Tag Archives: perspective
#436484 If Machines Want to Make Art, Will ...
Assuming that the emergence of consciousness in artificial minds is possible, those minds will feel the urge to create art. But will we be able to understand it? To answer this question, we need to consider two subquestions: when does the machine become an author of an artwork? And how can we form an understanding of the art that it makes?
Empathy, we argue, is the force behind our capacity to understand works of art. Think of what happens when you are confronted with an artwork. We maintain that, to understand the piece, you use your own conscious experience to ask what could possibly motivate you to make such an artwork yourself—and then you use that first-person perspective to try to come to a plausible explanation that allows you to relate to the artwork. Your interpretation of the work will be personal and could differ significantly from the artist’s own reasons, but if we share sufficient experiences and cultural references, it might be a plausible one, even for the artist. This is why we can relate so differently to a work of art after learning that it is a forgery or imitation: the artist’s intent to deceive or imitate is very different from the attempt to express something original. Gathering contextual information before jumping to conclusions about other people’s actions—in art, as in life—can enable us to relate better to their intentions.
But the artist and you share something far more important than cultural references: you share a similar kind of body and, with it, a similar kind of embodied perspective. Our subjective human experience stems, among many other things, from being born and slowly educated within a society of fellow humans, from fighting the inevitability of our own death, from cherishing memories, from the lonely curiosity of our own mind, from the omnipresence of the needs and quirks of our biological body, and from the way it dictates the space- and time-scales we can grasp. All conscious machines will have embodied experiences of their own, but in bodies that will be entirely alien to us.
We are able to empathize with nonhuman characters or intelligent machines in human-made fiction because they have been conceived by other human beings from the only subjective perspective accessible to us: “What would it be like for a human to behave as x?” In order to understand machinic art as such—and assuming that we stand a chance of even recognizing it in the first place—we would need a way to conceive a first-person experience of what it is like to be that machine. That is something we cannot do even for beings that are much closer to us. It might very well happen that we understand some actions or artifacts created by machines of their own volition as art, but in doing so we will inevitably anthropomorphize the machine’s intentions. Art made by a machine can be meaningfully interpreted in a way that is plausible only from the perspective of that machine, and any coherent anthropomorphized interpretation will be implausibly alien from the machine perspective. As such, it will be a misinterpretation of the artwork.
But what if we grant the machine privileged access to our ways of reasoning, to the peculiarities of our perception apparatus, to endless examples of human culture? Wouldn’t that enable the machine to make art that a human could understand? Our answer is yes, but this would also make the artworks human—not authentically machinic. All examples so far of “art made by machines” are actually just straightforward examples of human art made with computers, with the artists being the computer programmers. It might seem like a strange claim: how can the programmers be the authors of the artwork if, most of the time, they can’t control—or even anticipate—the actual materializations of the artwork? It turns out that this is a long-standing artistic practice.
Suppose that your local orchestra is playing Beethoven’s Symphony No 7 (1812). Even though Beethoven will not be directly responsible for any of the sounds produced there, you would still say that you are listening to Beethoven. Your experience might depend considerably on the interpretation of the performers, the acoustics of the room, the behavior of fellow audience members or your state of mind. Those and other aspects are the result of choices made by specific individuals or of accidents happening to them. But the author of the music? Ludwig van Beethoven. Let’s say that, as a somewhat odd choice for the program, John Cage’s Imaginary Landscape No 4 (March No 2) (1951) is also played, with 24 performers controlling 12 radios according to a musical score. In this case, the responsibility for the sounds being heard should be attributed to unsuspecting radio hosts, or even to electromagnetic fields. Yet, the shaping of sounds over time—the composition—should be credited to Cage. Each performance of this piece will vary immensely in its sonic materialization, but it will always be a performance of Imaginary Landscape No 4.
Why should we change these principles when artists use computers if, in these respects at least, computer art does not bring anything new to the table? The (human) artists might not be in direct control of the final materializations, or even be able to predict them but, despite that, they are the authors of the work. Various materializations of the same idea—in this case formalized as an algorithm—are instantiations of the same work manifesting different contextual conditions. In fact, a common use of computation in the arts is the production of variations of a process, and artists make extensive use of systems that are sensitive to initial conditions, external inputs, or pseudo-randomness to deliberately avoid repetition of outputs. Having a computer executing a procedure to build an artwork, even if using pseudo-random processes or machine-learning algorithms, is no different than throwing dice to arrange a piece of music, or to pursuing innumerable variations of the same formula. After all, the idea of machines that make art has an artistic tradition that long predates the current trend of artworks made by artificial intelligence.
Machinic art is a term that we believe should be reserved for art made by an artificial mind’s own volition, not for that based on (or directed towards) an anthropocentric view of art. From a human point of view, machinic artworks will still be procedural, algorithmic, and computational. They will be generative, because they will be autonomous from a human artist. And they might be interactive, with humans or other systems. But they will not be the result of a human deferring decisions to a machine, because the first of those—the decision to make art—needs to be the result of a machine’s volition, intentions, and decisions. Only then will we no longer have human art made with computers, but proper machinic art.
The problem is not whether machines will or will not develop a sense of self that leads to an eagerness to create art. The problem is that if—or when—they do, they will have such a different Umwelt that we will be completely unable to relate to it from our own subjective, embodied perspective. Machinic art will always lie beyond our ability to understand it because the boundaries of our comprehension—in art, as in life—are those of the human experience.
This article was originally published at Aeon and has been republished under Creative Commons.
Image Credit: Rene Böhmer / Unsplash Continue reading
Posted in Human Robots
#436190 What Is the Uncanny Valley?
Have you ever encountered a lifelike humanoid robot or a realistic computer-generated face that seem a bit off or unsettling, though you can’t quite explain why?
Take for instance AVA, one of the “digital humans” created by New Zealand tech startup Soul Machines as an on-screen avatar for Autodesk. Watching a lifelike digital being such as AVA can be both fascinating and disconcerting. AVA expresses empathy through her demeanor and movements: slightly raised brows, a tilt of the head, a nod.
By meticulously rendering every lash and line in its avatars, Soul Machines aimed to create a digital human that is virtually undistinguishable from a real one. But to many, rather than looking natural, AVA actually looks creepy. There’s something about it being almost human but not quite that can make people uneasy.
Like AVA, many other ultra-realistic avatars, androids, and animated characters appear stuck in a disturbing in-between world: They are so lifelike and yet they are not “right.” This void of strangeness is known as the uncanny valley.
Uncanny Valley: Definition and History
The uncanny valley is a concept first introduced in the 1970s by Masahiro Mori, then a professor at the Tokyo Institute of Technology. The term describes Mori’s observation that as robots appear more humanlike, they become more appealing—but only up to a certain point. Upon reaching the uncanny valley, our affinity descends into a feeling of strangeness, a sense of unease, and a tendency to be scared or freaked out.
Image: Masahiro Mori
The uncanny valley as depicted in Masahiro Mori’s original graph: As a robot’s human likeness [horizontal axis] increases, our affinity towards the robot [vertical axis] increases too, but only up to a certain point. For some lifelike robots, our response to them plunges, and they appear repulsive or creepy. That’s the uncanny valley.
In his seminal essay for Japanese journal Energy, Mori wrote:
I have noticed that, in climbing toward the goal of making robots appear human, our affinity for them increases until we come to a valley, which I call the uncanny valley.
Later in the essay, Mori describes the uncanny valley by using an example—the first prosthetic hands:
One might say that the prosthetic hand has achieved a degree of resemblance to the human form, perhaps on a par with false teeth. However, when we realize the hand, which at first site looked real, is in fact artificial, we experience an eerie sensation. For example, we could be startled during a handshake by its limp boneless grip together with its texture and coldness. When this happens, we lose our sense of affinity, and the hand becomes uncanny.
In an interview with IEEE Spectrum, Mori explained how he came up with the idea for the uncanny valley:
“Since I was a child, I have never liked looking at wax figures. They looked somewhat creepy to me. At that time, electronic prosthetic hands were being developed, and they triggered in me the same kind of sensation. These experiences had made me start thinking about robots in general, which led me to write that essay. The uncanny valley was my intuition. It was one of my ideas.”
Uncanny Valley Examples
To better illustrate how the uncanny valley works, here are some examples of the phenomenon. Prepare to be freaked out.
1. Telenoid
Photo: Hiroshi Ishiguro/Osaka University/ATR
Taking the top spot in the “creepiest” rankings of IEEE Spectrum’s Robots Guide, Telenoid is a robotic communication device designed by Japanese roboticist Hiroshi Ishiguro. Its bald head, lifeless face, and lack of limbs make it seem more alien than human.
2. Diego-san
Photo: Andrew Oh/Javier Movellan/Calit2
Engineers and roboticists at the University of California San Diego’s Machine Perception Lab developed this robot baby to help parents better communicate with their infants. At 1.2 meters (4 feet) tall and weighing 30 kilograms (66 pounds), Diego-san is a big baby—bigger than an average 1-year-old child.
“Even though the facial expression is sophisticated and intuitive in this infant robot, I still perceive a false smile when I’m expecting the baby to appear happy,” says Angela Tinwell, a senior lecturer at the University of Bolton in the U.K. and author of The Uncanny Valley in Games and Animation. “This, along with a lack of detail in the eyes and forehead, can make the baby appear vacant and creepy, so I would want to avoid those ‘dead eyes’ rather than interacting with Diego-san.”
3. Geminoid HI
Photo: Osaka University/ATR/Kokoro
Another one of Ishiguro’s creations, Geminoid HI is his android replica. He even took hair from his own scalp to put onto his robot twin. Ishiguro says he created Geminoid HI to better understand what it means to be human.
4. Sophia
Photo: Mikhail Tereshchenko/TASS/Getty Images
Designed by David Hanson of Hanson Robotics, Sophia is one of the most famous humanoid robots. Like Soul Machines’ AVA, Sophia displays a range of emotional expressions and is equipped with natural language processing capabilities.
5. Anthropomorphized felines
The uncanny valley doesn’t only happen with robots that adopt a human form. The 2019 live-action versions of the animated film The Lion King and the musical Cats brought the uncanny valley to the forefront of pop culture. To some fans, the photorealistic computer animations of talking lions and singing cats that mimic human movements were just creepy.
Are you feeling that eerie sensation yet?
Uncanny Valley: Science or Pseudoscience?
Despite our continued fascination with the uncanny valley, its validity as a scientific concept is highly debated. The uncanny valley wasn’t actually proposed as a scientific concept, yet has often been criticized in that light.
Mori himself said in his IEEE Spectrum interview that he didn’t explore the concept from a rigorous scientific perspective but as more of a guideline for robot designers:
Pointing out the existence of the uncanny valley was more of a piece of advice from me to people who design robots rather than a scientific statement.
Karl MacDorman, an associate professor of human-computer interaction at Indiana University who has long studied the uncanny valley, interprets the classic graph not as expressing Mori’s theory but as a heuristic for learning the concept and organizing observations.
“I believe his theory is instead expressed by his examples, which show that a mismatch in the human likeness of appearance and touch or appearance and motion can elicit a feeling of eeriness,” MacDorman says. “In my own experiments, I have consistently reproduced this effect within and across sense modalities. For example, a mismatch in the human realism of the features of a face heightens eeriness; a robot with a human voice or a human with a robotic voice is eerie.”
How to Avoid the Uncanny Valley
Unless you intend to create creepy characters or evoke a feeling of unease, you can follow certain design principles to avoid the uncanny valley. “The effect can be reduced by not creating robots or computer-animated characters that combine features on different sides of a boundary—for example, human and nonhuman, living and nonliving, or real and artificial,” MacDorman says.
To make a robot or avatar more realistic and move it beyond the valley, Tinwell says to ensure that a character’s facial expressions match its emotive tones of speech, and that its body movements are responsive and reflect its hypothetical emotional state. Special attention must also be paid to facial elements such as the forehead, eyes, and mouth, which depict the complexities of emotion and thought. “The mouth must be modeled and animated correctly so the character doesn’t appear aggressive or portray a ‘false smile’ when they should be genuinely happy,” she says.
For Christoph Bartneck, an associate professor at the University of Canterbury in New Zealand, the goal is not to avoid the uncanny valley, but to avoid bad character animations or behaviors, stressing the importance of matching the appearance of a robot with its ability. “We’re trained to spot even the slightest divergence from ‘normal’ human movements or behavior,” he says. “Hence, we often fail in creating highly realistic, humanlike characters.”
But he warns that the uncanny valley appears to be more of an uncanny cliff. “We find the likability to increase and then crash once robots become humanlike,” he says. “But we have never observed them ever coming out of the valley. You fall off and that’s it.” Continue reading
Posted in Human Robots
#436044 Want a Really Hard Machine Learning ...
What’s the world’s hardest machine learning problem? Autonomous vehicles? Robots that can walk? Cancer detection?
Nope, says Julian Sanchez. It’s agriculture.
Sanchez might be a little biased. He is the director of precision agriculture for John Deere, and is in charge of adding intelligence to traditional farm vehicles. But he does have a little perspective, having spent time working on software for both medical devices and air traffic control systems.
I met with Sanchez and Alexey Rostapshov, head of digital innovation at John Deere Labs, at the organization’s San Francisco offices last month. Labs launched in 2017 to take advantage of the area’s tech expertise, both to apply machine learning to in-house agricultural problems and to work with partners to build technologies that play nicely with Deere’s big green machines. Deere’s neighbors in San Francisco’s tech-heavy South of Market are LinkedIn, Salesforce, and Planet Labs, which puts it in a good position for recruiting.
“We’ve literally had folks knock on the door and say, ‘What are you doing here?’” says Rostapshov, and some return to drop off resumes.
Here’s why Sanchez believes agriculture is such a big challenge for artificial intelligence.
“It’s not just about driving tractors around,” he says, although autonomous driving technologies are part of the mix. (John Deere is doing a lot of work with precision GPS to improve autonomous driving, for example, and allow tractors to plan their own routes around fields.)
But more complex than the driving problem, says Sanchez, are the classification problems.
Corn: A Classic Classification Problem
Photo: Tekla Perry
One key effort, Sanchez says, are AI systems “that allow me to tell whether grain being harvested is good quality or low quality and to make automatic adjustment systems for the harvester.” The company is already selling an early version of this image analysis technology. But the many differences between grain types, and grains grown under different conditions, make this task a tough one for machine learning.
“Take corn,” Sanchez says. “Let’s say we are building a deep learning algorithm to detect this corn. And we take lots of pictures of kernels to give it. Say we pick those kernels in central Illinois. But, one mile over, the farmer planted a slightly different hybrid which has slightly different coloration of yellow. Meanwhile, this other farm harvested three days later in a field five miles away; it’s the same hybrid, but it also looks different.
“It’s an overwhelming classification challenge, and that’s just for corn. But you are not only doing it for corn, you have to add 20 more varieties of grain to the mix; and some, like canola, are almost microscopic.”
Even the ground conditions vary dramatically—far more than road conditions, Sanchez points out.
“Let’s say we are building a deep learning algorithm to detect how much residue is left on the soil after a harvest, including stubble and some chaff. Let’s drive 2,000 acres of fields in the Midwest looking at residue. That’s great, but I guarantee that if you go drive those the next year, it will look significantly different.
“Deep learning is great at interpolating conditions between what it knows; it is not good at extrapolating to situations it hasn’t seen. And in agriculture, you always feel that there is a set of conditions that you haven’t yet classified.”
A Flood of Big Data
The scale of the data is also daunting, Rostapshov points out. “We are one of the largest users of cloud computing services in the world,” he says. “We are gathering 5 to 15 million measurements per second from 130,000 connected machines globally. We have over 150 million acres in our databases, using petabytes and petabytes [of storage]. We process more data than Twitter does.”
Much of this information is so-called dirty data, that is, it doesn’t share the same format or structure, because it’s coming not only from a wide variety of John Deere machines, but also includes data from some 100 other companies that have access to the platform, including weather information, aerial imagery, and soil analyses.
As a result, says Sanchez, Deere has had to make “tremendous investments in back-end data cleanup.”
Deep learning is great at interpolating conditions between what it knows; it is not good at extrapolating to situations it hasn’t seen.”
—Julian Sanchez, John Deere
“We have gotten progressively more skilled at that problem,” he says. “We started simply by cleaning up our own data. You’d think it would be nice and neat, since it’s coming from our own machines, but there is a wide variety of different models and different years. Then we started geospatially tagging the agronomic data—the information about where you are applying herbicides and fertilizer and the like—coming in from our vehicles. When we started bringing in other data, from drones, say, we were already good at cleaning it up.”
John Deere’s Hiring Pitch
Hard problems can be a good thing to have for a company looking to hire machine learning engineers.
“Our opening line to potential recruits,” Sanchez says, “is ‘This stuff matters.’ Then, if we get a chance to talk to them more, we follow up with ‘Not only does this stuff matter, but the problems are really hard and interesting.’ When we explain the variability in farming and how we have to apply all the latest tools to these problems, we get their attention.”
Software engineers “know that feeding a growing population is a massive problem and are excited about the prospect of making a difference,” Rostapshov says.
Only 20 engineers work in the San Francisco labs right now, and that’s on a busy day—some of the researchers spend part of their time at Blue River Technology, a startup based in Sunnyvale that was acquired by Deere in 2017. About half of the researchers are focusing on AI. The Lab is in the process of doubling its office space (no word on staffing plans for that expansion yet).
“We are one of the largest users of cloud computing services in the world.”
—Alexey Rostapshov, John Deere Labs
Company-wide, Deere has thousands of software engineers, with many using AI and machine learning tools in their work, and about the same number of mechanical and electrical engineers, Sanchez reports. “If you look at our hiring 10 years ago,” he says, “it was heavily weighted to mechanical engineers. But if you look at those numbers now, it is by a large majority [engineers working] in the software space. We still need mechanical engineers—we do build green machines—but if you go by our footprint of tech talent, it is pretty safe to call John Deere a software company. And if you follow the key conversations that are happening in the company right now, 95 percent of them are software-related.”
For now, these software engineers are focused on developing technologies that allow farmers to “do more with less,” Sanchez says. Meaning, to get more and better crops from less fuel, less seed, less fertilizer, less pesticide, and fewer workers, and putting together building blocks that, he says, could eventually lead to fully autonomous farm vehicles. The data Deere collects today, for the most part, stays in silos (the virtual kind), with AI algorithms that analyze specific sets of data to provide guidance to individual farmers. At some point, however, with tools to anonymize data and buy-in from farmers, aggregating data could provide some powerful insights.
“We are not asking farmers for that yet,” Sanchez says. “We are not doing aggregation to look for patterns. We are focused on offering technology that allows an individual farmer to use less, on positioning ourselves to be in a neutral spot. We are not about selling you more seed or more fertilizer. So we are building up a good trust level. In the long term, we can have conversations about doing more with deep learning.” Continue reading
Posted in Human Robots
#435769 The Ultimate Optimization Problem: How ...
Lucas Joppa thinks big. Even while gazing down into his cup of tea in his modest office on Microsoft’s campus in Redmond, Washington, he seems to see the entire planet bobbing in there like a spherical tea bag.
As Microsoft’s first chief environmental officer, Joppa came up with the company’s AI for Earth program, a five-year effort that’s spending US $50 million on AI-powered solutions to global environmental challenges.
The program is not just about specific deliverables, though. It’s also about mindset, Joppa told IEEE Spectrum in an interview in July. “It’s a plea for people to think about the Earth in the same way they think about the technologies they’re developing,” he says. “You start with an objective. So what’s our objective function for Earth?” (In computer science, an objective function describes the parameter or parameters you are trying to maximize or minimize for optimal results.)
Photo: Microsoft
Lucas Joppa
AI for Earth launched in December 2017, and Joppa’s team has since given grants to more than 400 organizations around the world. In addition to receiving funding, some grantees get help from Microsoft’s data scientists and access to the company’s computing resources.
In a wide-ranging interview about the program, Joppa described his vision of the “ultimate optimization problem”—figuring out which parts of the planet should be used for farming, cities, wilderness reserves, energy production, and so on.
Every square meter of land and water on Earth has an infinite number of possible utility functions. It’s the job of Homo sapiens to describe our overall objective for the Earth. Then it’s the job of computers to produce optimization results that are aligned with the human-defined objective.
I don’t think we’re close at all to being able to do this. I think we’re closer from a technology perspective—being able to run the model—than we are from a social perspective—being able to make decisions about what the objective should be. What do we want to do with the Earth’s surface?
Such questions are increasingly urgent, as climate change has already begun reshaping our planet and our societies. Global sea and air surface temperatures have already risen by an average of 1 degree Celsius above preindustrial levels, according to the Intergovernmental Panel on Climate Change.
Today, people all around the world participated in a “climate strike,” with young people leading the charge and demanding a global transition to renewable energy. On Monday, world leaders will gather in New York for the United Nations Climate Action Summit, where they’re expected to present plans to limit warming to 1.5 degrees Celsius.
Joppa says such summit discussions should aim for a truly holistic solution.
We talk about how to solve climate change. There’s a higher-order question for society: What climate do we want? What output from nature do we want and desire? If we could agree on those things, we could put systems in place for optimizing our environment accordingly. Instead we have this scattered approach, where we try for local optimization. But the sum of local optimizations is never a global optimization.
There’s increasing interest in using artificial intelligence to tackle global environmental problems. New sensing technologies enable scientists to collect unprecedented amounts of data about the planet and its denizens, and AI tools are becoming vital for interpreting all that data.
The 2018 report “Harnessing AI for the Earth,” produced by the World Economic Forum and the consulting company PwC, discusses ways that AI can be used to address six of the world’s most pressing environmental challenges (climate change, biodiversity, and healthy oceans, water security, clean air, and disaster resilience).
Many of the proposed applications involve better monitoring of human and natural systems, as well as modeling applications that would enable better predictions and more efficient use of natural resources.
Joppa says that AI for Earth is taking a two-pronged approach, funding efforts to collect and interpret vast amounts of data alongside efforts that use that data to help humans make better decisions. And that’s where the global optimization engine would really come in handy.
For any location on earth, you should be able to go and ask: What’s there, how much is there, and how is it changing? And more importantly: What should be there?
On land, the data is really only interesting for the first few hundred feet. Whereas in the ocean, the depth dimension is really important.
We need a planet with sensors, with roving agents, with remote sensing. Otherwise our decisions aren’t going to be any good.
AI for Earth isn’t going to create such an online portal within five years, Joppa stresses. But he hopes the projects that he’s funding will contribute to making such a portal possible—eventually.
We’re asking ourselves: What are the fundamental missing layers in the tech stack that would allow people to build a global optimization engine? Some of them are clear, some are still opaque to me.
By the end of five years, I’d like to have identified these missing layers, and have at least one example of each of the components.
Some of the projects that AI for Earth has funded seem to fit that desire. Examples include SilviaTerra, which used satellite imagery and AI to create a map of the 92 billion trees in forested areas across the United States. There’s also OceanMind, a non-profit that detects illegal fishing and helps marine authorities enforce compliance. Platforms like Wildbook and iNaturalist enable citizen scientists to upload pictures of animals and plants, aiding conservation efforts and research on biodiversity. And FarmBeats aims to enable data-driven agriculture with low-cost sensors, drones, and cloud services.
It’s not impossible to imagine putting such services together into an optimization engine that knows everything about the land, the water, and the creatures who live on planet Earth. Then we’ll just have to tell that engine what we want to do about it.
Editor’s note: This story is published in cooperation with more than 250 media organizations and independent journalists that have focused their coverage on climate change ahead of the UN Climate Action Summit. IEEE Spectrum’s participation in the Covering Climate Now partnership builds on our past reporting about this global issue. Continue reading
Posted in Human Robots
#435748 Video Friday: This Robot Is Like a ...
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 2019 – June 22-26, 2019 – Freiburg, Germany
Hamlyn Symposium on Medical Robotics – June 23-26, 2019 – London, U.K.
ETH Robotics Summer School – June 27-1, 2019 – Zurich, Switzerland
MARSS 2019 – July 1-5, 2019 – Helsinki, Finland
ICRES 2019 – July 29-30, 2019 – London, U.K.
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, Pa., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.
It’s been a while since we last spoke to Joe Jones, the inventor of Roomba, about his solar-powered, weed-killing robot, called Tertill, which he was launching as a Kickstarter project. Tertill is now available for purchase (US $300) and is shipping right now.
[ Tertill ]
Usually, we don’t post videos that involve drone use that looks to be either illegal or unsafe. These flights over the protests in Hong Kong are almost certainly both. However, it’s also a unique perspective on the scale of these protests.
[ Team BlackSheep ]
ICYMI: iRobot announced this week that it has acquired Root Robotics.
[ iRobot ]
This Boston Dynamics parody video went viral this week.
The CGI is good but the gratuitous violence—even if it’s against a fake robot—is a bit too much?
This is still our favorite Boston Dynamics parody video:
[ Corridor ]
Biomedical Engineering Department Head Bin He and his team have developed the first-ever successful non-invasive mind-controlled robotic arm to continuously track a computer cursor.
[ CMU ]
Organic chemists, prepare to meet your replacement:
Automated chemical synthesis carries great promises of safety, efficiency and reproducibility for both research and industry laboratories. Current approaches are based on specifically-designed automation systems, which present two major drawbacks: (i) existing apparatus must be modified to be integrated into the automation systems; (ii) such systems are not flexible and would require substantial re-design to handle new reactions or procedures. In this paper, we propose a system based on a robot arm which, by mimicking the motions of human chemists, is able to perform complex chemical reactions without any modifications to the existing setup used by humans. The system is capable of precise liquid handling, mixing, filtering, and is flexible: new skills and procedures could be added with minimum effort. We show that the robot is able to perform a Michael reaction, reaching a yield of 34%, which is comparable to that obtained by a junior chemist (undergraduate student in Chemistry).
[ arXiv ] via [ NTU ]
So yeah, ICRA 2019 was huge and awesome. Here are some brief highlights.
[ Montreal Gazette ]
For about US $5, this drone will deliver raw meat and beer to you if you live on an uninhabited island in Tokyo Bay.
[ Nikkei ]
The Smart Microsystems Lab at Michigan State University has a new version of their Autonomous Surface Craft. It’s autonomous, open source, and awfully hard to sink.
[ SML ]
As drone shows go, this one is pretty good.
[ CCTV ]
Here’s a remote controlled robot shooting stuff with a very large gun.
[ HDT ]
Over a period of three quarters (September 2018 thru May 2019), we’ve had the opportunity to work with five graduating University of Denver students as they brought their idea for a Misty II arm extension to life.
[ Misty Robotics ]
If you wonder how it looks to inspect burners and superheaters of a boiler with an Elios 2, here you are! This inspection was performed by Svenska Elektrod in a peat-fired boiler for Vattenfall in Sweden. Enjoy!
[ Flyability ]
The newest Soft Robotics technology, mGrip mini fingers, made for tight spaces, small packaging, and delicate items, giving limitless opportunities for your applications.
[ Soft Robotics ]
What if legged robots were able to generate dynamic motions in real-time while interacting with a complex environment? Such technology would represent a significant step forward the deployment of legged systems in real world scenarios. This means being able to replace humans in the execution of dangerous tasks and to collaborate with them in industrial applications.
This workshop aims to bring together researchers from all the relevant communities in legged locomotion such as: numerical optimization, machine learning (ML), model predictive control (MPC) and computational geometry in order to chart the most promising methods to address the above-mentioned scientific challenges.
[ Num Opt Wkshp ]
Army researchers teamed with the U.S. Marine Corps to fly and test 3-D printed quadcopter prototypes a the Marine Corps Air Ground Combat Center in 29 Palms, California recently.
[ CCDC ARL ]
Lex Fridman’s Artificial Intelligence podcast featuring Rosalind Picard.
[ AI Podcast ]
In this week’s episode of Robots in Depth, per speaks with Christian Guttmann, executive director of the Nordic AI Artificial Intelligence Institute.
Christian Guttmann talks about AI and wanting to understand intelligence enough to recreate it. Christian has be focusing on AI in healthcare and has recently started to communicate the opportunities and challenges in artificial intelligence to the general public. This is something that the host Per Sjöborg is also very passionate about. We also get to hear about the Nordic AI institute and the work it does to inform all parts of society about AI.
[ Robots in Depth ] Continue reading
Posted in Human Robots