Josh Tenenbaum named Innovator of the Year by R&D Magazine

R&D Magazine has named Josh Tenenbaum the 2018 Innovator of the Year. Tenenbaum, a professor of computational cognitive science in the Department of Brain and Cognitive Sciences at MIT, was recognized for his work studying the nature and origins of intelligence in the human mind and brain and applying that knowledge to build more human-like intelligence in machines. The award, part of the annual R&D 100 Awards, honor pioneers in science and technology.

“We selected Josh as our 2018 Innovator of the Year not only because of his accomplishments in the fields of cognitive science and artificial intelligence, but because of his willingness to partner with experts across the board — from computer scientists and engineers to neuroscientists and cognitive psychologists — in the name of innovation,” said Bea Riemschneider, editorial director of R&D Magazine in a press release announcing the award.

Tenenbaum’s research currently focuses on two areas: describing the structure, content, and development of people’s commonsense theories, especially intuitive physics and intuitive psychology, and understanding how people are able to learn and generalize new concepts, models, theories and tasks from very few examples — often called “one-shot learning.” Through a combination of mathematical modeling, computer simulation and behavioral experiments, his team works to uncover the logic behind our everyday inductive leaps: constructing perceptual representations, separating “style” and “content” in perception, learning concepts and words, judging similarity or representativeness, inferring causal connections, noticing coincidences, and predicting the future.

“In a long tradition of recognizing great scientists and engineers, R&D Magazine stands for excellence both in basic science and engineering and for making a bridge between those fields,” said Tenenbaum. “It’s especially exciting to receive this award because here at MIT we are trying to do exactly that — build a bridge between science and engineering to address great questions of intelligence around how human intelligence arises in the mind and brain and how we can use those insights to build smarter, more human-like forms of intelligence in machines so we are truly smarter and better-off.”

Tenenbaum is a member of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Center for Brains, Minds and Machines (CBMM). He also leads the Computational Cognitive Science lab at MIT and is a scientific director for the MIT Quest for Intelligence Core.


Robots with sticky feet can climb up, down, and all around

Jet engines can have up to 25,000 individual parts, making regular maintenance a tedious task that can take over a month per engine. Many components are located deep inside the engine and cannot be inspected without taking the machine apart, adding time and costs to maintenance. This problem is not only confined to jet engines, either; many complicated, expensive machines like construction equipment, generators, and scientific instruments require large investments of time and money to inspect and maintain.

Researchers at Harvard University’s Wyss Institute for Biologically Inspired Engineering and John A. Paulson School of Engineering and Applied Sciences (SEAS) have created a micro-robot whose electroadhesive foot pads, origami ankle joints, and specially engineered walking gait allow it to climb on vertical and upside-down conductive surfaces, like the inside walls of a commercial jet engine. The work is reported in Science Robotics.

“Now that these robots can explore in three dimensions instead of just moving back and forth on a flat surface, there’s a whole new world that they can move around in and engage with,” said first author Sébastien de Rivaz, a former Research Fellow at the Wyss Institute and SEAS who now works at Apple. “They could one day enable non-invasive inspection of hard-to-reach areas of large machines, saving companies time and money and making those machines safer.”

The new robot, called HAMR-E (Harvard Ambulatory Micro-Robot with Electroadhesion), was developed in response to a challenge issued to the Harvard Microrobotics Lab by Rolls-Royce, which asked if it would be possible to design and build an army of micro-robots capable of climbing inside parts of its jet engines that are inaccessible to human workers. Existing climbing robots can tackle vertical surfaces, but experience problems when trying to climb upside-down, as they require a large amount of adhesive force to prevent them from falling.

The team based HAMR-E on one of its existing micro-robots, HAMR, whose four legs enable it to walk on flat surfaces and swim through water. While the basic design of HAMR-E is similar to HAMR, the scientists had to solve a series of challenges to get HAMR-E to successfully stick to and traverse the vertical, inverted, and curved surfaces that it would encounter in a jet engine.

First, they needed to create adhesive foot pads that would keep the robot attached to the surface even when upside-down, but also release to allow the robot to “walk” by lifting and placing its feet. The pads consist of a polyimide-insulated copper electrode, which enables the generation of electrostatic forces between the pads and the underlying conductive surface. The foot pads can be easily released and re-engaged by switching the electric field on and off, which operates at a voltage similar to that required to move the robot’s legs, thus requiring very little additional power. The electroadhesive foot pads can generate shear forces of 5.56 grams and normal forces of 6.20 grams — more than enough to keep the 1.48-gram robot from sliding down or falling off its climbing surface. In addition to providing high adhesive forces, the pads were designed to be able to flex, thus allowing the robot to climb on curved or uneven surfaces.

The scientists also created new ankle joints for HAMR-E that can rotate in three dimensions to compensate for rotations of its legs as it walks, allowing it to maintain its orientation on its climbing surface. The joints were manufactured out of layered fiberglass and polyimide, and folded into an origami-like structure that allows the ankles of all the legs to rotate freely, and to passively align with the terrain as HAMR-E climbs.

Finally, the researchers created a special walking pattern for HAMR-E, as it needs to have three foot pads touching a vertical or inverted surface at all times to prevent it from falling or sliding off. One foot releases from the surface, swings forward, and reattaches while the remaining three feet stay attached to the surface. At the same time, a small amount of torque is applied by the foot diagonally across from the lifted foot to keep the robot from moving away from the climbing surface during the leg-swinging phase. This process is repeated for the three other legs to create a full walking cycle, and is synchronized with the pattern of electric field switching on each foot.

When HAMR-E was tested on vertical and inverted surfaces, it was able to achieve more than one hundred steps in a row without detaching. It walked at speeds comparable to other small climbing robots on inverted surfaces and slightly slower than other climbing robots on vertical surfaces, but was significantly faster than other robots on horizontal surfaces, making it a good candidate for exploring environments that have a variety of surfaces in different arrangements in space. It is also able to perform 180-degree turns on horizontal surfaces.

HAMR-E also successfully maneuvered around a curved, inverted section of a jet engine while staying attached, and its passive ankle joints and adhesive foot pads were able to accommodate the rough and uneven features of the engine surface simply by increasing the electroadhesion voltage.

The team is continuing to refine HAMR-E, and plans to incorporate sensors into its legs that can detect and compensate for detached foot pads, which will help prevent it from falling off of vertical or inverted surfaces. HAMR-E’s payload capacity is also greater than its own weight, opening the possibility of carrying a power supply and other electronics and sensors to inspect various environments. The team is also exploring options for using HAMR-E on non-conductive surfaces.

“This iteration of HAMR-E is the first and most convincing step towards showing that this approach to a centimeter-scale climbing robot is possible, and that such robots could in the future be used to explore any sort of infrastructure, including buildings, pipes, engines, generators, and more,” said corresponding author Robert Wood, Ph.D., who is a Founding Core Faculty member of the Wyss Institute as well as the Charles River Professor of Engineering and Applied Sciences at SEAS.

“While academic scientists are very good at coming up with fundamental questions to explore in the lab, sometimes collaborations with industrial scientists who understand real-world problems are required to develop innovative technologies that can be translated into useful products. We are excited to help catalyze these collaborations here at the Wyss Institute, and to see the breakthrough advances that emerge,” said Wyss Founding Director Donald Ingber, M.D., Ph.D., who is also the Judah Folkman Professor of Vascular Biology at Harvard Medical School and the Vascular Biology Program at Boston Children’s Hospital, and Professor of Bioengineering at SEAS.

New models sense human trust in smart machines

New “classification models” sense how well humans trust intelligent machines they collaborate with, a step toward improving the quality of interactions and teamwork.

The long-term goal of the overall field of research is to design intelligent machines capable of changing their behavior to enhance human trust in them. The new models were developed in research led by assistant professor Neera Jain and associate professor Tahira Reid, in Purdue University’s School of Mechanical Engineering.

“Intelligent machines, and more broadly, intelligent systems are becoming increasingly common in the everyday lives of humans,” Jain said. “As humans are increasingly required to interact with intelligent systems, trust becomes an important factor for synergistic interactions.”

For example, aircraft pilots and industrial workers routinely interact with automated systems. Humans will sometimes override these intelligent machines unnecessarily if they think the system is faltering.

“It is well established that human trust is central to successful interactions between humans and machines,” Reid said.

The researchers have developed two types of “classifier-based empirical trust sensor models,” a step toward improving trust between humans and intelligent machines.

A YouTube video is available at https://www.youtube.com/watch?v=Mucl6pAgEQg.

The work aligns with Purdue’s Giant Leaps celebration, acknowledging the university’s global advancements made in AI, algorithms and automation as part of Purdue’s 150th anniversary. This is one of the four themes of the yearlong celebration’s Ideas Festival, designed to showcase Purdue as an intellectual center solving real-world issues.

The models use two techniques that provide data to gauge trust: electroencephalography and galvanic skin response. The first records brainwave patterns, and the second monitors changes in the electrical characteristics of the skin, providing psychophysiological “feature sets” correlated with trust.

Forty-five human subjects donned wireless EEG headsets and wore a device on one hand to measure galvanic skin response.

One of the new models, a “general trust sensor model,” uses the same set of psychophysiological features for all 45 participants. The other model is customized for each human subject, resulting in improved mean accuracy but at the expense of an increase in training time. The two models had a mean accuracy of 71.22 percent, and 78.55 percent, respectively.

It is the first time EEG measurements have been used to gauge trust in real time, or without delay.

“We are using these data in a very new way,” Jain said. “We are looking at it in sort of a continuous stream as opposed to looking at brain waves after a specific trigger or event.”

Findings are detailed in a research paper appearing in a special issue of the Association for Computing Machinery’s Transactions on Interactive Intelligent Systems. The journal’s special issue is titled “Trust and Influence in Intelligent Human-Machine Interaction.” The paper was authored by mechanical engineering graduate student Kumar Akash; former graduate student Wan-Lin Hu, who is now a postdoctoral research associate at Stanford University; Jain and Reid.

“We are interested in using feedback-control principles to design machines that are capable of responding to changes in human trust level in real time to build and manage trust in the human-machine relationship,” Jain said. “In order to do this, we require a sensor for estimating human trust level, again in real-time. The results presented in this paper show that psychophysiological measurements could be used to do this.”

The issue of human trust in machines is important for the efficient operation of “human-agent collectives.”

“The future will be built around human-agent collectives that will require efficient and successful coordination and collaboration between humans and machines,” Jain said. “Say there is a swarm of robots assisting a rescue team during a natural disaster. In our work we are dealing with just one human and one machine, but ultimately we hope to scale up to teams of humans and machines.”

Algorithms have been introduced to automate various processes.

“But we still have humans there who monitor what’s going on,” Jain said. “There is usually an override feature, where if they think something isn’t right they can take back control.”

Sometimes this action isn’t warranted.

“You have situations in which humans may not understand what is happening so they don’t trust the system to do the right thing,” Reid said. “So they take back control even when they really shouldn’t.”

In some cases, for example in the case of pilots overriding the autopilot, taking back control might actually hinder safe operation of the aircraft, causing accidents.

“A first step toward designing intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real time,” Jain said.

To validate their method, 581 online participants were asked to operate a driving simulation in which a computer identified road obstacles. In some scenarios, the computer correctly identified obstacles 100 percent of the time, whereas in other scenarios the computer incorrectly identified the obstacles 50 percent of the time.

“So, in some cases it would tell you there is an obstacle, so you hit the brakes and avoid an accident, but in other cases it would incorrectly tell you an obstacle exists when there was none, so you hit the breaks for no reason,” Reid said.

The testing allowed the researchers to identify psychophysiological features that are correlated to human trust in intelligent systems, and to build a trust sensor model accordingly. “We hypothesized that the trust level would be high in reliable trials and be low in faulty trials, and we validated this hypothesis using responses collected from 581 online participants,” she said.

The results validated that the method effectively induced trust and distrust in the intelligent machine.

“In order to estimate trust in real time, we require the ability to continuously extract and evaluate key psychophysiological measurements,” Jain said. “This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor.”

The EEG headset records signals over nine channels, each channel picking up different parts of the brain.

“Everyone’s brainwaves are different, so you need to make sure you are building a classifier that works for all humans.”

For autonomous systems, human trust can be classified into three categories: dispositional, situational, and learned.

Dispositional trust refers to the component of trust that is dependent on demographics such as gender and culture, which carry potential biases.

“We know there are probably nuanced differences that should be taken into consideration,” Reid said. “Women trust differently than men, for example, and trust also may be affected by differences in age and nationality.”

Situational trust may be affected by a task’s level of risk or difficulty, while learned is based on the human’s past experience with autonomous systems.

The models they developed are called classification algorithms.

“The idea is to be able to use these models to classify when someone is likely feeling trusting versus likely feeling distrusting,” she said.

Jain and Reid have also investigated dispositional trust to account for gender and cultural differences, as well as dynamic models able to predict how trust will change in the future based on the data.

Mountain splendor? Scientists know where your eyes will look

Using precise brain measurements, Yale researchers predicted how people’s eyes move when viewing natural scenes, an advance in understanding the human visual system that can improve a host of artificial intelligence efforts, such as the development of driverless cars, said the researchers.

“We are visual beings and knowing how the brain rapidly computes where to look is fundamentally important,” said Yale’s Marvin Chun, Richard M. Colgate Professor of Psychology, professor of neuroscience and co-author of new research published Dec. 4 in the journal Nature Communications.

Eye movements have been extensively studied, and researchers can tell with some certainty where a gaze will be directed at different elements in the environment. What hasn’t been understood is how the brain orchestrates this ability, which is so fundamental to survival.

In a previous example of “mind reading,” Chun’s group successfully reconstructed facial images viewed while people were scanned in an MRI machine, based on their brain imaging data alone.

In the new paper, Chun and lead author Thomas P. O’Connell took a similar approach and showed that by analyzing the brain responses to complex, natural scenes, they could predict where people would direct their attention and gaze. This was made possible by analyzing the brain data with deep convolutional neural networks — models that are extensively used in artificial intelligence (AI).

“The work represents a perfect marriage of neuroscience and data science,” Chun said.

The findings have a myriad of potential applications — such as testing competing artificial intelligence systems that categorize images and guide driverless cars.

“People can see better than AI systems can,” Chun said. “Understanding how the brain performs its complex calculations is an ultimate goal of neuroscience and benefits AI efforts.”

 

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Materials provided by Yale University. Original written by Bill Hathaway. Note: Content may be edited for style and length.

Computers successfully trained to identify animals in photos

A computer model developed at the University of Wyoming by UW researchers and others has demonstrated remarkable accuracy and efficiency in identifying images of wild animals from camera-trap photographs in North America.

The artificial-intelligence breakthrough, detailed in a paper published in the scientific journal Methods in Ecology and Evolution, is described as a significant advancement in the study and conservation of wildlife. The computer model is now available in a software package for Program R, a widely used programming language and free software environment for statistical computing.

“The ability to rapidly identify millions of images from camera traps can fundamentally change the way ecologists design and implement wildlife studies,” says the paper, whose lead authors are recent UW Department of Zoology and Physiology Ph.D. graduate Michael Tabak and Ryan Miller, both of the U.S. Department of Agriculture’s Center for Epidemiology and Animal Health in Fort Collins, Colo.

The study builds on UW research published earlier this year in the Proceedings of the National Academy of Sciences (PNAS) in which a computer model analyzed 3.2 million images captured by camera traps in Africa by a citizen science project called Snapshot Serengeti. The artificial-intelligence technique called deep learning categorized animal images at a 96.6 percent accuracy rate, the same as teams of human volunteers achieved, at a much more rapid pace than did the people.

In the latest study, the researchers trained a deep neural network on Mount Moran, UW’s high-performance computer cluster, to classify wildlife species using 3.37 million camera-trap images of 27 species of animals obtained from five states across the United States. The model then was tested on nearly 375,000 animal images at a rate of about 2,000 images per minute on a laptop computer, achieving 97.6 percent accuracy — likely the highest accuracy to date in using machine learning for wildlife image classification.

The computer model also was tested on an independent subset of 5,900 images of moose, cattle, elk and wild pigs from Canada, producing an accuracy rate of 81.8 percent. And it was 94 percent successful in removing “empty” images (without any animals) from a set of photographs from Tanzania.

The researchers have made their model freely available in a software package in Program R. The package, “Machine Learning for Wildlife Image Classification in R (MLWIC),” allows other users to classify their images containing the 27 species in the dataset, but it also allows users to train their own machine learning models using images from new datasets.

The lead author of the PNAS article, recent UW computer science Ph.D. graduate Mohammad Sadegh (Arash) Norouzzadeh, is one of multiple contributors to the new paper in Methods in Ecology and Evolution. Other participating researchers from UW are Department of Computer Science Associate Professor Jeff Clune and postdoctoral researcher Elizabeth Mandeville of the Wyoming Cooperative Fish and Wildlife Research Unit.

Other organizations represented in the research group are the USDA’s National Wildlife Research Center, Arizona State University, California’s Tejon Ranch Conservancy, the University of Georgia, the University of Florida, Colorado Parks and Wildlife, the University of Saskatchewan and the University of Montana.

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Smarter AI: Machine learning without negative data

A research team from the RIKEN Center for Advanced Intelligence Project (AIP) has successfully developed a new method for machine learning that allows an AI to make classifications without what is known as “negative data,” a finding which could lead to wider application to a variety of classification tasks.

Classifying things is critical for our daily lives. For example, we have to detect spam mail, fake political news, as well as more mundane things such as objects or faces. When using AI, such tasks are based on “classification technology” in machine learning — having the computer learn using the boundary separating positive and negative data. For example, “positive” data would be photos including a happy face, and “negative” data photos that include a sad face. Once a classification boundary is learned, the computer can determine whether a certain data is positive or negative. The difficulty with this technology is that it requires both positive and negative data for the learning process, and negative data are not available in many cases (for instance, it is hard to find photos with the label, “this photo includes a sad face,” since most people smile in front of a camera.)

In terms of real-life programs, when a retailer is trying to predict who will make a purchase, it can easily find data on customers who purchased from them (positive data), but it is basically impossible to obtain data on customers who did not purchase from them (negative data), since they do not have access to their competitors’ data. Another example is a common task for app developers: they need to predict which users will continue using the app (positive) or stop (negative). However, when a user unsubscribes, the developers lose the user’s data because they have to completely delete data regarding that user in accordance with the privacy policy to protect personal information.

According to lead author Takashi Ishida from RIKEN AIP, “Previous classification methods could not cope with the situation where negative data were not available, but we have made it possible for computers to learn with only positive data, as long as we have a confidence score for our positive data, constructed from information such as buying intention or the active rate of app users. Using our new method, we can let computers learn a classifier only from positive data equipped with confidence.”

Ishida proposed, together with researcher Niu Gang from his group and team leader Masashi Sugiyama, that they let computers learn well by adding the confidence score, which mathematically corresponds to the probability whether the data belongs to a positive class or not. They succeeded in developing a method that can let computers learn a classification boundary only from positive data and information on its confidence (positive reliability) against classification problems of machine learning that divide data positively and negatively.

To see how well the system functioned, they used it on a set of photos that contains various labels of fashion items. For example, they chose “T-shirt,” as the positive class and one other item, e.g., “sandal,” as the negative class. Then they attached a confidence score to the “T-shirt” photos. They found that without accessing the negative data (e.g., “sandal” photos), in some cases, their method was just as good as a method that involves using positive and negative data.

According to Ishida, “This discovery could expand the range of applications where classification technology can be used. Even in fields where machine learning has been actively used, our classification technology could be used in new situations where only positive data can be gathered due to data regulation or business constraints. In the near future, we hope to put our technology to use in various research fields, such as natural language processing, computer vision, robotics, and bioinformatics.”

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Android child’s face strikingly expressive

Japan’s affection for robots is no secret. But is the feeling mutual in the country’s amazing androids? We may now be a step closer to giving androids greater facial expressions to communicate with.

While robots have featured in advances in healthcare, industrial, and other settings in Japan, capturing humanistic expression in a robotic face remains an elusive challenge. Although their system properties have been generally addressed, androids’ facial expressions have not been examined in detail. This is owing to factors such as the huge range and asymmetry of natural human facial movements, the restrictions of materials used in android skin, and of course the intricate engineering and mathematics driving robots’ movements.

A trio of researchers at Osaka University has now found a method for identifying and quantitatively evaluating facial movements on their android robot child head. Named Affetto, the android’s first-generation model was reported in a 2011 publication. The researchers have now found a system to make the second-generation Affetto more expressive. Their findings offer a path for androids to express greater ranges of emotion, and ultimately have deeper interaction with humans.

The researchers reported their findings in the journal Frontiers in Robotics and AI.

“Surface deformations are a key issue in controlling android faces,” study co-author Minoru Asada explains. “Movements of their soft facial skin create instability, and this is a big hardware problem we grapple with. We sought a better way to measure and control it.”

The researchers investigated 116 different facial points on Affetto to measure its three-dimensional movement. Facial points were underpinned by so-called deformation units. Each unit comprises a set of mechanisms that create a distinctive facial contortion, such as lowering or raising of part of a lip or eyelid. Measurements from these were then subjected to a mathematical model to quantify their surface motion patterns.

While the researchers encountered challenges in balancing the applied force and in adjusting the synthetic skin, they were able to employ their system to adjust the deformation units for precise control of Affetto’s facial surface motions.

“Android robot faces have persisted in being a black box problem: they have been implemented but have only been judged in vague and general terms,” study first author Hisashi Ishihara says. “Our precise findings will let us effectively control android facial movements to introduce more nuanced expressions, such as smiling and frowning.”

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Materials provided by Osaka University. Note: Content may be edited for style and length.

New method peeks inside the ‘black box’ of artificial intelligence

Artificial intelligence — specifically, machine learning — is a part of daily life for computer and smartphone users. From autocorrecting typos to recommending new music, machine learning algorithms can help make life easier. They can also make mistakes.

It can be challenging for computer scientists to figure out what went wrong in such cases. This is because many machine learning algorithms learn from information and make their predictions inside a virtual “black box,” leaving few clues for researchers to follow.

A group of computer scientists at the University of Maryland has developed a promising new approach for interpreting machine learning algorithms. Unlike previous efforts, which typically sought to “break” the algorithms by removing key words from inputs to yield the wrong answer, the UMD group instead reduced the inputs to the bare minimum required to yield the correct answer. On average, the researchers got the correct answer with an input of less than three words.

In some cases, the researchers’ model algorithms provided the correct answer based on a single word. Frequently, the input word or phrase appeared to have little obvious connection to the answer, revealing important insights into how some algorithms react to specific language. Because many algorithms are programmed to give an answer no matter what — even when prompted by a nonsensical input — the results could help computer scientists build more effective algorithms that can recognize their own limitations.

The researchers will present their work on November 4, 2018 at the 2018 Conference on Empirical Methods in Natural Language Processing.

“Black-box models do seem to work better than simpler models, such as decision trees, but even the people who wrote the initial code can’t tell exactly what is happening,” said Jordan Boyd-Graber, the senior author of the study and an associate professor of computer science at UMD. “When these models return incorrect or nonsensical answers, it’s tough to figure out why. So instead, we tried to find the minimal input that would yield the correct result. The average input was about three words, but we could get it down to a single word in some cases.”

In one example, the researchers entered a photo of a sunflower and the text-based question, “What color is the flower?” as inputs into a model algorithm. These inputs yielded the correct answer of “yellow.” After rephrasing the question into several different shorter combinations of words, the researchers found that they could get the same answer with “flower?” as the only text input for the algorithm.

In another, more complex example, the researchers used the prompt, “In 1899, John Jacob Astor IV invested $100,000 for Tesla to further develop and produce a new lighting system. Instead, Tesla used the money to fund his Colorado Springs experiments.”

They then asked the algorithm, “What did Tesla spend Astor’s money on?” and received the correct answer, “Colorado Springs experiments.” Reducing this input to the single word “did” yielded the same correct answer.

The work reveals important insights about the rules that machine learning algorithms apply to problem solving. Many real-world issues with algorithms result when an input that makes sense to humans results in a nonsensical answer. By showing that the opposite is also possible — that nonsensical inputs can also yield correct, sensible answers — Boyd-Graber and his colleagues demonstrate the need for algorithms that can recognize when they answer a nonsensical question with a high degree of confidence.

“The bottom line is that all this fancy machine learning stuff can actually be pretty stupid,” said Boyd-Graber, who also has co-appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) as well as UMD’s College of Information Studies and Language Science Center. “When computer scientists train these models, we typically only show them real questions or real sentences. We don’t show them nonsensical phrases or single words. The models don’t know that they should be confused by these examples.”

Most algorithms will force themselves to provide an answer, even with insufficient or conflicting data, according to Boyd-Graber. This could be at the heart of some of the incorrect or nonsensical outputs generated by machine learning algorithms — in model algorithms used for research, as well as real-world algorithms that help us by flagging spam email or offering alternate driving directions. Understanding more about these errors could help computer scientists find solutions and build more reliable algorithms.

“We show that models can be trained to know that they should be confused,” Boyd-Graber said. “Then they can just come right out and say, ‘You’ve shown me something I can’t understand.'”

In addition to Boyd-Graber, UMD-affiliated researchers involved with this work include undergraduate researcher Eric Wallace; graduate students Shi Feng and Pedro Rodriguez; and former graduate student Mohit Iyyer (M.S. ’14, Ph.D. ’17, computer science).

The research presentation, “Pathologies of Neural Models Make Interpretation Difficult,” Shi Feng, Eric Wallace, Alvin Grissom II, Pedro Rodriguez, Mohit Iyyer, and Jordan Boyd-Graber, will be presented at the 2018 Conference on Empirical Methods in Natural Language Processing on November 4, 2018.

This work was supported by the Defense Advanced Research Projects Agency (Award No. HR0011-15-C-011) and the National Science Foundation (Award No. IIS1652666). The content of this article does not necessarily reflect the views of these organizations.

Machines that learn language more like kids do

Children learn language by observing their environment, listening to the people around them, and connecting the dots between what they see and hear. Among other things, this helps children establish their language’s word order, such as where subjects and verbs fall in a sentence.

In computing, learning language is the task of syntactic and semantic parsers. These systems are trained on sentences annotated by humans that describe the structure and meaning behind words. Parsers are becoming increasingly important for web searches, natural-language database querying, and voice-recognition systems such as Alexa and Siri. Soon, they may also be used for home robotics.

But gathering the annotation data can be time-consuming and difficult for less common languages. Additionally, humans don’t always agree on the annotations, and the annotations themselves may not accurately reflect how people naturally speak.

In a paper being presented at this week’s Empirical Methods in Natural Language Processing conference, MIT researchers describe a parser that learns through observation to more closely mimic a child’s language-acquisition process, which could greatly extend the parser’s capabilities. To learn the structure of language, the parser observes captioned videos, with no other information, and associates the words with recorded objects and actions. Given a new sentence, the parser can then use what it’s learned about the structure of the language to accurately predict a sentence’s meaning, without the video.

This “weakly supervised” approach — meaning it requires limited training data — mimics how children can observe the world around them and learn language, without anyone providing direct context. The approach could expand the types of data and reduce the effort needed for training parsers, according to the researchers. A few directly annotated sentences, for instance, could be combined with many captioned videos, which are easier to come by, to improve performance.

In the future, the parser could be used to improve natural interaction between humans and personal robots. A robot equipped with the parser, for instance, could constantly observe its environment to reinforce its understanding of spoken commands, including when the spoken sentences aren’t fully grammatical or clear. “People talk to each other in partial sentences, run-on thoughts, and jumbled language. You want a robot in your home that will adapt to their particular way of speaking … and still figure out what they mean,” says co-author Andrei Barbu, a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds, and Machines (CBMM) within MIT’s McGovern Institute.

The parser could also help researchers better understand how young children learn language. “A child has access to redundant, complementary information from different modalities, including hearing parents and siblings talk about the world, as well as tactile information and visual information, [which help him or her] to understand the world,” says co-author Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. “It’s an amazing puzzle, to process all this simultaneous sensory input. This work is part of bigger piece to understand how this kind of learning happens in the world.”

Co-authors on the paper are: first author Candace Ross, a graduate student in the Department of Electrical Engineering and Computer Science and CSAIL, and a researcher in CBMM; Yevgeni Berzak PhD ’17, a postdoc in the Computational Psycholinguistics Group in the Department of Brain and Cognitive Sciences; and CSAIL graduate student Battushig Myanganbayar.

Visual learner

For their work, the researchers combined a semantic parser with a computer-vision component trained in object, human, and activity recognition in video. Semantic parsers are generally trained on sentences annotated with code that ascribes meaning to each word and the relationships between the words. Some have been trained on still images or computer simulations.

The new parser is the first to be trained using video, Ross says. In part, videos are more useful in reducing ambiguity. If the parser is unsure about, say, an action or object in a sentence, it can reference the video to clear things up. “There are temporal components — objects interacting with each other and with people — and high-level properties you wouldn’t see in a still image or just in language,” Ross says.

The researchers compiled a dataset of about 400 videos depicting people carrying out a number of actions, including picking up an object or putting it down, and walking toward an object. Participants on the crowdsourcing platform Mechanical Turk then provided 1,200 captions for those videos. They set aside 840 video-caption examples for training and tuning, and used 360 for testing. One advantage of using vision-based parsing is “you don’t need nearly as much data — although if you had [the data], you could scale up to huge datasets,” Barbu says.

In training, the researchers gave the parser the objective of determining whether a sentence accurately describes a given video. They fed the parser a video and matching caption. The parser extracts possible meanings of the caption as logical mathematical expressions. The sentence, “The woman is picking up an apple,” for instance, may be expressed as: ?xy. woman x, pick_up x y, apple y.

Those expressions and the video are inputted to the computer-vision algorithm, called “Sentence Tracker,” developed by Barbu and other researchers. The algorithm looks at each video frame to track how objects and people transform over time, to determine if actions are playing out as described. In this way, it determines if the meaning is possibly true of the video.

Connecting the dots

The expression with the most closely matching representations for objects, humans, and actions becomes the most likely meaning of the caption. The expression, initially, may refer to many different objects and actions in the video, but the set of possible meanings serves as a training signal that helps the parser continuously winnow down possibilities. “By assuming that all of the sentences must follow the same rules, that they all come from the same language, and seeing many captioned videos, you can narrow down the meanings further,” Barbu says.

In short, the parser learns through passive observation: To determine if a caption is true of a video, the parser by necessity must identify the highest probability meaning of the caption. “The only way to figure out if the sentence is true of a video [is] to go through this intermediate step of, ‘What does the sentence mean?’ Otherwise, you have no idea how to connect the two,” Barbu explains. “We don’t give the system the meaning for the sentence. We say, ‘There’s a sentence and a video. The sentence has to be true of the video. Figure out some intermediate representation that makes it true of the video.'”

The training produces a syntactic and semantic grammar for the words it’s learned. Given a new sentence, the parser no longer requires videos, but leverages its grammar and lexicon to determine sentence structure and meaning.

Ultimately, this process is learning “as if you’re a kid,” Barbu says. “You see world around you and hear people speaking to learn meaning. One day, I can give you a sentence and ask what it means and, even without a visual, you know the meaning.”

In future work, the researchers are interested in modeling interactions, not just passive observations. “Children interact with the environment as they’re learning. Our idea is to have a model that would also use perception to learn,” Ross says.

This work was supported, in part, by the CBMM, the National Science Foundation, a Ford Foundation Graduate Research Fellowship, the Toyota Research Institute, and the MIT-IBM Brain-Inspired Multimedia Comprehension project.

Humans help robots learn tasks

In the basement of the Gates Computer Science Building at Stanford University, a screen attached to a red robotic arm lights up. A pair of cartoon eyes blinks. “Meet Bender,” says Ajay Mandlekar, PhD student in electrical engineering.

Bender is one of the robot arms that a team of Stanford researchers is using to test two frameworks that, together, could make it faster and easier to teach robots basic skills. The RoboTurk framework allows people to direct the robot arms in real time with a smartphone and a browser by showing the robot how to carry out tasks like picking up objects. SURREAL speeds the learning process by running multiple experiences at once, essentially allowing the robots to learn from many experiences simultaneously.

“With RoboTurk and SURREAL, we can push the boundary of what robots can do by combining lots of data collected by humans and coupling that with large-scale reinforcement learning,” said Mandlekar, a member of the team that developed the frameworks.

The group will be presenting RoboTurk and SURREAL Oct. 29 at the conference on robot learning in Zurich, Switzerland.

Humans teaching robots

Yuke Zhu, a PhD student in computer science and a member of the team, showed how the system works by opening the app on his iPhone and waving it through the air. He guided the robot arm — like a mechanical crane in an arcade game — to hover over his prize: a wooden block painted to look like a steak. This is a simple pick-and-place task that involves identifying objects, picking them up and putting them into the bin with the correct label.

To humans, the task seems ridiculously easy. But for the robots of today, it’s quite difficult. Robots typically learn by interacting with and exploring their environment — which usually results in lots of random arm waving — or from large datasets. Neither of these is as efficient as getting some human help. In the same way that parents teach their children to brush their teeth by guiding their hands, people can demonstrate to robots how to do specific tasks.

However, those lessons aren’t always perfect. When Zhu pressed hard on his phone screen and the robot released its grip, the wooden steak hit the edge of the bin and clattered onto the table. “Humans are by no means optimal at this,” Mandlekar said, “but this experience is still integral for the robots.”

Faster learning in parallel

These trials — even the failures — provide invaluable information. The demonstrations collected through RoboTurk will give the robots background knowledge to kickstart their learning. SURREAL can run thousands of simulated experiences by people worldwide at once to speed the learning process.

“With SURREAL, we want to accelerate this process of interacting with the environment,” said Linxi Fan, a PhD student in computer science and a member of the team. These frameworks drastically increase the amount of data for the robots to learn from.

“The twin frameworks combined can provide a mechanism for AI-assisted human performance of tasks where we can bring humans away from dangerous environments while still retaining a similar level of task execution proficiency,” said postdoctoral fellow Animesh Garg, a member of the team that developed the frameworks.

The team envisions that robots will be an integral part of everyday life in the future: helping with household chores, performing repetitive assembly tasks in manufacturing or completing dangerous tasks that may pose a threat to humans.

“You shouldn’t have to tell the robot to twist its arm 20 degrees and inch forward 10 centimeters,” said Zhu. “You want to be able to tell the robot to go to the kitchen and get an apple.”

Current members of the RoboTurk and SURREAL team include Ajay Mandlekar, Yuke Zhu, Linxi Fan, Animesh Garg and faculty members Fei-Fei Li and Silvio Savarese.

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Shielded quantum bits

The researchers have found ways to shield electric and magnetic noise for a short time. This will make it possible to use spins as memory for quantum computers, as the coherence time is extended and many thousand computer operations can be performed during this interval. The study was published in the current issue of the journal Physical Review Letters.

The technological vision of building a quantum computer does not only depend on computer and information science. New insights in theoretical physics, too, are decisive for progress in the practical implementation. Every computer or communication device contains information embedded in physical systems. “In the case of a quantum computer, we use spin qubits, for example, to realize information processing,” explains Professor Guido Burkard, who carries out his research in cooperation with colleagues from Princeton University. The theoretical findings that led to the current publication were largely made by the lead author of the study, doctoral researcher Maximilian Russ from the University of Konstanz.

In the quest for the quantum computer, spin qubits and their magnetic properties are the centre of attention. To use spins as memory in quantum technology, they must be lined up, because otherwise they cannot be controlled specifically. “Usually magnets are controlled by magnetic fields — like a compass needle in the Earth’s magnetic field’, explains Guido Burkard. “In our case the particles are extremely small and the magnets very weak, which makes it really difficult to control them.” The physicists meet this challenge with electric fields and a procedure in which several electrons, in this case four, form a quantum bit. Another problem they have to face is the electron spins, which are rather sensitive and fragile. Even in solid bodies of silicon they react to external interferences with electric or magnetic noise. The current study focuses on theoretical models and calculations of how the quantum bits can be shielded from this noise — an important contribution to basic research for a quantum computer: If this noise can be shielded for even the briefest of times, thousands of computer operations can be carried out in these fractions of a second — at least theoretically.

The next step for the physicists from Konstanz will now be to work with their experimental colleagues towards testing their theory in experiments. For the first time, four instead of three electrons will be used in these experiments, which could, e.g., be implemented by the research partners in Princeton. While the Konstanz-based physicists provide the theoretical basis, the collaboration partners in the US perform the experimental part. This research is not the only reason why Konstanz is now on the map for qubit research. This autumn, for example, Konstanz attracted the internationally leading scientific community in this field for the “4th School and Conference on Based Quantum Information Processing.”

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A "GPS for inside your body"

Investigating inside the human body often requires cutting open a patient or swalloing long tubes with built-in cameras. But what if physicians could get a better glimpse in a less expensive, invasive, and time-consuming manner?

A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) led by Professor Dina Katabi is working on doing exactly that with an “in-body GPS” system dubbed ReMix. The new method can pinpoint the location of ingestible implants inside the body using low-power wireless signals. These implants could be used as tiny tracking devices on shifting tumors to help monitor their slight movements.

In animal tests, the team demonstrated that they can track the implants with centimeter-level accuracy. The team says that, one day, similar implants could be used to deliver drugs to specific regions in the body.

ReMix was developed in collaboration with researchers from Massachusetts General Hospital (MGH). The team describes the system in a paper that’s being presented at this week’s Association for Computing Machinery’s Special Interest Group on Data Communications (SIGCOMM) conference in Budapest, Turkey.

Tracking inside the body

To test ReMix, Katabi’s group first implanted a small marker in animal tissues. To track its movement, the researchers used a wireless device that reflects radio signals off the patient. This was based on a wireless technology that the researchers previously demonstrated to detect heart rate, breathing, and movement. A special algorithm then uses that signal to pinpoint the exact location of the marker.

Interestingly, the marker inside the body does not need to transmit any wireless signal. It simply reflects the signal transmitted by the wireless device outside the body. Therefore, it doesn’t need a battery or any other external source of energy.

A key challenge in using wireless signals in this way is the many competing reflections that bounce off a person’s body. In fact, the signals that reflect off a person’s skin are actually 100 million times more powerful than the signals of the metal marker itself.

To overcome this, the team designed an approach that essentially separates the interfering skin signals from the ones they’re trying to measure. They did this using a small semiconductor device, called a “diode,” that mixes signals together so the team can then filter out the skin-related signals. For example, if the skin reflects at frequencies of F1 and F2, the diode creates new combinations of those frequencies, such as F1-F2 and F1+F2. When all of the signals reflect back to the system, the system only picks up the combined frequencies, filtering out the original frequencies that came from the patient’s skin.

One potential application for ReMix is in proton therapy, a type of cancer treatment that involves bombarding tumors with beams of magnet-controlled protons. The approach allows doctors to prescribe higher doses of radiation, but requires a very high degree of precision, which means that it’s usually limited to only certain cancers.

Its success hinges on something that’s actually quite unreliable: a tumor staying exactly where it is during the radiation process. If a tumor moves, then healthy areas could be exposed to the radiation. But with a small marker like ReMix’s, doctors could better determine the location of a tumor in real-time and either pause the treatment or steer the beam into the right position. (To be clear, ReMix is not yet accurate enough to be used in clinical settings. Katabi says a margin of error closer to a couple of millimeters would be necessary for actual implementation.)

“The ability to continuously sense inside the human body has largely been a distant dream,” says Romit Roy Choudhury, a professor of electrical engineering and computer science at the University of Illinois, who was not involved in the research. “One of the roadblocks has been wireless communication to a device and its continuous localization. ReMix makes a leap in this direction by showing that the wireless component of implantable devices may no longer be the bottleneck.”

Looking ahead

There are still many ongoing challenges for improving ReMix. The team next hopes to combine the wireless data with medical data, such as that from magnetic resonance imaging (MRI) scans, to further improve the system’s accuracy. In addition, the team will continue to reassess the algorithm and the various tradeoffs needed to account for the complexity of different bodies.

“We want a model that’s technically feasible, while still complex enough to accurately represent the human body,” says MIT PhD student Deepak Vasisht, lead author on the new paper. “If we want to use this technology on actual cancer patients one day, it will have to come from better modeling a person’s physical structure.”

The researchers say that such systems could help enable more widespread adoption of proton therapy centers. Today, there are only about 100 centers globally.

“One reason that [proton therapy] is so expensive is because of the cost of installing the hardware,” Vasisht says. “If these systems can encourage more applications of the technology, there will be more demand, which will mean more therapy centers, and lower prices for patients.”

Katabi and Vasisht co-wrote the paper with MIT PhD student Guo Zhang, University of Waterloo professor Omid Abari, MGH physicist Hsaio-Ming Lu, and MGH technical director Jacob Flanz.


Robots as tools and partners in rehabilitation

In future decades the need for effective strategies for medical rehabilitation will increase significantly, because patients’ rate of survival after diseases with severe functional deficits, such as a stroke, will increase. Socially assistive robots (SARs) are already being used in rehabilitation for this reason. In the journal Science Robotics, a research team led by neuroscientist Dr. Philipp Kellmeyer of the Freiburg University Medical Center and Prof. Dr. Oliver Müller from the Department of Philosophy of the University of Freiburg, analyzes the improvements necessary to make SARs valuable and trustworthy assistants for medical therapies.

The researchers conclude that the development of SARs not only requires technical improvements, but primarily social, trust-building measures. Rehabilitation patients in particular are dependent on a reliable relationship with their therapists. So there must be trust in the safety of the robotic system, especially regarding the predictability of the machines’ behavior. Given the ever-growing intelligence of the robots and with it their independence, this is highly important.

In addition, robots and patients can only interact well, the scientists explain, when they have shared goals that they pursue through the therapy. To achieve this, aspects of philosophical and developmental psychology must also be taken into account in the development of SARs: the ability of robots to recognize the aims and motives of a patient is a critical requirement if cooperation is to be successful. So there must also be trust for the participants to adapt to one another. The frustration felt by patients, for instance as a result of physical or linguistic limitations, would be avoided if the robots were adapted to the specific needs and vulnerabilities of the patient in question.

Philipp Kellmeyer and Oliver Müller are members of the Cluster of Excellence BrainLinks-BrainTools of the University of Freiburg. The study also involved Prof. Dr. Shelly Levy-Tzedek and Ronit Feingold-Polak from the Ben Gurion University of the Negev, Israel. In the 2018/19 academic year, the Freiburg researchers together with the legal academic Prof. Dr. Silja Vöneky and the IT specialist Prof. Dr. Wolfram Burgard, both from the University of Freiburg, are developing a Research Focus into normative aspects of interaction between people and autonomous intelligent systems at the Freiburg Institute for Advanced Studies (FRIAS).

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More workers working might not get more work done, ants (and robots) show

For ants and robots operating in confined spaces like tunnels, having more workers does not necessarily mean getting more work done. Just as too many cooks in a kitchen get in each other’s way, having too many robots in tunnels creates clogs that can bring the work to a grinding halt.

A study published August 17 in the journal Science shows that in fire ant colonies, a small number of workers does most of digging, leaving the other ants to look somewhat less than industrious. For digging nest tunnels, this less busy approach gets the job done without ant traffic jams — ensuring smooth excavation flow. Researchers found that applying the ant optimization strategy to autonomous robots avoids mechanized clogs and gets the work done with the least amount of energy.

Optimizing the activity of autonomous underground robots could be useful for tasks such as disaster recovery, mining or even digging underground shelters for future planetary explorers. The research was supported by the National Science Foundation’s Physics of Living Systems program, the Army Research Office and the Dunn Family Professorship.

“We noticed that if you have 150 ants in a container, only 10 or 15 of them will actually be digging in the tunnels at any given time,” said Daniel Goldman, a professor in the School of Physics at the Georgia Institute of Technology. “We wanted to know why, and to understand how basic laws of physics might be at work. We found a functional, community benefit to this seeming inequality in the work environment. Without it, digging just doesn’t get done.”

By monitoring the activities of 30 ants that had been painted to identify each individual, Goldman and colleagues, including former postdoctoral fellow Daria Monaenkova and Ph.D. student Bahnisikha Dutta, discovered that just 30 percent of the ants were doing 70 percent of the work — an inequality that seems to keep the work humming right along. However, that is apparently not because the busiest ants are the most qualified. When the researchers removed the five hardest working ants from the nest container, they saw no productivity decline as the remaining 25 continued to dig.

Having a nest is essential to fire ants, and if a colony is displaced — by a flood, for instance — the first thing the ants will do upon reaching dry land is start digging. Their tunnels are narrow, barely wide enough for two ants to pass, a design feature hypothesized to give locomotion advantages in the developing vertical tunnels. Still, the ants know how to avoid creating clogs by retreating from tunnels already occupied by other workers — and sometimes by not doing anything much at all.

To avoid clogs and maximize digging in the absence of a leader, robots built by Goldman’s master’s degree student Vadim Linevich were programmed to capture aspects of the dawdling and retreating ants. The researchers found that as many as three robots could work effectively in a narrow horizontal tunnel digging 3D printed magnetic plastic balls that simulated sticky soil. If a fourth robot entered the tunnel, however, that produced a clog that stopped the work entirely.

“When we put four robots into a confined environment and tried to get them to dig, they immediately jammed up,” said Goldman, who is the Dunn Family Professor in the School of Physics. “While observing the ants, we were surprised to see that individuals would sometimes go to the tunnel and if they encountered even a small amount of clog, they’d just turn around and retreat. When we put those rules into combinations with the robots, that created a good strategy for digging rapidly with low amounts of energy use per robot.”

Experimentally, the research team tested three potential behaviors for the robots, which they termed “eager,” “reversal” or “lazy.” Using the eager strategy, all four robots plunged into the work — and quickly jammed up. In the reversal behavior, robots gave up and turned around when they encountered delays reaching the work site. In the lazy strategy, dawdling was encouraged.

“Eager is the best strategy if you only have three robots, but if you add a fourth, that behavior tanks because they get in each other’s way,” said Goldman. “Reversal produces relatively sane and sensible digging. It is not the fastest strategy, but there are no jams. If you look at energy consumed, lazy is the best course.” Analysis techniques based on glassy and supercooled fluids, led by former Ph.D. student Jeffrey Aguilar, gave insight into how the different strategies mitigated and prevented clog-forming clusters.

To understand what was going on and experiment with the parameters, Goldman and colleagues — including Will Savoie, a Georgia Tech Ph.D. student, Research Assistant Hui-Shun Kuan and Professor Meredith Betterton from the School of Physics at University of Colorado at Boulder — used computer modeling known as cellular automata that has similarities to the way in which traffic engineers model the movement of cars and trucks on a highway.

“On highways, too few cars don’t provide much flow, while too many cars create a jam,” Goldman said. “There is an intermediate level where things are best, and that is called the fundamental diagram. From our modeling, we learned that the ants are working right at the peak of the diagram. The right mix of unequal work distributions and reversal behaviors has the benefit of keeping them moving at maximum efficiency without jamming.”

The researchers used robots designed and built for the research, but they were no match for the capabilities of the ants. The ants are flexible and robust, able to squeeze past each other in confines that would cause the inflexible robots to jam. In some cases, the robots in Goldman’s lab even damaged each other while jostling into position for digging.

The research findings could be useful for space exploration where tunnels might be needed to quickly shield humans from approaching dust storms or other threats. “If you were a robot swarm on Mars and needed to dig deeply in a hurry to get away from dust storms, this strategy might help provide shelter without having perfect information about what everybody was doing,” Goldman explained.

Beyond the potential robotics applications, the work provides insights into the complex social skills of ants and adds to the understanding of active matter.

“Ants that live in complex subterranean environments have to develop sophisticated social rules to avoid the bad things that can happen when you have a lot of individuals in a crowded environment,” Goldman said. “We are also contributing to understanding the physics of task-oriented active matter, putting more experimental knowledge into phenomenon such as swarms.”

Artificial Intelligence Weekly – Artificial Intelligence News #83 – Aug 16th 2018

Face Off: Law enforcement use of face recognition technology

“Face recognition is poised to become one of the most pervasive surveillance technologies, and law enforcement’s use of it is increasing rapidly. Today, law enforcement officers can use mobile devices to capture face recognition-ready photographs of people they stop on the street; surveillance cameras boast real-time face scanning and identification capabilities; and federal, state, and local law enforcement agencies have access to hundreds of millions of images of faces of law-abiding Americans. On the horizon, law enforcement would like to use face recognition with body-worn cameras, to identify people in the dark, to match a person to a police sketch, or even to construct an image of a person’s face from a small sample of their DNA.”

Robots have power to significantly influence children’s opinions

Young children are significantly more likely than adults to have their opinions and decisions influenced by robots, according to new research.

The study, conducted at the University of Plymouth, compared how adults and children respond to an identical task when in the presence of both their peers and humanoid robots.

It showed that while adults regularly have their opinions influenced by peers, something also demonstrated in previous studies, they are largely able to resist being persuaded by robots.

However, children aged between seven and nine were more likely to give the same responses as the robots, even if they were obviously incorrect.

The study used the Asch paradigm, first developed in the 1950s, which asks people to look at a screen showing four lines and say which two match in length. When alone, people almost never make a mistake but when doing the experiment with others, they tend to follow what others are saying.

When children were alone in the room in this research, they scored 87% on the test, but when the robots join in their score drops to 75%. And of the wrong answers, 74% matched those of the robot.

Writing in Science Robotics, scientists say the study provides an interesting insight into how robots could be used positively within society. However, they also say it does raise some concerns around the potential for robots to have a negative influence on vulnerable young children.

The research was led by former Plymouth researcher Anna Vollmer, now a Postdoctoral Researcher at the University of Bielefeld, and Professor in Robotics Tony Belpaeme, from the University of Plymouth and Ghent University.

Professor Belpaeme said: “People often follow the opinions of others and we’ve known for a long time that it is hard to resist taking over views and opinions of people around us. We know this as conformity. But as robots will soon be found in the home and the workplace, we were wondering if people would conform to robots.

“What our results show is that adults do not conform to what the robots are saying. But when we did the experiment with children, they did. It shows children can perhaps have more of an affinity with robots than adults, which does pose the question: what if robots were to suggest, for example, what products to buy or what to think?”

Researchers in Plymouth have worked extensively to explore the positive impact robots can have in health and education settings.

They led the four-year ALIZ-E programme, which showed that social robots can help diabetic children accept the nature of their condition, and are leading L2TOR, which aims to design a robot that can be used to support preschool children learning a second language.

In their conclusion to the current study, the researchers add: “A future in which autonomous social robots are used as aids for education professionals or child therapists is not distant. In these applications, the robot is in a position in which the information provided can significantly affect the individuals they interact with. A discussion is required about whether protective measures, such as a regulatory framework, should be in place that minimise the risk to children during social child-robot interaction and what form they might take so as not to adversely affect the promising development of the field.”

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Robots will never replace teachers but can boost children’s education

Robots can play an important role in the education of young people but will never fully replace teachers, a new study suggests.

Writing in Science Robotics, scientists say social robots are proving effective in the teaching of certain narrow subjects, such as vocabulary or prime numbers.

But current technical limitations — particularly around speech recognition and the ability for social interaction — mean their role will largely be confined to that of teaching assistants or tutors, at least for the foreseeable future.

The study was led by Professor in Robotics Tony Belpaeme, from the University of Plymouth and Ghent University, who has worked in the field of social robotics for around two decades.

He said: “In recent years scientists have started to build robots for the classroom — not the robot kits used to learn about technology and mathematics, but social robots that can actually teach. This is because pressures on teaching budgets, and calls for more personalised teaching, have led to a search for technological solutions.

“In the broadest sense, social robots have the potential to become part of the educational infrastructure just like paper, white boards, and computer tablets. But a social robot has the potential to support and challenge students in ways unavailable in current resource-limited educational environments. Robots can free up precious time for teachers, allowing the teacher to focus on what people still do best — provide a comprehensive, empathic, and rewarding educational experience.”

The current study, compiled in conjunction with academics at Yale University and the University of Tsukuba, involved a review of more than 100 published articles, which have shown robots to be effective at increasing outcomes, largely because of their physical presence.

However it also explored in detail some of the technical constraints highlighting that speech recognition, for example, is still insufficiently robust to allow the robot to understand spoken utterances from young children.

It also says that introducing social robots into the school curriculum would pose significant logistical challenges and might in fact carry risks, with some children being seen to rely too heavily on the help offered by robots rather than simply using them when they are in difficulty.

In their conclusion, the authors add: “Next to the practical considerations of introducing robots in education, there are also ethical issues. How far do we want the education of our children to be delegated to machines? Overall, learners are positive about their experiences, but parents and teaching staff adopt a more cautious attitude.

“Notwithstanding that, robots show great promise when teaching restricted topics with the effects almost matching those of human tutoring. So although the use of robots in educational settings is limited by technical and logistical challenges for now, it is highly likely that classrooms of the future will feature robots that assist a human teacher.”

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AI Analysis Uncovers Coral Reefs Resistant to Climate Change

UNDERWATER WASTELAND. Global warming is destroying Earth’s coral reefs — the colorful underwater ecosystems simply can’t survive as the ocean warms and acidifies. However, researchers have now discovered a type of coral off the coast of Indonesia’s Sulawesi Island that seems to be resistant to global warming. The discovery could help us ensure at least some of the world’s coral reefs survive climate change.

As part of 50 Reefs, an initiative designed to identify climate change-resistant corals, researchers spent six weeks in June and July using underwater scooters equipped with 360-degree cameras to take more than 56,000 images of shallow water reefs. In total, they snapped images of 3,851 square kilometers (1,487 square miles) worth of reefs.

Next, they needed to analyze all those images, and for that, they turned to artificial intelligence (AI) .

ANALYTICAL AI. First, the researchers used about 400 to 600 images to train AI to identify different coral and invertebrate types within the image. After that, the system was able to identify and catalog the reef imagery on its own.

“The use of AI to rapidly analyze photographs of coral has vastly improved the efficiency of what we do — what would take a coral reef scientist 10 to 15 minutes now takes the machine a few seconds,” lead researcher Emma Kennedy told The Guardian.

From the AI’s analysis, the team determined that the Sulawesi reefs were actually in better shape in 2018 than when they were originally surveyed in 2014.

“After several depressing years as a coral reef scientist, witnessing the worst-ever global coral bleaching event, it is unbelievably encouraging to experience reefs such as these,” said Kennedy. “It means we still have time to save some coral reefs through the science-based targeting of conservation action.”

A NEED FOR CORAL. If we don’t decrease our carbon dioxide emissions, scientists believe the world’s coral reef ecosystems could fully collapse as soon as 2050. Not only would this be catastrophic for ocean biodiversity — reefs shelter about one-fourth of marine species — it would also have a major impact on humanity.

Reefs protect our shorelines, and they could host future medical breakthroughs. They are also integral to tourism and fishing industries, which provides food and jobs for millions of people.

Now that researchers know certain reefs have a better chance than others of surviving global warming, they can hunt down those reefs throughout the world. After that, they could enact measures to ensure other threats — such as overfishing or pollution — don’t take out what may end up being the only reefs to make it past the 2050 milestone.

READ MORE: AI Identifies Heat-Resistant Coral Reefs in Indonesia [The Guardian]

More on coral bleaching: Coral Is Dying Globally. But We Can Save Some Reefs From Total Destruction.

AI Can Make Sure Cancer Patients Get Just Enough (but Not Too Much) Treatment

QUALITY OF LIFE. Patients with glioblastoma, a malignant tumor in the brain or spinal cord, typically live no more than five years after receiving their diagnosis. And those five years can be painful — in an effort to minimize the tumor, doctors often prescribe a combination of radiation therapy and drugs that can cause debilitating side effects for patients.

Now, researchers from MIT Media Lab have developed artificial intelligence (AI) that can determine the minimum drug doses needed to effectively shrink glioblastoma patients’ tumors. They plan to present their research at Stanford University’s 2018 Machine Learning for Healthcare conference.

CARROT AND STICK. To create an AI that could determine the best dosing regimen for glioblastoma patients, the MIT researchers turned to a training technique known as reinforcement learning (RL).

First, they created a testing group of 50 simulated glioblastoma patients based on a large dataset of those that had previously undergone treatment for their disease. Then they asked their AI to recommend doses of several drugs typically used to treat glioblastoma [oftemozolomide (TMZ) and a combination of procarbazine, lomustine, and vincristine (PVC)] for each patient at regular intervals (either weeks or months).

After the AI prescribed a dose, it would check a computer model capable of predicting how likely a dose is to shrink a tumor. When the AI prescribed a tumor-shrinking dosage, it received a reward. However, if the AI simply prescribed the maximum dose all the time, it received a penalty.

According to the researchers, this need to strike a balance between a goal  and the consequences of an action — in this case, tumor reduction and patient quality of life respectively — is unique in the field of RL. Other RL models simply work toward a goal; for example, DeepMind’s AlphaZero simply has to focus on winning a game.

“If all we want to do is reduce the mean tumor diameter, and let it take whatever actions it wants, it will administer drugs irresponsibly,” principal investigator Pratik Shah told MIT News. “Instead, we said, ‘We need to reduce the harmful actions it takes to get to that outcome.’”

GETTING PERSONAL. The AI conducted about 20,000 test runs for each simulated patient to complete its training. Next, the researchers tested the AI on a group of 50 new simulated patients and found it could decrease both the doses and their frequency while still reducing tumor size. It could also take into account information specific to each patient, such as their tumor size, medical history, and biomarkers.

“We said [to the model], ‘Do you have to administer the same dose for all the patients? And it said, ‘No. I can give a quarter dose to this person, half to this person, and maybe we skip a dose for this person,’” said Shah. “That was the most exciting part of this work, where we are able to generate precision medicine-based treatments by conducting one-person trials using unorthodox machine-learning architectures.”

The AI will still need to undergo further testing and vetting by the Food and Drug Administration (FDA) before doctors could put it into practice. But if it passes those tests, it could eventually help people with glioblastoma attack their brain tumors without causing them more pain in the process.

READ MORE: Artificial Intelligence Model “Learns” From Patient Data to Make Cancer Treatment Less Toxic [MIT News]

More on AI healthcare: In Just 4 Hours, Google’s AI Mastered All the Chess Knowledge in History

Toward a universal quantum computer

Researchers have demonstrated holonomic quantum gates under zero-magnetic field at room temperature, which will enable the realization of fast and fault-tolerant universal quantum computers.

A quantum computer is a powerful machine with the potential to solve complex problems much faster than today’s conventional computer can. Researchers are currently working on the next step in quantum computing: building a universal quantum computer.

The paper, published in the journal Nature Communications, reports experimental demonstration of non-adiabatic and non-abelian holonomic quantum gates over a geometric spin qubit on an electron or nitrogen nucleus, which paves the way to realizing a universal quantum computer.

The geometric phase is currently a key issue in quantum physics. A holonomic quantum gate manipulating purely the geometric phase in the degenerate ground state system is believed to be an ideal way to build a fault-tolerant universal quantum computer. The geometric phase gate or holonomic quantum gate has been experimentally demonstrated in several quantum systems including nitrogen-vacancy (NV) centers in diamond. However, previous experiments required microwaves or light waves to manipulate the non-degenerate subspace, leading to the degradation of gate fidelity due to unwanted interference of the dynamic phase.

“To avoid unwanted interference, we used a degenerate subspace of the triplet spin qutrit to form an ideal logical qubit, which we call a geometric spin qubit, in an NV center. This method facilitated fast and precise geometric gates at a temperature below 10 K, and the gate fidelity was limited by radiative relaxation,” says the corresponding author Hideo Kosaka, Professor, Yokohama National University. “Based on this method, in combination with polarized microwaves, we succeeded in manipulation of the geometric phase in an NV center in diamond under a zero-magnetic field at room temperature.”

The group also demonstrated a two-qubit holonomic gate to show universality by manipulating the electron-nucleus entanglement. The scheme renders a purely holonomic gate without requiring an energy gap, which would have induced dynamic phase interference to degrade the gate fidelity, and thus enables precise and fast control over long-lived quantum memories, for realizing quantum repeaters interfacing between universal quantum computers and secure communication networks.

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Machine learning technique reconstructs images passing through a multimode fiber

Through innovative use of a neural network that mimics image processing by the human brain, a research team reports accurate reconstruction of images transmitted over optical fibers for distances of up to a kilometer.

In The Optical Society’s journal for high-impact research, Optica, the researchers report teaching a type of machine learning algorithm known as a deep neural network to recognize images of numbers from the pattern of speckles they create when transmitted to the far end of a fiber. The work could improve endoscopic imaging for medical diagnosis, boost the amount of information carried over fiber-optic telecommunication networks, or increase the optical power delivered by fibers.

“We use modern deep neural network architectures to retrieve the input images from the scrambled output of the fiber,” said Demetri Psaltis, Swiss Federal Institute of Technology, Lausanne, who led the research in collaboration with colleague Christophe Moser. “We demonstrate that this is possible for fibers up to 1 kilometer long” he added, calling the work “an important milestone.”

Deciphering the blur

Optical fibers transmit information with light. Multimode fibers have much greater information-carrying capacity than single-mode fibers. Their many channels — known as spatial modes because they have different spatial shapes — can transmit different streams of information simultaneously.

While multimode fibers are well suited for carrying light-based signals, transmitting images is problematic. Light from the image travels through all of the channels and what comes out the other end is a pattern of speckles that the human eye cannot decode.

To tackle this problem, Psaltis and his team turned to a deep neural network, a type of machine learning algorithm that functions much the way the brain does. Deep neural networks can give computers the ability to identify objects in photographs and help improve speech recognition systems. Input is processed through several layers of artificial neurons, each of which performs a small calculation and passes the result on to the next layer. The machine learns to identify the input by recognizing the patterns of output associated with it.

“If we think about the origin of neural networks, which is our very own brain, the process is simple,” explains Eirini Kakkava, a doctoral student working on the project. “When a person stares at an object, neurons in the brain are activated, indicating recognition of a familiar object. Our brain can do this because it gets trained throughout our life with images or signals of the same category of objects, which changes the strength of the connections between the neurons.” To train an artificial neural network, researchers follow essentially the same process, teaching the network to recognize certain images (in this case, handwritten digits) until it is able to recognize images in the same category as the training images that it has not seen before.

Learning by the numbers

To train their system, the researchers turned to a database containing 20,000 samples of handwritten numbers, 0 through 9. They selected 16,000 to be used as training data, and kept aside 2,000 to validate the training and another 2,000 for testing the validated system. They used a laser to illuminate each digit and sent the light beam through an optical fiber, which had approximately 4,500 channels, to a camera on the far end. A computer measured how the intensity of the output light varied across the captured image, and they collected a series of examples for each digit.

Although the speckle patterns collected for each digit looked the same to the human eye, the neural network was able to discern differences and recognize patterns of intensity associated with each digit. Testing with the set-aside images showed that the algorithm achieved 97.6 percent accuracy for images transmitted through a 0.1 meter long fiber and 90 percent accuracy with a 1 kilometer length of fiber.

A simpler method

Navid Borhani, a research-team member, says this machine learning approach is much simpler than other methods to reconstruct images passed through optical fibers, which require making a holographic measurement of the output. The neural network was also able to cope with distortions caused by environmental disturbances to the fiber such as temperature fluctuations or movements caused by air currents that can add noise to the image — a situation that gets worse with fiber length.

“The remarkable ability of deep neural networks to retrieve information transmitted through multimode fibers is expected to benefit medical procedures like endoscopy and communications applications,” Psaltis said. Telecommunication signals often have to travel through many kilometers of fiber and can suffer distortions, which this method could correct. Doctors could use ultrathin fiber probes to collect images of the tracts and arteries inside the human body without needing complex holographic recorders or worrying about movement. “Slight movements because of breathing or circulation can distort the images transmitted through a multimode fiber,” Psaltis said. The deep neural networks are a promising solution for dealing with that noise.

Psaltis and his team plan to try the technique with biological samples, to see if that works as well as reading handwritten numbers. They hope to conduct a series of studies using different categories of images to explore the possibilities and limits of their technique.

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Materials provided by The Optical Society. Note: Content may be edited for style and length.

AI Is Shining a Spotlight on Women Scientists That Were Previously Overlooked

FEMINIST AI. Yes, artificial intelligence (AI) cab perpetuate bias. But it turns out it can also help us overcome it.

On Friday, John Bohannon, director of science for AI startup Primer, published a blog post about Quicksilver, an AI tool that is helping improve the way Wikipedia covers overlooked scientists, many of whom are women.

TRAINING THE TECH. The Primer team started by feeding Quicksilver a whole lot of information — specifically, 30,000 scientists with Wikipedia entries. This information included the Wikipedia articles themselves, the scientists’ Wikidata entries, and a total of more than 3 million sentences from news coverage describing the scientists and their work.

Next, the team fed Quicksilver the names and affiliations of 200,000 people who’d written scientific papers. Within a day, the system determined that 40,000 of those authors didn’t have Wikipedia entries, even though they had been covered in the news just as much as scientists with entries. It also found valuable information that was missing from the entries that already exist.

Identifying overlooked scientists wasn’t all Quicksilver could do. It could also automatically draft Wikipedia-style entries on those scientists using all the reference information at its fingertips (so to speak). The company published 100 of these entries online in the hopes a human would pick up where Quicksilver left off by actually adding the entries to Wikipedia.

EQUAL REPRESENTATION. Eighty-two percent of the biographies on Wikipedia are about men, but Quicksilver could change that.

“Wikipedia is incredibly biased and the underrepresentation of women in science is particularly bad,” Jessica Wade, a physicist who has personally written Wikipedia entries for nearly 300 women scientists over the past year, told WIRED. “With Quicksilver, you don’t have to trawl around to find missing names, and you get a huge amount of well-sourced information very quickly.”

The Primer team has already lent Quicksilver to three English Wikipedia edit-a-thons specifically focused on improving the site’s coverage of women scientists. Between the AI’s efforts and those of Wade (and people like her), we’re closer than ever to closing Wikipedia’s science biography gender gap. And maybe that will push us towards closing the gender gap in science altogether.

READ MORE: AI Spots 40,000 Prominent Scientists Overlooked by Wikipedia [The Verge]

More on AI bias: Microsoft Announces Tool to Catch Biased AI Because We Keep Making Biased AI

How a computer learns to dribble: Practice, practice, practice

Basketball players need lots of practice before they master the dribble, and it turns out that’s true for computer-animated players as well. By using deep reinforcement learning, players in video basketball games can glean insights from motion capture data to sharpen their dribbling skills.

Researchers at Carnegie Mellon University and DeepMotion Inc., a California company that develops smart avatars, have for the first time developed a physics-based, real-time method for controlling animated characters that can learn dribbling skills from experience. In this case, the system learns from motion capture of the movements performed by people dribbling basketballs.

This trial-and-error learning process is time consuming, requiring millions of trials, but the results are arm movements that are closely coordinated with physically plausible ball movement. Players learn to dribble between their legs, dribble behind their backs and do crossover moves, as well as how to transition from one skill to another.

“Once the skills are learned, new motions can be simulated much faster than real-time,” said Jessica Hodgins, Carnegie Mellon professor of computer science and robotics.

Hodgins and Libin Liu, chief scientist at DeepMotion, will present the method at SIGGRAPH 2018, the Conference on Computer Graphics and Interactive Techniques, Aug. 12-18, in Vancouver.

“This research opens the door to simulating sports with skilled virtual avatars,” said Liu, the report’s first author. “The technology can be applied beyond sport simulation to create more interactive characters for gaming, animation, motion analysis, and in the future, robotics.”

Motion capture data already add realism to state-of-the-art video games. But these games also include disconcerting artifacts, Liu noted, such as balls that follow impossible trajectories or that seem to stick to a player’s hand.

A physics-based method has the potential to create more realistic games, but getting the subtle details right is difficult. That’s especially so for dribbling a basketball because player contact with the ball is brief and finger position is critical. Some details, such as the way a ball may continue spinning briefly when it makes light contact with the player’s hands, are tough to reproduce. And once the ball is released, the player has to anticipate when and where the ball will return.

Liu and Hodgins opted to use deep reinforcement learning to enable the model to pick up these important details. Artificial intelligence programs have used this form of deep learning to figure out a variety of video games and the AlphaGo program famously employed it to master the board game Go.

The motion capture data used as input was of people doing things such as rotating the ball around the waist, dribbling while running and dribbling in place both with the right hand and while switching hands. This capture data did not include the ball movement, which Liu explained is difficult to record accurately. Instead, they used trajectory optimization to calculate the ball’s most likely paths for a given hand motion.

The program learned the skills in two stages — first it mastered locomotion and then learned how to control the arms and hands and, through them, the motion of the ball. This decoupled approach is sufficient for actions such as dribbling or perhaps juggling, where the interaction between the character and the object doesn’t have an effect on the character’s balance. Further work is required to address sports, such as soccer, where balance is tightly coupled with game maneuvers, Liu said.

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Soft multi-functional robots get really small … and spider-shaped

Roboticists are envisioning a future in which soft, animal-inspired robots could be safely deployed in difficult-to-access natural and human-made environments, such as in delicate surgical procedures in the human body, or in spaces too small and unpredictable to be conquered with rigid robots or too dangerous for humans to work with rigid robots in. Centimeter-sized soft robots have been created, but thus far it has not been possible to fabricate multifunctional flexible robots that can move and operate at smaller size scales.

A team of researchers at Harvard’s Wyss Institute for Biologically Inspired Engineering, Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and Boston University now has overcome this challenge by developing an integrated fabrication process that enables the design of soft robots on the millimeter scale with micrometer-scale features. To demonstrate the capabilities of their new technology, they created a robotic soft spider — inspired by the millimeter-sized colorful Australian peacock spider — from a single elastic material with body-shaping, motion, and color features. The study is published in Advanced Materials.

“The smallest soft robotic systems still tend to be very simple, with usually only one degree of freedom, which means that they can only actuate one particular change in shape or type of movement,” said Sheila Russo, Ph.D., co-author of the study. Russo helped initiate the project as a Postdoctoral Fellow in Robert Wood’s group at the Wyss Institute and SEAS and now is Assistant Professor at Boston University. “By developing a new hybrid technology that merges three different fabrication techniques, we created a soft robotic spider made only of silicone rubber with 18 degrees of freedom, encompassing changes in structure, motion, and color, and with tiny features in the micrometer range.”

Wood, Ph.D., is a Core Faculty member and co-leader of the Bioinspired Soft Robotics platform at the Wyss Institute and the Charles River Professor of Engineering and Applied Sciences at SEAS. “In the realm of soft robotic devices, this new fabrication approach can pave the way towards achieving similar levels of complexity and functionality on this small scale as those exhibited by their rigid counterparts. In the future, it can also help us emulate and understand structure-function relationships in small animals much better than rigid robots can,” he said.

In their Microfluidic Origami for Reconfigurable Pneumatic/Hydrolic (MORPH) devices, the team first used a soft lithography technique to generate 12 layers of an elastic silicone that together constitute the soft spider’s material basis. Each layer is precisely cut out of a mold with a laser micromachining technique, and then bonded to the one below to create the rough 3D structure of the soft spider.

Key to transforming this intermediate structure into the final design is a pre-conceived network of hollow microfluidic channels that is integrated into individual layers. With a third technique known as injection-induced self-folding, pressurized one set of these integrated microfluidic channels with a curable resin from the outside. This induces individual layers, and with them also their neighboring layers, to locally bend into their final configuration, which is fixed in space when the resin hardens. This way, for example, the soft spider’s swollen abdomen and downward-curved legs become permanent features.

“We can precisely control this origami-like folding process by varying the thickness and relative consistency of the silicone material adjacent to the channels across different layers or by laser-cutting at different distances from the channels. During pressurization, the channels then function as actuators that induce a permanent structural change,” said first and corresponding author Tommaso Ranzani, Ph.D., who started the study as a Postdoctoral Fellow in Wood’s group and now also is Assistant Professor at Boston University.

The remaining set of integrated microfluidic channels were used as additional actuators to colorize the eyes and simulate the abdominal color patterns of the peacock species by flowing colored fluids; and to induce walking-like movements in the leg structures. “This first MORPH system was fabricated in a single, monolithic process that can be performed in few days and easily iterated in design optimization efforts,” said Ranzani.

“The MORPH approach could open up the field of soft robotics to researchers who are more focused on medical applications where the smaller sizes and flexibility of these robots could enable an entirely new approach to endoscopy and microsurgery,” said Wyss Institute Founding Director Donald Ingber, M.D., Ph.D., who is also the Judah Folkman Professor of Vascular Biology at HMS and the Vascular Biology Program at Boston Children’s Hospital, as well as Professor of Bioengineering at SEAS.

A kernel of promise in popcorn-powered robots

Cornell University researchers have discovered how to power simple robots with a novel substance that, when heated, can expand more than 10 times in size, change its viscosity by a factor of 10 and transition from regular to highly irregular granules with surprising force.

You can also eat it with a little butter and salt.

“Popcorn-Driven Robotic Actuators,” a recent paper co-authored by Steven Ceron, mechanical engineering doctoral student, and Kirstin H. Petersen, assistant professor of electrical and computer engineering, examines how popcorn’s unique qualities can power inexpensive robotic devices that grip, expand or change rigidity.

“The goal of our lab is to try to make very minimalistic robots which, when deployed in high numbers, can still accomplish great things,” said Petersen, who runs Cornell’s Collective Embodied Intelligence Lab. “Simple robots are cheap and less prone to failures and wear, so we can have many operating autonomously over a long time. So we are always looking for new and innovative ideas that will permit us to have more functionalities for less, and popcorn is one of those.”

The study is the first to consider powering robots with popcorn, which is inexpensive, readily available, biodegradable and of course, edible. Since kernels can expand rapidly, exerting force and motion when heated, they could potentially power miniature jumping robots. Edible devices could be ingested for medical procedures. The mix of hard, unpopped granules and lighter popped corn could replace fluids in soft robots without the need for air pumps or compressors.

“Pumps and compressors tend to be more expensive, and they add a lot of weight and expense to your robot,” said Ceron, the paper’s lead author. “With popcorn, in some of the demonstrations that we showed, you just need to apply voltage to get the kernels to pop, so it would take all the bulky and expensive parts out of the robots.”

Since kernels can’t shrink once they’ve popped, a popcorn-powered mechanism can generally be used only once, though multiple uses are conceivable because popped kernels can dissolve in water, Ceron said.

The paper was presented at the IEEE International Conference on Robotics and Automation. Petersen said she hopes it inspires researchers to explore the possibilities of other nontraditional materials.

“Robotics is really good at embracing new ideas, and we can be super creative about what we use to generate multifunctional properties,” she said. “In the end we come up with very simple solutions to fairly complex problems. We don’t always have to look for high-tech solutions. Sometimes the answer is right in front of us.”

The work was supported by the Cornell Engineering Learning Initiative, the Cornell Electrical and Computer Engineering Early Career Award and the Cornell Sloan Fellowship.

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Materials provided by Cornell University. Original written by Melanie Lefkowitz. Note: Content may be edited for style and length.

‘Blurred face’ news anonymity gets an artificial intelligence spin

A research team from SFU’s School of Interactive Arts and Technology (SIAT) has come up with a way to replace the use of ‘blurring’ faces in news reports when anonymity is needed. The team’s method uses artificial intelligence (AI) techniques that aim to improve visuals while amplifying emotions tied to the story.

SIAT professors Steve DiPaola and Kate Hennessy, together with Taylor Owen from UBC’s journalism school, received a Google/Knight Foundation grant to carry out the research. They presented the work to international journalists at a Journalism 360 demo event honoring grantees in New York on July 24, and the next day at a full conference held across the street from the New York Times headquarters.

“Our goal is to create a working technique that would be much better at conveying emotional and knowledge information than current anonymization techniques,” says DiPaola, a pioneer in AI/VR facial recognition processes.

Based on its research, the team has created an updated pixelating technique using an AI “painting” approach to anonymization.

“When artists paint a portrait, they try to convey the subject’s outer and inner resemblance,” says DiPaola, who heads SFU’s Interactive Visualization Lab. “With our AI, which learns from more than 1,000 years of artistic technique, we have taught the system to lower the outer resemblance and keep as high as possible the subject’s inner resemblance — in other words, what they are conveying and how they are feeling.

“Our system uses five levels of AI processing to simulate a smart painter, using art abstraction to repaint the video as if an artist was painting every frame. The result is more engaging, especially since not everyone listens to stories — so the art component becomes more relevant.”

The system doesn’t change the pixels of the video frames as Adobe-like systems can, but instead produces a painting-style result of every frame, DiPaola notes. “It’s actually an open and dynamic process that allows levels of control throughout. We eventually want to the subject or producer to be able to customize the final result based on their needs.”

DiPaola says the tool’s effectiveness at anonymizing while retaining a strong degree of emotional connection or resonance should result in better final product for anonymized video, especially in 360 or VR. The team plans to put the technology to work in a variety of projects.

While at the conference DiPaola’s team garnished interest from several major media companies looking to explore new media approaches, including the New York Times, Washington Post and the BBC.

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Materials provided by Simon Fraser University. Note: Content may be edited for style and length.

This AI-Operated Robotic Hand Moves With “Unprecedented Dexterity”

ROBOTIC HIGH FIVE. On Monday, researchers at OpenAI, the nonprofit AI research company co-founded by Elon Musk, introduced Dactyl, an AI system trained to control a robotic hand. According to the researchers, the system can manipulate physical objects in the hand with dexterity never before possible for AI.

The task Dactyl tackled might sound like something you’d teach a toddler: take this six-sided block and move it around until a certain side is on top. Unlike a toddler, though, Dactyl needed more than a century’s worth of experience to learn how to expertly complete the task. But thanks to powerful computers, the researchers were able to pack all that experience into just 50 “real-world” hours.

PRACTICE MAKES (ALMOST) PERFECT. The researchers trained Dactyl in a simulated environment — that is, a digital setting with a computer-generated hand — using a technique called domain randomization. They built certain parameters into their simulated environment, such as the cube’s size or the angle of gravity, and then randomized those variables. They had multiple simulated hands doing this at once. By pushing Dactyl to adapt to so many different virtual scenarios, the researchers prepared the AI’s ability to adapt to scenarios in the real world.

After 50 hours of training in the simulated environment, the AI could manipulate a real-world robotic hand to successfully complete its given task 50 times in a row (a successful completion was one in which the system didn’t drop the block or take longer than 80 seconds). To figure out how to move the hand to complete the task, it simply needed to look at the block through a trio of cameras.

ONE ALGORITHM TO TRAIN THEM ALL. As the researchers note in their blog post, they trained Dactyl using the same algorithm that they used for OpenAI Five, a team of five neural networks trained to play the computer strategy game DOTA 2. Dactyl’s success proves it’s possible to build a general-purpose algorithm that can teach AI to complete two very different tasks. This could make it much easier for researchers to train AI for lots of different purposes in the future, since they wouldn’t need to start the process from scratch.

READ MORE: Learning Dexterity [OpenAI Blog]

More on OpenAI: The Digest: Five AI Algorithms Worked Together to Beat Humans at a Strategy Game

Google Glass Is Back, And May Have Finally Found A Place It Belongs

“IT’S BAAACK.” Jennifer Bennett, a technical director for Google Cloud, announced that Google Glass would be returning. But instead of being a goofy headset that your everyday person can use to covertly record everyday life, it’s geared towards industrial applications. The factory floor is really where people could use some hands-free assistance, and might not mind being goofy while doing it, according to WIRED.

OKAY, GOOGLE. If you’re among those who breathed a sigh of relief when Google decided to stop selling its glasses a few years back, I have good and bad news. Google Glass resurfaced last summer as the newly-retooled “Enterprise Edition.” If you’re wondering why you don’t have one yet, it’s probably because Google has been marketing these gadgets to businesses, not individual people. And, also, unless you work in a factory, you’re probably no longer its ideal clientele.

The new Google Glass Enterprise Edition includes an app by Israeli software company Plataine that basically embeds a smart assistant into each headset. That means the AI system can understand and respond to voice commands either by displaying information on the glasses or responding out loud.

Google suspects this can help workers manage their workload, scan barcodes and prepare for projects, and look up recommendations without needing to drag a laptop around with them.

THE HAWTHORNE EFFECT. You may recall that gross feeling when you walked by a person wearing Google Glass, that feeling of wondering whether they had just filmed you or subjected you to some sort of facial recognition tech. Well, that element is still there in the Enterprise edition: the WIRED article mentioned that bosses might monitor their employees through their hip new spectacles. While this may boost productivity, it will almost certainly lower morale as employees wonder whether or not their boss is spying on them at any given moment. It’s almost like Google couldn’t resist adding a teensy pinch of technological dystopia to its product.

Read more: Google Glass Is Back — Now With Artificial Intelligence [WIRED]

More about Google Glass: Google Glass Is Back, and It’s No Longer Meant for Everyone