow suit until Facebook makes policy changes related to hate speech and groups that promote hate, misinformation, and conspiracies.

Facebook CEO Mark Zuckerberg responded with news that Facebook will widen the category of what it considers hateful content in ads. Now the company prohibits claims that people from a specific race, ethnicity, national origin, religious affiliation, caste, sexual orientation, gender identity, or immigration status are a threat to the physical safety, health, or survival of others. The policy change also aims to better protect immigrants, migrants, refugees, and asylum seekers from ads suggesting these groups are inferior or expressing contempt. Finally, Zuckerberg announced that the company will label some problematic posts by politicians and government officials as content that violates Facebook’s policies.

However, civil rights groups say that’s not enough. And an independent audit released in July also said that Facebook needs to go much further in addressing civil rights concerns and disinformation.

Schroepfer indicated that Facebook’s AI systems are designed to quickly adapt to changes in policy. “I don’t expect considerable technical changes are needed to adjust,” he told Spectrum.

This interview has been edited and condensed for clarity.

IEEE Spectrum: What are the stakes of content moderation? Is this an existential threat to Facebook? And is it critical that you deal well with the issue of election interference this year?

Schroepfer: It’s probably existential; it’s certainly massive. We are devoting a tremendous amount of our attention to it.

The idea that anyone could meddle in an election is deeply disturbing and offensive to all of us here, just as people and citizens of democracies. We don’t want to see that happen anywhere, and certainly not on our watch. So whether it’s important to the company or not, it’s important to us as people. And I feel a similar way on the content-moderation side.

There are not a lot of easy choices here. The only way to prevent people, with certainty, from posting bad things is to not let them post anything. We can take away all voice and just say, “Sorry, the Internet’s too dangerous. No one can use it.” That will certainly get rid of all hate speech online. But I don’t want to end up in that world. And there are variants of that world that various governments are trying to implement, where they get to decide what’s true or not, and you as a person don’t. I don’t want to get there either.

My hope is that we can build a set of tools that make it practical for us to do a good enough job, so that everyone is still excited about the idea that anyone can share what they want, and so that Facebook is a safe and reasonable place for people to operate in.

Spectrum: You joined Facebook in 2008, before AI was part of the company’s toolbox. When did that change? When did you begin to think that AI tools would be useful to Facebook?

Schroepfer: Ten years ago, AI wasn’t commercially practical; the technology just didn’t work very well. In 2012, there was one of those moments that a lot of people point to as the beginning of the current revolution in deep learning and AI. A computer-vision model—a neural network—was trained using what we call supervised training, and it turned out to be better than all the existing models.

Spectrum: How is that training done, and how did computer-vision models come to Facebook?

Image: Facebook

Just Broccoli? Facebook’s image analysis algorithms can tell the difference between marijuana [left] and tempura broccoli [right] better than some humans.

Schroepfer: Say I take a bunch of photos and I have people look at them. If they see a photo of a cat, they put a text label that says cat; if it’s one of a dog, the text label says dog. If you build a big enough data set and feed that to the neural net, it learns how to tell the difference between cats and dogs.

Prior to 2012, it didn’t work very well. And then in 2012, there was this moment where it seemed like, “Oh wow, this technique might work.” And a few years later we were deploying that form of technology to help us detect problematic imagery.

Spectrum: Do your AI systems work equally well on all types of prohibited content?

Schroepfer: Nudity was technically easiest. I don’t need to understand language or culture to understand that this is either a naked human or not. Violence is a much more nuanced problem, so it was harder technically to get it right. And with hate speech, not only do you have to understand the language, it may be very contextual, even tied to recent events. A week before the Christchurch shooting [New Zealand, 2019], saying “I wish you were in the mosque” probably doesn’t mean anything. A week after, that might be a terrible thing to say.

Spectrum: How much progress have you made on hate speech?

Schroepfer: AI, in the first quarter of 2020, proactively detected 88.8 percent of the hate-speech content we removed, up from 80.2 percent in the previous quarter. In the first quarter of 2020, we took action on 9.6 million pieces of content for violating our hate-speech policies.

Image: Facebook

Off Label: Sometimes image analysis isn’t enough to determine whether a picture posted violates the company’s policies. In considering these candy-colored vials of marijuana, for example, the algorithms can look at any accompanying text and, if necessary, comments on the post.

Spectrum: It sounds like you’ve expanded beyond tools that analyze images and are also using AI tools that analyze text.

Schroepfer: AI started off as very siloed. People worked on language, people worked on computer vision, people worked on video. We’ve put these things together—in production, not just as research—into multimodal classifiers.

[Schroepfer shows a photo of a pan of Rice Krispies treats, with text referring to it as a “potent batch”] This is a case in which you have an image, and then you have the text on the post. This looks like Rice Krispies. On its own, this image is fine. You put the text together with it in a bigger model; that can then understand what’s going on. That didn’t work five years ago.

Spectrum: Today, every post that goes up on Facebook is immediately checked by automated systems. Can you explain that process?

Image: Facebook

Bigger Picture: Identifying hate speech is often a matter of context. Either the text or the photo in this post isn’t hateful standing alone, but putting them together tells a different story.

Schroepfer: You upload an image and you write some text underneath it, and the systems look at both the image and the text to try to see which, if any, policies it violates. Those decisions are based on our Community Standards. It will also look at other signals on the posts, like the comments people make.

It happens relatively instantly, though there may be times things happen after the fact. Maybe you uploaded a post that had misinformation in it, and at the time you uploaded it, we didn’t know it was misinformation. The next day we fact-check something and scan again; we may find your post and take it down. As we learn new things, we’re going to go back through and look for violations of what we now know to be a problem. Or, as people comment on your post, we might update our understanding of it. If people are saying, “That’s terrible,” or “That’s mean,” or “That looks fake,” those comments may be an interesting signal.

Spectrum: How is Facebook applying its AI tools to the problem of election interference?

Schroepfer: I would split election interference into two categories. There are times when you’re going after the content, and there are times you’re going after the behavior or the authenticity of the person.

On content, if you’re sharing misinformation, saying, “It’s super Wednesday, not super Tuesday, come vote on Wednesday,” that’s a problem whether you’re an American sitting in California or a foreign actor.

Other times, people create a series of Facebook pages pretending they’re Americans, but they’re really a foreign entity. That is a problem on its own, even if all the content they’re sharing completely meets our Community Standards. The problem there is that you have a foreign government running an information operation.

There, you need different tools. What you’re trying to do is put pieces together, to say, “Wait a second. All of these pages—Martians for Justice, Moonlings for Justice, and Venusians for Justice”—are all run by an administrator with an IP address that’s outside the United States. So they’re all connected, even though they’re pretending to not be connected. That’s a very different problem than me sitting in my office in Menlo Park [Calif.] sharing misinformation.

I’m not going to go into lots of technical detail, because this is an area of adversarial nature. The fundamental problem you’re trying to solve is that there’s one entity coordinating the activity of a bunch of things that look like they’re not all one thing. So this is a series of Instagram accounts, or a series of Facebook pages, or a series of WhatsApp accounts, and they’re pretending to be totally different things. We’re looking for signals that these things are related in some way. And we’re looking through the graph [what Facebook calls its map of relationships between users] to understand the properties of this network.

Spectrum: What cutting-edge AI tools and methods have you been working on lately?

Schroepfer: Supervised learning, with humans setting up the instruction process for the AI systems, is amazingly effective. But it has a very obvious flaw: the speed at which you can develop these things is limited by how fast you can curate the data sets. If you’re dealing in a problem domain where things change rapidly, you have to rebuild a new data set and retrain the whole thing.

Self-supervision is inspired by the way people learn, by the way kids explore the world around them. To get computers to do it themselves, we take a bunch of raw data and build a way for the computer to construct its own tests. For language, you scan a bunch of Web pages, and the computer builds a test where it takes a sentence, eliminates one of the words, and figures out how to predict what word belongs there. And because it created the test, it actually knows the answer. I can use as much raw text as I can find and store because it’s processing everything itself and doesn’t require us to sit down and build the information set. In the last two years there has been a revolution in language understanding as a result of AI self-supervised learning.

Spectrum: What else are you excited about?

Schroepfer: What we’ve been working on over the last few years is multilingual understanding. Usually, when I’m trying to figure out, say, whether something is hate speech or not I have to go through the whole process of training the model in every language. I have to do that one time for every language. When you make a post, the first thing we have to figure out is what language your post is in. “Ah, that’s Spanish. So send it to the Spanish hate-speech model.”

We’ve started to build a multilingual model—one box where you can feed in text in 40 different languages and it determines whether it’s hate speech or not. This is way more effective and easier to deploy.

To geek out for a second, just the idea that you can build a model that understands a concept in multiple languages at once is crazy cool. And it not only works for hate speech, it works for a variety of things.

When we started working on this multilingual model years ago, it performed worse than every single individual model. Now, it not only works as well as the English model, but when you get to the languages where you don’t have enough data, it’s so much better. This rapid progress is very exciting.

Spectrum: How do you move new AI tools from your research labs into operational use?

Schroepfer: Engineers trying to make the next breakthrough will often say, “Cool, I’ve got a new thing and it achieved state-of-the-art results on machine translation.” And we say, “Great. How long does it take to run in production?” They say, “Well, it takes 10 seconds for every sentence to run on a CPU.” And we say, “It’ll eat our whole data center if we deploy that.” So we take that state-of-the-art model and we make it 10 or a hundred or a thousand times more efficient, maybe at the cost of a little bit of accuracy. So it’s not as good as the state-of-the-art version, but it’s something we can actually put into our data centers and run in production.

Spectrum: What’s the role of the humans in the loop? Is it true that Facebook currently employs 35,000 moderators?

Schroepfer: Yes. Right now our goal is not to reduce that. Our goal is to do a better job catching bad content. People often think that the end state will be a fully automated system. I don’t see that world coming anytime soon.

As automated systems get more sophisticated, they take more and more of the grunt work away, freeing up the humans to work on the really gnarly stuff where you have to spend an hour researching.

We also use AI to give our human moderators power tools. Say I spot this new meme that is telling everyone to vote on Wednesday rather than Tuesday. I have a tool in front of me that says, “Find variants of that throughout the system. Find every photo with the same text, find every video that mentions this thing and kill it in one shot.” Rather than, I found this one picture, but then a bunch of other people upload that misinformation in different forms.

Another important aspect of AI is that anything I can do to prevent a person from having to look at terrible things is time well spent. Whether it’s a person employed by us as a moderator or a user of our services, looking at these things is a terrible experience. If I can build systems that take the worst of the worst, the really graphic violence, and deal with that in an automated fashion, that’s worth a lot to me. Continue reading

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#437763 Peer Review of Scholarly Research Gets ...

In the world of academics, peer review is considered the only credible validation of scholarly work. Although the process has its detractors, evaluation of academic research by a cohort of contemporaries has endured for over 350 years, with “relatively minor changes.” However, peer review may be set to undergo its biggest revolution ever—the integration of artificial intelligence.

Open-access publisher Frontiers has debuted an AI tool called the Artificial Intelligence Review Assistant (AIRA), which purports to eliminate much of the grunt work associated with peer review. Since the beginning of June 2020, every one of the 11,000-plus submissions Frontiers received has been run through AIRA, which is integrated into its collaborative peer-review platform. This also makes it accessible to external users, accounting for some 100,000 editors, authors, and reviewers. Altogether, this helps “maximize the efficiency of the publishing process and make peer-review more objective,” says Kamila Markram, founder and CEO of Frontiers.

AIRA’s interactive online platform, which is a first of its kind in the industry, has been in development for three years.. It performs three broad functions, explains Daniel Petrariu, director of project management: assessing the quality of the manuscript, assessing quality of peer review, and recommending editors and reviewers. At the initial validation stage, the AI can make up to 20 recommendations and flag potential issues, including language quality, plagiarism, integrity of images, conflicts of interest, and so on. “This happens almost instantly and with [high] accuracy, far beyond the rate at which a human could be expected to complete a similar task,” Markram says.

“We have used a wide variety of machine-learning models for a diverse set of applications, including computer vision, natural language processing, and recommender systems,” says Markram. This includes simple bag-of-words models, as well as more sophisticated deep-learning ones. AIRA also leverages a large knowledge base of publications and authors.

Markram notes that, to address issues of possible AI bias, “We…[build] our own datasets and [design] our own algorithms. We make sure no statistical biases appear in the sampling of training and testing data. For example, when building a model to assess language quality, scientific fields are equally represented so the model isn’t biased toward any specific topic.” Machine- and deep-learning approaches, along with feedback from domain experts, including errors, are captured and used as additional training data. “By regularly re-training, we make sure our models improve in terms of accuracy and stay up-to-date.”

The AI’s job is to flag concerns; humans take the final decisions, says Petrariu. As an example, he cites image manipulation detection—something AI is super-efficient at but is nearly impossible for a human to perform with the same accuracy. “About 10 percent of our flagged images have some sort of problem,” he adds. “[In academic publishing] nobody has done this kind of comprehensive check [using AI] before,” says Petrariu. AIRA, he adds, facilitates Frontiers’ mission to make science open and knowledge accessible to all. Continue reading

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#437735 Robotic Chameleon Tongue Snatches Nearby ...

Chameleons may be slow-moving lizards, but their tongues can accelerate at astounding speeds, snatching insects before they have any chance of fleeing. Inspired by this remarkable skill, researchers in South Korea have developed a robotic tongue that springs forth quickly to snatch up nearby items.

They envision the tool, called Snatcher, being used by drones and robots that need to collect items without getting too close to them. “For example, a quadrotor with this manipulator will be able to snatch distant targets, instead of hovering and picking up,” explains Gwang-Pil Jung, a researcher at Seoul National University of Science and Technology (SeoulTech) who co-designed the new device.

There has been other research into robotic chameleon tongues, but what’s unique about Snatcher is that it packs chameleon-tongue fast snatching performance into a form factor that’s portable—the total size is 12 x 8.5 x 8.5 centimeters and it weighs under 120 grams. Still, it’s able to fast snatch up to 30 grams from 80 centimeters away in under 600 milliseconds.

Image: SeoulTech

The fast snatching deployable arm is powered by a wind-up spring attached to a motor (a series elastic actuator) combined with an active clutch. The clutch is what allows the single spring to drive both the shooting and the retracting.

To create Snatcher, Jung and a colleague at SeoulTech, Dong-Jun Lee, set about developing a spring-like device that’s controlled by an active clutch combined with a single series elastic actuator. Powered by a wind-up spring, a steel tapeline—analogous to a chameleon’s tongue—passes through two geared feeders. The clutch is what allows the single spring unwinding in one direction to drive both the shooting and the retracting, by switching a geared wheel between driving the forward feeder or the backward feeder.

The end result is a lightweight snatching device that can retrieve an object 0.8 meters away within 600 milliseconds. Jung notes that some other, existing devices designed for retrieval are capable of accomplishing the task quicker, at about 300 milliseconds, but these designs tend to be bulky. A more detailed description of Snatcher was published July 21 in IEEE Robotics and Automation Letters.

Photo: Dong-Jun Lee and Gwang-Pil Jung/SeoulTech

Snatcher’s relative small size means that it can be installed on a DJI Phantom drone. The researchers want to find out if their system can help make package delivery or retrieval faster and safer.

“Our final goal is to install the Snatcher to a commercial drone and achieve meaningful work, such as grasping packages,” says Jung. One of the challenges they still need to address is how to power the actuation system more efficiently. “To solve this issue, we are finding materials having high energy density.” Another improvement is designing a chameleon tongue-like gripper, replacing the simple hook that’s currently used to pick up objects. “We are planning to make a bi-stable gripper to passively grasp a target object as soon as the gripper contacts the object,” says Jung.

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