Aurelia Butler

Scientists used the Advanced Photon Source to watch a nonliving material mimic behavior associated with learning, paving the way for better artificial intelligence.
Scientists looking to create a new generation of supercomputers are looking for inspiration from the most complex and energy-efficient computer ever built: the human brain.
In some of their initial forays into making brain-inspired computers, researchers are looking at different nonbiological materials whose properties could be tailored to show evidence of learning-like behaviors. These materials could form the basis for hardware that could be paired with new software algorithms to enable more potent, useful and energy-efficient artificial intelligence (AI).
In a new study led by scientists from Purdue University, researchers have exposed oxygen deficient nickel oxide to brief electrical pulses and elicited two different electrical responses that are similar to learning. The result is an all-electrically-driven system that shows these learning behaviors, said Rutgers University professor Shriram Ramanathan. (Ramanathan was a professor at Purdue University at the time of this work.) The research team used the resources of the Advanced Photon Source (APS), a U.S. Department of Energy (DOE) Office of Science user facility at DOE’s Argonne National Laboratory.
The first response, habituation, occurs when the material “gets used to” being slightly zapped. The scientists noticed that although the material’s resistance increases after an initial jolt, it soon becomes accustomed to the electric stimulus. “Habituation is like what happens when you live near an airport,” said Fanny Rodolakis, a physicist and beamline scientist at the APS. “The day you move in, you think ‘what a racket,’ but eventually you hardly notice anymore.”
The other response shown by the material, sensitization, occurs when a larger dose of electricity is administered. “With a larger stimulus, the material’s response grows instead of diminishing over time,” Rodolakis said. “It’s akin to watching a scary movie, and then having someone say ‘boo!’ from behind a corner — you see it really jump.”
“Pretty much all living organisms demonstrate these two characteristics,” Ramanathan said. “They really are a foundational aspect of intelligence.” More

An artificial intelligence tool developed by researchers at UCL and Queen Mary University of London could help governments decide whether or not to bail out a bank in crisis by predicting if the intervention will save money for taxpayers in the long term.
The AI tool, described in a new paper in Nature Communications, assesses not only if a bailout is the best strategy for taxpayers, but also suggests how much should be invested in the bank, and which bank or banks should be bailed out at any given time.
The algorithm was tested by the authors using data from the European Banking Authority on a network of 35 European financial institutions judged to be the most important to the global financial system, but it can also be used and calibrated by national banks using detailed proprietary data unavailable to the public.
Dr Neofytos Rodosthenous (UCL Mathematics), corresponding author of the paper, said: “Government bank bailouts are complex decisions that have financial, social and political implications. We believe the AI approach we have developed can be an important tool for governments, helping officials assess specifically financial implications — this means checking if a bailout is in the best interest of taxpayers, or whether it would be better value for money to let the bank fail. Our techniques are freely available for banking authorities to use as tools in their decision-making process.”
Co-author Professor Vito Latora (Queen Mary University of London) added: “Governments and banking authorities can also use our approach to retrospectively review past crises and gain valuable learnings to inform future actions. One could, for example, review the UK government bailout of the Royal Bank of Scotland (RBS) during the financial crisis of 2007-9 and reflect on how this could potentially be improved (from a financial standpoint) in the future in order to primarily benefit taxpayers.”
In a bank bailout, a government investment in a bank increases the bank’s equity and reduces its risk of defaulting. This cost in the short term may be justified to the taxpayer if it leads to lower taxpayer losses in the long term — i.e., it prevents bank defaults that are more damaging to government finances. More

Sharing medical data between laboratories and medical experts is important for medical research. However, data sharing is often sufficiently complex and sometimes even impossible due to the strict data regulatory legislation in Europe. Researchers at the University of Jyväskylä Digital Health Intelligence Laboratory addressed the problem and developed an artificial neural network that creates synthetic x-ray images that can fool even medical experts.
A group of researchers from University of Jyväskylä’s AI Hub Central Finland project developed an AI based method to create synthetic knee x-ray images to replace or complement real x-ray images in knee osteoarthritis classification.
Researchers used synthetically generated X-ray images to complement a data set of real X-ray images from the osteoarthritis study. The authenticity of the images was then assessed together with specialists from the central Finland healthcare district.
Medical experts were asked to rate osteoarthritis severity without knowing that the data set included synthetic images. In the second phase, experts tried to identify authentic and synthetic images. The results showed that on average, it was improbable even for medical experts to distinguish between real and synthetic x-ray images.
“The use of synthetic data is not subject to the same data protection regulations as real data. Using synthetic data can facilitate collaboration between, for example, research groups, companies and educational institutions,” says Sami Äyrämö, Head of Digital Health Intelligence Laboratory at the University of Jyväskylä.
According to Äyrämö, the use of synthetic data also speeds up authorisation processes and thus, among other things, testing of new ideas. More

Femtosecond pulsed lasers — which emit light in ultrafast bursts lasting a millionth of a billionth of a second — are powerful tools used in a range of applications from medicine and manufacturing, to sensing and precision measurements of space and time. Today, these lasers are typically expensive table-top systems, which limits their use in applications that have size and power consumption restrictions.
An on-chip femtosecond pulse source would unlock new applications in quantum and optical computing, astronomy, optical communications and beyond. However, it’s been a challenge to integrate tunable and highly efficient pulsed lasers onto chips.
Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a high-performance, on-chip femtosecond pulse source using a tool that seems straight out of science fiction: a time lens.
The research is published in Nature.
“Pulsed lasers that produce high-intensity, short pulses consisting of many colors of light have remained large,” said Marko Lon?ar, the Tiantsai Lin Professor of Electrical Engineering at SEAS and senior author of the study. “To make these sources more practical, we decided to shrink a well-known approach, used to realize conventional — and large — femtosecond sources, leveraging a state of the art integrated photonics platform that we have developed. Importantly, our chips are made using microfabrication techniques like those used to make computer chips, which ensures not only reduced cost and size, but also improved performance and reliability of our femtosecond sources.”
Traditional lenses, like contact lenses or those found in magnifying glasses and microscopes, bend rays of light coming from different directions by altering their phase so that they hit the same location in space — the focal point. More

Researchers at Purdue University have discovered new waves with picometer-scale spatial variations of electromagnetic fields which can propagate in semiconductors like silicon. The research team, led by Dr. Zubin Jacob, Elmore Associate Professor of Electrical and Computer Engineering and Department of Physics and Astronomy (courtesy), published their findings in APS Physics Review Applied in a paper titled, “Picophotonics: Anomalous Atomistic Waves in Silicon.”
“The word microscopic has its origins in the length scale of a micron which is a million times smaller than a meter. Our work is for light matter interaction within the picoscopic regime which is far smaller, where the discrete arrangement of atomic lattices changes light’s properties in surprising ways,” says Jacob.
These intriguing findings demonstrate that natural media host a variety of rich light-matter interaction phenomena at the atomistic level. The use of picophotonic waves in semiconducting materials may lead researchers to design new, functional optical devices, allowing for applications in quantum technologies.
Light-matter interaction in materials is central to several photonic devices from lasers to detectors. Over the past decade, nanophotonics, the study of how light flows on the nanometer scale in engineered structures such as photonic crystals and metamaterials have led to important advances. This existing research can be captured within the realm of classical theory of atomic matter. The current finding leading to picophotonics was made possible by a major leap forward using a quantum theory of atomistic response in matter. The team consists of Jacob as well as Dr. Sathwik Bharadwaj, research scientist at Purdue University, and Dr. Todd Van Mechelen, former post-doc at Purdue University.
The long-standing puzzle in the field was the missing link between atomic lattices, their symmetries and the role it plays on deeply picoscopic light fields. To answer this puzzle, the theory team developed a Maxwell Hamiltonian framework of matter combined with a quantum theory of light induced response in materials.
“This is a pivotal shift from the classical treatment of light flow applied in nanophotonics,” says Jacob. “The quantum nature of light’s behavior in materials is the key for the emergence of picophotonics phenomena.”
Bharadwaj and colleagues showed that hidden amidst traditional well-known electromagnetic waves, new anomalous waves emerge in the atomic lattice. These light waves are highly oscillatory even within one fundamental building block of the silicon crystal (sub-nanometer length scale).
“Natural materials itself have rich intrinsic crystal lattice symmetries and light is strongly influenced by these symmetries,” says Bharadwaj. “The immediate next goal is to apply our theory to the plethora of quantum and topological materials and also verify the existence of these new waves experimentally.”
“Our group has been leading the frontier of research on pico-scale electrodynamic fields inside matter at the atomistic level,” says Jacob. “We recently initiated the picoelectrodynamics theory network where we are bringing together diverse researchers to explore macroscopic phenomena stemming from microscopic pico-electrodynamic fields inside matter.”
This research was funded by the DARPA QUEST program.
Writer: Cheryl Pierce, communications specialist, Earth, Atmospheric, Planetary Sciences | Physics/Astronomy, Purdue University
Story Source:
Materials provided by Purdue University. Original written by Cheryl Pierce. Note: Content may be edited for style and length. More

This little robot can go almost anywhere.
Researchers at Carnegie Mellon University’s School of Computer Science and the University of California, Berkeley, have designed a robotic system that enables a low-cost and relatively small legged robot to climb and descend stairs nearly its height; traverse rocky, slippery, uneven, steep and varied terrain; walk across gaps; scale rocks and curbs; and even operate in the dark.
“Empowering small robots to climb stairs and handle a variety of environments is crucial to developing robots that will be useful in people’s homes as well as search-and-rescue operations,” said Deepak Pathak, an assistant professor in the Robotics Institute. “This system creates a robust and adaptable robot that could perform many everyday tasks.”
The team put the robot through its paces, testing it on uneven stairs and hillsides at public parks, challenging it to walk across stepping stones and over slippery surfaces, and asking it to climb stairs that for its height would be akin to a human leaping over a hurdle. The robot adapts quickly and masters challenging terrain by relying on its vision and a small onboard computer.
The researchers trained the robot with 4,000 clones of it in a simulator, where they practiced walking and climbing on challenging terrain. The simulator’s speed allowed the robot to gain six years of experience in a single day. The simulator also stored the motor skills it learned during training in a neural network that the researchers copied to the real robot. This approach did not require any hand-engineering of the robot’s movements — a departure from traditional methods.
Most robotic systems use cameras to create a map of the surrounding environment and use that map to plan movements before executing them. The process is slow and can often falter due to inherent fuzziness, inaccuracies, or misperceptions in the mapping stage that affect the subsequent planning and movements. Mapping and planning are useful in systems focused on high-level control but are not always suited for the dynamic requirements of low-level skills like walking or running over challenging terrains. More

When fighting the spread of misinformation, social media platforms typically place most users in the passenger seat. Platforms often use machine-learning algorithms or human fact-checkers to flag false or misinforming content for users.
“Just because this is the status quo doesn’t mean it is the correct way or the only way to do it,” says Farnaz Jahanbakhsh, a graduate student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).
She and her collaborators conducted a study in which they put that power into the hands of social media users instead.
They first surveyed people to learn how they avoid or filter misinformation on social media. Using their findings, the researchers developed a prototype platform that enables users to assess the accuracy of content, indicate which users they trust to assess accuracy, and filter posts that appear in their feed based on those assessments.
Through a field study, they found that users were able to effectively assess misinforming posts without receiving any prior training. Moreover, users valued the ability to assess posts and view assessments in a structured way. The researchers also saw that participants used content filters differently — for instance, some blocked all misinforming content while others used filters to seek out such articles.
This work shows that a decentralized approach to moderation can lead to higher content reliability on social media, says Jahanbakhsh. This approach is also more efficient and scalable than centralized moderation schemes, and may appeal to users who mistrust platforms, she adds. More

New wearable electronics paired with artificial intelligence could transform screening for health problems.
Flexible, wearable electronics are making their way into everyday use, and their full potential is still to be realized. Soon, this technology could be used for precision medical sensors attached to the skin, designed to perform health monitoring and diagnosis. It would be like having a high-tech medical center at your instant beck and call.
Such a skin-like device is being developed in a project between the U.S. Department of Energy’s (DOE) Argonne National Laboratory and the University of Chicago’s Pritzker School of Molecular Engineering (PME). Leading the project is Sihong Wang, assistant professor in UChicago PME with a joint appointment in Argonne’s Nanoscience and Technology division.
Worn routinely, future wearable electronics could potentially detect possible emerging health problems — such as heart disease, cancer or multiple sclerosis — even before obvious symptoms appear. The device could also do a personalized analysis of the tracked health data while minimizing the need for its wireless transmission. “The diagnosis for the same health measurements could differ depending on the person’s age, medical history and other factors,” Wang said. “Such a diagnosis, with health information being continuously gathered over an extended period, is very data intensive.”
Such a device would need to collect and process a vast amount of data, well above what even the best smartwatches can do today. And it would have to do this data crunching with very low power consumption in a very tiny space.
To address that need, the team called upon neuromorphic computing. This AI technology mimics operation of the brain by training on past data sets and learning from experience. Its advantages include compatibility with stretchable material, lower energy consumption and faster speed than other types of AI. More