Aurelia Butler

It’s easy to believe that robots are stealing jobs from human workers and drastically disrupting the labor market; after all, you’ve likely heard that chatbots make more efficient customer service representatives and that computer programs are tracking and moving packages without the use of human hands.
But there’s no need to panic about a pending robot takeover just yet, says a new study from BYU sociology professor Eric Dahlin. Dahlin’s research found that robots aren’t replacing humans at the rate most people think, but people are prone to severely exaggerate the rate of robot takeover.
The study, recently published in Socius: Sociological Research for a Dynamic World, found that only 14% of workers say they’ve seen their job replaced by a robot. But those who have experienced job displacement due to a robot overstate the effect of robots taking jobs from humans by about three times.
To understand the relationship between job loss and robots, Dahlin surveyed nearly 2,000 individuals about their perceptions of jobs being replaced by robots. Respondents were first asked to estimate the percentage of employees whose employers have replaced jobs with robots. They were then asked whether their employer had ever replaced their job with a robot.
Those who had been replaced by a robot (about 14%), estimated that 47% of all jobs have been taken over by robots. Similarly, those who hadn’t experienced job replacement still estimated that 29% of jobs have been supplanted by robots.
“Overall, our perceptions of robots taking over is greatly exaggerated,” said Dahlin. “Those who hadn’t lost jobs overestimated by about double, and those who had lost jobs overestimated by about three times.”
Attention-grabbing headlines predicting a dire future of employment have likely overblown the threat of robots taking over jobs, said Dahlin, who noted that humans’ fear of being replaced by automated work processes dates to the early 1800s.
“We expect novel technologies to be adopted without considering all of the relevant contextual impediments such as cultural, economic, and government arrangements that support the manufacturing, sale, and use of the technology,” he said. “But just because a technology can be used for something does not mean that it will be implemented.”
Dahlin says these findings are consistent with previous studies, which suggest that robots aren’t displacing workers. Rather, workplaces are integrating both employees and robots in ways that generate more value for human labor.
“An everyday example is an autonomous, self-propelled machine roaming the isles and cleaning floors at your local grocery store,” says Dahlin. “This robot cleans the floors while employees clean under shelves or other difficult-to-reach places.”
Dahlin says the aviation industry is another good example of robots and humans working together. Airplane manufacturers used robots to paint airplane wings. A robot can administer one coat of paint in 24 minutes — something that would take a human painter hours to accomplish. Humans load and unload the paint while the robot does the painting.
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Materials provided by Brigham Young University. Original written by Tyler Stahle. Note: Content may be edited for style and length. More

Technology developed by researchers at the Indiana University School of Medicine that can change skin tissue into blood vessels and nerve cells has also shown promise as a treatment for traumatic muscle loss.
Tissue nanotransfection is a minimally invasive nanochip device that can reprogram tissue function by applying a harmless electric spark to deliver specific genes in a fraction of a second.
A new study, published in Nature Partner Journals Regenerative Medicine, tested tissue nanotransfection-based gene therapy as a treatment, with the goal of delivering a gene known to be a major driver of muscle repair and regeneration. They found that muscle function improved when tissue nanotransfection was used as a therapy for seven days following volumetric muscle loss in rats. It is the first study to report that tissue nanotransfection technology can be used to generate muscle tissue and demonstrates its benefit in addressing volumetric muscle loss.
Volumetric muscle loss is the traumatic or surgical loss of skeletal muscle that results in compromised muscle strength and mobility. Incapable of regenerating the amount of lost tissue, the affected muscle undergoes substantial loss of function, thus compromising quality of life. A 20 percent loss in mass can result in an up to 90 percent loss in muscle function.
Current clinical treatments for volumetric muscle loss are physical therapy or autologous tissue transfer (using a person’s own tissue), the outcomes of which are promising but call for improved treatment regimens.
“We are encouraged that tissue nanotransfection is emerging as a versatile platform technology for gene delivery, gene editing and in vivo tissue reprogramming,” said Chandan Sen, director of the Indiana Center for Regenerative Medicine and Engineering, associate vice president for research and Distinguished Professor at the IU School of Medicine. “This work proves the potential of tissue nanotransfection in muscle tissue, opening up a new avenue of investigational pursuit that should help in addressing traumatic muscle loss. Importantly, it demonstrates the versatility of the tissue nanotransfection technology platform in regenerative medicine.”
Sen also leads the regenerative medicine and engineering scientific pillar of the IU Precision Health Initiative and is lead author on the new publication.
The Indiana Center for Regenerative Medicine and Engineering is home to the tissue nanotransfection technology for in vivo tissue reprogramming, gene delivery and gene editing. So far, tissue nanotransfection has also been achieved in blood vessel and nerve tissue. In addition, recent work has shown that topical tissue nanotransfection can achieve cell-specific gene editing of skin wound tissue to improve wound closure.
Other study authors include Andrew Clark, Subhadip Ghatak, Poornachander Reddy Guda, Mohamed S. El Masry and Yi Xuan, all of IU, and Amy Y. Sato and Teresita Bellido of Purdue University.
This work was supported by Department of Defense Discovery Award W81XWH-20-1-251. It is also supported in part by NIH grant DK128845 and Lilly Endowment INCITE (Indiana Collaborative Initiative for Talent Enrichment).
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Materials provided by Indiana University. Note: Content may be edited for style and length. More

A new study at Monash University illustrates how substrates affect strong electronic interactions in two-dimensional metal-organic frameworks.
Materials with strong electronic interactions can have applications in energy-efficient electronics. When these materials are placed on a substrate, their electronic properties are changed by charge transfer, strain, and hybridisation.
The study also shows that electric fields and applied strain could be used to ‘switch’ interacting phases such as magnetism on and off, allowing potential applications in future energy-efficient electronics.
TURNING MAGNETISM ON AND OFF WITH SUBSTRATES
Strong interactions between electrons in materials gives rise to effects such as magnetism and superconductivity. These effects have uses in magnetic memory, spintronics, and quantum computing, making them appealing for emerging technologies.
Last year, another study at Monash discovered strong electronic interactions in a 2D metal-organic framework. The researchers found signatures of magnetism in this material. They showed that this magnetism arose due to strong interactions that were only present when the non-magnetic components were brought together. More

This is the first time AI has been used to automatically discover vulnerabilities in this type of system, examples of which are used by Google Maps and Facebook.
The experts, from Imperial’s Computational Privacy Group, looked at attacks on query-based systems (QBS) — controlled interfaces through which analysts can query data to extract useful aggregate information about the world. They then developed a new AI-enabled method called QuerySnout to detect attacks on QBS.
QBS give analysts access to collections of statistics gathered from individual-level data like location and demographics. They are currently used in Google Maps to show live information on how busy an area is, or in Facebook’s Audience Measurement feature to estimate audience size in a particular location or demographic to help with advertising promotions.
In their new study, published as part of the 29th ACM Conference on Computer and Communications Security, the team including the Data Science Institute’s Ana Maria Cretu, Dr Florimond Houssiau, Dr Antoine Cully and Dr Yves-Alexandre de Montjoye found that powerful and accurate attacks against QBS can easily be automatically detected at the pressing of a button.
According to Senior Author Dr Yves-Alexandre de Montjoye: “Attacks have so far been manually developed using highly skilled expertise. This means it was taking a long time for vulnerabilities to be discovered, which leaves systems at risk.
“OuerySnout is already outperforming humans at discovering vulnerabilities in real-world systems.”
The need for query-based systems More

Using a miniature camera and a customized deep neural network, Cornell researchers have developed a first-of-its-kind wristband that tracks the entire body posture in 3D.
BodyTrak is the first wearable to track the full body pose with a single camera. If integrated into future smartwatches, BodyTrak could be a game-changer in monitoring user body mechanics in physical activities where precision is critical, said Cheng Zhang, assistant professor of information science and the paper’s senior author.
“Since smartwatches already have a camera, technology like BodyTrak could understand the user’s pose and give real-time feedback,” Zhang said. “That’s handy, affordable and does not limit the user’s moving area.”
A corresponding paper, “BodyTrak: Inferring Full-body Poses from Body Silhouettes Using a Miniature Camera on a Wristband,” was published in the Proceedings of the Association for Computing Machinery (ACM) on Interactive, Mobile, Wearable and Ubiquitous Technology, and presented in September at UbiComp 2022, the ACM international conference on pervasive and ubiquitous computing.
BodyTrak is the latest body-sensing system from the SciFiLab — based in the Cornell Ann S. Bowers College of Computing and Information Science — a group that has previously developed and leveraged similar deep learning models to track hand and finger movements, facial expressions and even silent-speech recognition.
The secret to BodyTrak is not only in the dime-sized camera on the wrist, but also the customized deep neural network behind it. This deep neural network — a method of AI that trains computers to learn from mistakes — reads the camera’s rudimentary images or “silhouettes” of the user’s body in motion and virtually re-creates 14 body poses in 3D and in real time.
In other words, the model accurately fills out and completes the partial images captured by the camera, said Hyunchul Lim, a doctoral student in the field of information science and the paper’s lead author.
“Our research shows that we don’t need our body frames to be fully within camera view for body sensing,” Lim said. “If we are able to capture just a part of our bodies, that is a lot of information to infer to reconstruct the full body.”
Maintaining privacy for bystanders near someone wearing such a sensing device is a legitimate concern when developing these technologies, Zhang and Lim said. They said BodyTrak mitigates privacy concerns for bystanders since the camera is pointed toward the user’s body and collects only partial body images of the user.
They also recognize that today’s smartwatches don’t yet have small or powerful enough cameras and adequate battery life to integrate full body sensing, but could in the future.
Along with Lim and Zhang, paper co-authors are Matthew Dressa ’22, Jae Hoon Kim ’23 and Ruidong Zhang, a doctoral student in the field of information science; Yaxuan Li of McGill University; and Fang Hu of Shanghai Jian Tong University.
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Materials provided by Cornell University. Original written by Louis DiPietro. Note: Content may be edited for style and length. More

Our current chip technology is largely based on silicon. Only in very special components a small amount of germanium is added. But there are good reasons to use higher germanium contents in the future: The compound semiconductor silicon-germanium has decisive advantages over today’s silicon technology in terms of energy efficiency and achievable clock frequencies.
The main problem here is to establish contacts between metal and semiconductor on a nanoscale in a reliable way. This is much more difficult with a high proportion of germanium than with silicon. The team at TU Wien, however, together with research teams from Linz and Thun (Switzerland), has now shown that this problem can be solved — with contacts made of crystalline aluminium of extremely high quality and a sophisticated silicon germanium layer system. This enables different interesting contact properties — especially for optoelectronic and quantum components.
The problem with oxygen
“Every semiconductor layer is automatically contaminated in conventional processes; this simply cannot be prevented at the atomic level,” says Masiar Sistani from the Institute for Solid State Electronics at TU Wien. First and foremost, it is oxygen atoms that accumulate very quickly on the surface of the materials — an oxide layer is formed.
With silicon, however, this is not a problem: silicon always forms exactly the same kind of oxide. “With germanium, however, things are much more complicated,” explains Masiar Sistani. “In this case, there is a whole range of different oxides that can form. But that means that different nanoelectronic devices can have very different surface compositions and therefore different electronic properties.”
If you now want to connect a metallic contact to these components, you have a problem: Even if you try very hard to produce all these components in exactly the same way, there are still inevitably massive differences — and that makes the material complex to handle for use in the semiconductor industry. More

Universitat Autònoma de Barcelona researchers have developed a magnetic material capable of imitating the way the brain stores information. The material makes it possible to emulate the synapses of neurons and mimic, for the first time, the learning that occurs during deep sleep.
Neuromorphic computing is a new computing paradigm in which the behavior of the brain is emulated by mimicking the main synaptic functions of neurons. Among these functions is neuronal plasticity: the ability to store information or forget it depending on the duration and repetition of the electrical impulses that stimulate neurons, a plasticity that would be linked to learning and memory.
Among the materials that mimic neuron synapses, memresistive materials, ferroelectrics, phase change memory materials, topological insulators and, more recently, magneto-ionic materials stand out. In the latter, changes in the magnetic properties are induced by the displacement of ions within the material caused by the application of an electric field. In these materials it is well known how the magnetism is modulated when applying the electric field, but the evolution of magnetic properties when voltage is stopped (that is, the evolution after the stimulus) is difficult to control. This makes it complicated to emulate some brain-inspired functions, such as maintaining the efficiency of learning that takes place even while the brain is in a state of deep sleep (i.e., without external stimulation).
This study, led by researchers from the UAB Department of Physics Jordi Sort and Enric Menéndez, in collaboration with the ALBA Synchrotron, the Catalan Institute of Nanoscience and Nanotechnology (ICN2) and the ICMAB, proposes a new way of controlling the evolution of magnetization both in the stimulated and in the post-stimulus states.
The researchers have developed a material based on a thin layer of cobalt mononitride (CoN) where, by applying an electric field, the accumulation of N ions at the interface between the layer and a liquid electrolyte in which the layer has been placed can be controlled. “The new material works with the movement of ions controlled by electrical voltage, in a manner analogous to our brain, and at speeds similar to those produced in neurons, of the order of milliseconds,” explain ICREA research professor Jordi Sort and Serra Húnter Tenure-track Professor Enric Menéndez. “We have developed an artificial synapse that in the future may be the basis of a new computing paradigm, alternative to the one used by current computers,” Sort and Menéndez point out.
By applying voltage pulses, it has been possible to emulate, in a controlled way, processes such as memory, information processing, information retrieval and, for the first time, the controlled updating of information without applied voltage. This control has been achieved by modifying the thickness of the cobalt mononitride layers (which determines the speed of the ions motion), and the frequency of the pulses. The arrangement of the material allows the magnetoionic properties to be controlled not only when the voltage is applied but also, for the first time, when the voltage is removed. Once the external voltage stimulus disappears, the magnetization of the system can be reduced or increased, depending on the thickness of the material and the protocol how the voltage has been previously applied.
This new effect opens a whole range of opportunities for new neuromorphic computing functions. It offers a new logic function that allows, for example, the possibility of mimicking the neural learning that occurs after brain stimulation, when we sleep profoundly. This functionality cannot be emulated by any other type of existing neuromorphic materials.
“When the thickness of the cobalt mononitride layer is below 50 nanometers and with a voltage applied at a frequency greater than 100 cycles per second, we have managed to emulate an additional logic function: once the voltage is applied, the device can be programmed to learn or to forget, without the need for any additional input of energy, mimicking the synaptic functions that take place in the brain during deep sleep, when information processing can continue without applying any external signal,” highlight Jordi Sort and Enric Menendez.
The research, published in Materials Horizons, has been led by researchers from the UAB Department of Physics Jordi Sort, also a researcher at the Catalan Institute for Research and Advanced Studies (ICREA), and Enric Menéndez (Serra Húnter Tenure-track Professor). and with the participation of Zhengwei Tan, Julius de Rojas and Sofia Martins, researchers from the UAB Department of Physics; Aitor Lopeandia, from the Physics Department of the UAB and the Catalan Institute of Nanoscience and Nanotechnology (ICN2); Alberto Quintana, from the Barcelona Institute of Materials Science (ICMAB-CSIC); Javier Herrero-Martín, from the ALBA Synchrotron; José L. Costa-Krämer, from the Institute of Micro and Nanotechnology (IMN-CNM-CSIC); and researchers from CNR-SPIN in Italy, and from IMEC and Quantum Solid State Physics (KU Leuven) in Belgium. More