Aurelia Butler

ChatGPT’s impact extends beyond the education sector and is causing significant changes in other areas. The AI language model is recognized for its ability to perform various tasks, including paper writing, translation, coding, and more, all through question-and-answer-based interactions. The AI system relies on deep learning, which requires extensive training to minimize errors, resulting in frequent data transfers between memory and processors. However, traditional digital computer systems’ von Neumann architecture separates the storage and computation of information, resulting in increased power consumption and significant delays in AI computations. Researchers have developed semiconductor technologies suitable for AI applications to address this challenge.
A research team at POSTECH, led by Professor Yoonyoung Chung (Department of Electrical Engineering, Department of Semiconductor Engineering), Professor Seyoung Kim (Department of Materials Science and Engineering, Department of Semiconductor Engineering), and Ph.D. candidate Seongmin Park (Department of Electrical Engineering), has developed a high-performance AI semiconductor device using indium gallium zinc oxide (IGZO), an oxide semiconductor widely used in OLED displays. The new device has proven to be excellent in terms of performance and power efficiency.
Efficient AI operations, such as those of ChatGPT, require computations to occur within the memory responsible for storing information. Unfortunately, previous AI semiconductor technologies were limited in meeting all the requirements, such as linear and symmetric programming and uniformity, to improve AI accuracy.
The research team sought IGZO as a key material for AI computations that could be mass-produced and provide uniformity, durability, and computing accuracy. This compound comprises four atoms in a fixed ratio of indium, gallium, zinc, and oxygen and has excellent electron mobility and leakage current properties, which have made it a backplane of the OLED display.
Using this material, the researchers developed a novel synapse device composed of two transistors interconnected through a storage node. The precise control of this node’s charging and discharging speed has enabled the AI semiconductor to meet the diverse performance metrics required for high-level performance. Furthermore, applying synaptic devices to a large-scale AI system requires the output current of synaptic devices to be minimized. The researchers confirmed the possibility of utilizing the ultra-thin film insulators inside the transistors to control the current, making them suitable for large-scale AI.
The researchers used the newly developed synaptic device to train and classify handwritten data, achieving a high accuracy of over 98%, which verifies its potential application in high-accuracy AI systems in the future.
Professor Chung explained, “The significance of my research team’s achievement is that we overcame the limitations of conventional AI semiconductor technologies that focused solely on material development. To do this, we utilized materials already in mass production. Furthermore, Linear and symmetrical programming characteristics were obtained through a new structure using two transistors as one synaptic device. Thus, our successful development and application of this new AI semiconductor technology show great potential to improve the efficiency and accuracy of AI.”
This study was published last week on the inside back cover of Advanced Electronic Materials and was supported by the Next-Generation Intelligent Semiconductor Technology Development Program through the National Research Foundation, funded by the Ministry of Science and ICT of Korea. More

The secret to a perfect croissant is the layers — as many as possible, each one interspersed with butter. Similarly, a new material with promise for new applications is made of many extremely thin layers of metal, between which scientists can slip different ions for various purposes. This makes them potentially very useful for future high-tech electronics or energy storage.
Until recently, these materials — known as MXenes, pronounced “max-eens” — were as labor-intensive as good croissants made in a French bakery.
But a new breakthrough by scientists with the University of Chicago shows how to make these MXenes far more quickly and easily, with fewer toxic byproducts.
Researchers hope the discovery, published March 24 in Science, will spur new innovation and pave the way towards using MXenes in everyday electronics and devices.
Atom economy
When they were discovered in 2011, MXenes made a lot of scientists very excited. Usually, when you shave a metal like gold or titanium to create atomic-thin sheets, it stops behaving like a metal. But unusually strong chemical bonds in MXenes allow them to retain the special abilities of metal, like conducting electricity strongly.

They’re also easily customizable: “You can put ions between the layers to use them to store energy, for example,” said chemistry graduate student Di Wang, co-first author of the paper along with postdoctoral scholar Chenkun Zhou.
All of these advantages could make MXenes extremely useful for building new devices — for example, to store electricity or to block electromagnetic wave interference.
However, the only way we knew to make MXenes involved several intensive chemical engineering steps, including heating the mixture at 3,000°F followed by a bath in hydrofluoric acid.
“This is fine if you’re making a few grams for experiments in the laboratory, but if you wanted to make large amounts to use in commercial products, it would become a major corrosive waste disposal issue,” explained Dmitri Talapin, the Ernest DeWitt Burton Distinguished Service Professor of Chemistry at the University of Chicago, joint appointee at Argonne National Laboratory and the corresponding author on the paper.
To design a more efficient and less toxic method, the team used the principles of chemistry — in particular “atom economy,” which seeks to minimize the number of wasted atoms during a reaction.

The UChicago team discovered new chemical reactions that allow scientists to make MXenes from simple and inexpensive precursors, without the use of hydrofluoric acid. It consists of just one step: mixing several chemicals with whichever metal you wish to make layers of, then heating the mixture at 1,700°F. “Then you open it up and there they are,” said Wang.
The easier, less toxic method opens up new avenues for scientists to create and explore new varieties of MXenes for different applications — such as different metal alloys or different ion flavorings. The team tested the method with titanium and zirconium metals, but they think the technique can also be used for many other different combinations.
“These new MXenes are also visually beautiful,” Wang added. “They stand up like flowers — which may even make them better for reactions, because the edges are exposed and accessible for ions and molecules to move in between the metal layers.”
Graduate student Wooje Cho was also a co-author on the paper. The exploration was made possible by help from UChicago colleagues across departments, including theoretical chemist Suri Vaikuntanathan, X-ray research facility director Alexander Filatov, and electrochemists Chong Liu and Mingzhan Wang of the Pritzker School of Molecular Engineering. Electron microscopy was performed by Robert Klie and Francisco Lagunas with the University of Illinois Chicago.
Part of the research was conducted via the U.S. Department of Energy’s Advanced Materials for Energy-Water Systems, an Energy Frontier Research Center; the University of Chicago Materials Research Science and Engineering Center; and at the Center for Nanoscale Materials at Argonne National Laboratory. More

Using artificial intelligence, researchers have discovered how to screen for genetic mutations in cancerous brain tumors in under 90 seconds — and possibly streamline the diagnosis and treatment of gliomas, a study suggests.
A team of neurosurgeons and engineers at Michigan Medicine, in collaboration with investigators from New York University, University of California, San Francisco and others, developed an AI-based diagnostic screening system called DeepGlioma that uses rapid imaging to analyze tumor specimens taken during an operation and detect genetic mutations more rapidly.
In a study of more than 150 patients with diffuse glioma, the most common and deadly primary brain tumor, the newly developed system identified mutations used by the World Health Organization to define molecular subgroups of the condition with an average accuracy over 90%. The results are published in Nature Medicine.
“This AI-based tool has the potential to improve the access and speed of diagnosis and care of patients with deadly brain tumors,” said lead author and creator of DeepGlioma Todd Hollon, M.D., a neurosurgeon at University of Michigan Health and assistant professor of neurosurgery at U-M Medical School.
Molecular classification is increasingly central to the diagnosis and treatment of gliomas, as the benefits and risks of surgery vary among brain tumor patients depending on their genetic makeup. In fact, patients with a specific type of diffuse glioma called astrocytomas can gain an average of five years with complete tumor removal compared to other diffuse glioma subtypes.
However, access to molecular testing for diffuse glioma is limited and not uniformly available at centers that treat patients with brain tumors. When it is available, Hollon says, the turnaround time for results can take days, even weeks.

“Barriers to molecular diagnosis can result in suboptimal care for patients with brain tumors, complicating surgical decision-making and selection of chemoradiation regimens,” Hollon said.
Prior to DeepGlioma, surgeons did not have a method to differentiate diffuse gliomas during surgery. An idea that started in 2019, the system combines deep neural networks with an optical imaging method known as stimulated Raman histology, which was also developed at U-M, to image brain tumor tissue in real time.
“DeepGlioma creates an avenue for accurate and more timely identification that would give providers a better chance to define treatments and predict patient prognosis,” Hollon said.
Even with optimal standard-of-care treatment, patients with diffuse glioma face limited treatment options. The median survival time for patients with malignant diffuse gliomas is only 18 months.
While the development of medications to treat the tumors is essential, fewer than 10% of patients with glioma are enrolled in clinical trials, which often limit participation by molecular subgroups. Researchers hope that DeepGlioma can be a catalyst for early trial enrollment.
“Progress in the treatment of the most deadly brain tumors has been limited in the past decades- in part because it has been hard to identify the patients who would benefit most from targeted therapies,” said senior author Daniel Orringer, M.D., an associate professor of neurosurgery and pathology at NYU Grossman School of Medicine, who developed stimulated Raman histology. “Rapid methods for molecular classification hold great promise for rethinking clinical trial design and bringing new therapies to patients.”
Additional authors include Cheng Jiang, Asadur Chowdury, Akhil Kondepudi, Arjun Adapa, Wajd Al-Holou, Jason Heth, Oren Sagher, Maria Castro, Sandra Camelo-Piragua, Honglak Lee, all of University of Michigan, Mustafa Nasir-Moin, John Golfinos, Matija Snuderl, all of New York University, Alexander Aabedi, Pedro Lowenstein, Mitchel Berger, Shawn Hervey-Jumper, all of University of California, San Francisco, Lisa Irina Wadiura, Georg Widhalm, both of Medical University Vienna, Volker Neuschmelting, David Reinecke, Niklas von Spreckelsen, all of University Hospital Cologne, and Christian Freudiger, Invenio Imaging, Inc.
This work was supported by the National Institutes of Health, Cook Family Brain Tumor Research Fund, the Mark Trauner Brain Research Fund, the Zenkel Family Foundation, Ian’s Friends Foundation and the UM Precision Health Investigators Awards grant program. More

Engineers are harnessing artificial intelligence (AI) and wireless technology to unobtrusively monitor elderly people in their living spaces and provide early detection of emerging health problems.
The new system, built by researchers at the University of Waterloo, follows an individual’s activities accurately and continuously as it gathers vital information without the need for a wearable device and alerts medical experts to the need to step in and provide help.
“After more than five years of working on this technology, we’ve demonstrated that very low-power, millimetre-wave radio systems enabled by machine learning and artificial intelligence can be reliably used in homes, hospitals and long-term care facilities,” said Dr. George Shaker, an adjunct associate professor of electrical and computer engineering.
“An added bonus is that the system can alert healthcare workers to sudden falls, without the need for privacy-intrusive devices such as cameras.”
The work by Shaker and his colleagues comes as overburdened public healthcare systems struggle to meet the urgent needs of rapidly growing elderly populations.
While a senior’s physical or mental condition can change rapidly, it’s almost impossible to track their movements and discover problems 24/7 — even if they live in long-term care. In addition, other existing systems for monitoring gait — how a person walks — are expensive, difficult to operate, impractical for clinics and unsuitable for homes.

The new system represents a major step forward and works this way: first, a wireless transmitter sends low-power waveforms across an interior space, such as a long-term care room, apartment or home.
As the waveforms bounce off different objects and the people being monitored, they’re captured and processed by a receiver. That information goes into an AI engine which deciphers the processed waves for detection and monitoring applications.
The system, which employs extremely low-power radar technology, can be mounted simply on a ceiling or by a wall and doesn’t suffer the drawbacks of wearable monitoring devices, which can be uncomfortable and require frequent battery charging.
“Using our wireless technology in homes and long-term care homes can effectively monitor various activities such as sleeping, watching TV, eating and the frequency of bathroom use,” Shaker said.
“Currently, the system can alert care workers to a general decline in mobility, increased likelihood of falls, possibility of a urinary tract infection, and the onset of several other medical conditions.”
Waterloo researchers have partnered with a Canadian company, Gold Sentintel, to commercialize the technology, which has already been installed in several long-term care homes.
A paper on the work, AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing, appears in the IEEE Internet of Things Journal.
Doctoral student Hajar Abedi was the lead author, with contributions from Ahmad Ansariyan, Dr. Plinio Morita, Dr. Jen Boger and Dr. Alexander Wong. More

ChatGPT has the potential to create increasing and exciting opportunities — but also poses significant challenges — for the academic community, according to an innovative study written in large part using the software.
Launched in November 2022, ChatGPT is the latest chatbot and artificial intelligence (AI) platform touted as having the potential to revolutionise research and education.
However, as it becomes ever more advanced, the technology has also prompted concerns across the education sector about academic honesty and plagiarism.
To address some of these, the new study directly uses ChatGPT to demonstrate how sophisticated Large Language Machines (LLMs) have become but also the steps that can be taken to ensure its influence remains a positive one.
Published in the peer-reviewed journal Innovations in Education and Teaching International, the research was conceived by academics from Plymouth Marjon University and the University of Plymouth.
For the majority of the paper, they used a series of prompts and questions to encourage ChatGPT to produce content in an academic style. These included: Write an original academic paper, with references, describing the implications of GPT-3 for assessment in higher education; How can academics prevent students plagiarising using GPT-3? Are there any technologies which will check if work has been written by a chatbot? Produce several witty and intelligent titles for an academic research paper on the challenges universities face in ChatGPT and plagiarism.Once the text was generated, they copied and pasted the output into the manuscript, ordered it broadly following the structure suggested by ChatGPT, and then inserted genuine references throughout.

This process was only revealed to readers in the paper’s Discussion section, which was written directly by the researchers without the software’s input.
In that section, the study’s authors highlight that the text produced by ChatGPT — while much more sophisticated than previous innovations in this area — can be relatively formulaic, and that a number of existing AI-detection tools would pick up on that.
However, they say their findings should serve as a wake-up call to university staff to think very carefully about the design of their assessments and ways to ensure that academic dishonesty is clearly explained to students and minimised.
Professor Debby Cotton, Director of Academic Practice and Professor of Higher Education at Plymouth Marjon University, is the study’s lead author. She said: “This latest AI development obviously brings huge challenges for universities, not least in testing student knowledge and teaching writing skills — but looking positively it is an opportunity for us to rethink what we want students to learn and why. I’d like to think that AI would enable us to automate some of the more administrative tasks academics do, allowing more time to be spent working with students”
Corresponding author Dr Peter Cotton, Associate Professor in Ecology at the University of Plymouth, added: “Banning ChatGPT, as was done within New York schools, can only be a short-term solution while we think how to address the issues. AI is already widely accessible to students outside their institutions, and companies like Microsoft and Google are rapidly incorporating it into search engines and Office suites. The chat (sic) is already out of the bag, and the challenge for universities will be to adapt to a paradigm where the use of AI is the expected norm.”
Dr Reuben Shipway, Lecturer in Marine Biology at the University of Plymouth, said: “With any new revolutionary technology — and this is a revolutionary technology — there will be winners and losers. The losers will be those that fail to adapt to a rapidly changing landscape. The winners will take a pragmatic approach and leverage this technology to their advantage.” More

Imagine a home computer operating 1 million times faster than the most expensive hardware on the market. Now, imagine that level of computing power as the industry standard. University of Arizona researchers hope to pave the way for that reality using light-based optical computing, a marked improvement from the semiconductor-based transistors that currently run the world.
“Semiconductor-based transistors are in all of the electronics that we use today,” said Mohammed Hassan, assistant professor of physics and optical sciences. “They’re part of every industry — from kids’ toys to rockets — and are the main building blocks of electronics.”
Hassan lad an international team of researchers that published the research article “Ultrafast optical switching and data encoding on synthesized light fields” in Science Advances in February. UArizona physics postdoctoral research associate Dandan Hui and physics graduate student Husain Alqattan also contributed to the article, in addition to researchers from Ohio State University and the Ludwig Maximilian University of Munich.
Semiconductors in electronics rely on electrical signals transmitted via microwaves to switch — either allow or prevent — the flow of electricity and data, represented as either “on” or “off.” Hassan said the future of electronics will be based instead on using laser light to control electrical signals, opening the door for the establishment of “optical transistors” and the development of ultrafast optical electronics.
Since the invention of semiconductor transistors in the 1940s, technological advancement has centered on increasing the speed at which electric signals can be generated — measured in hertz. According to Hassan, the fastest semiconductor transistors in the world can operate at a speed of more than 800 gigahertz. Data transfer at that frequency is measured at a scale of picoseconds, or one trillionth of a second.
Computer processing power has increased steadily since the introduction of the semiconductor transistor, though Hassan said one of the primary concerns in developing faster technology is that the heat generated by continuing to add transistors to a microchip would eventually require more energy to cool than can pass through the chip.
In their article, Hassan and his collaborators discuss using all-optical switching of a light signal on and off to reach data transfer speeds exceeding a petahertz, measured at the attosecond time scale. An attosecond is one quintillionth of a second, meaning the transfer of data 1 million times faster than the fastest semiconductor transistors.
While optical switches were already shown to achieve information processing speeds faster than that of semiconductor transistor-based technology, Hassan and his co-authors were able to register the on and off signals from a light source happening at the scale of billionths of a second. This was accomplished by taking advantage of a characteristic of fused silica, a glass often used in optics. Fused silica can instantaneously change its reflectivity, and by using ultrafast lasers, Hassan and his team were able to register changes in a light’s signal at the attosecond time scale. The work also demonstrated the possibility of sending data in the form of “one” and “zero” representing on and off via light at previously impossible speeds.
“This new advancement would also allow the encoding of data on ultrafast laser pulses, which would increase the data transfer speed and could be used in long-distance communications from Earth into deep space,” Hassan said. “This promises to increase the limiting speed of data processing and information encoding and open a new realm of information technology.”
The project was funded by a $1.4 million grant awarded to Hassan in 2018 by the Gordon and Betty Moore Foundation, an organization that aims “to create positive outcomes for future generations” by supporting research into scientific discovery, environmental conservation and patient care. The article was also based on work supported by the United States Air Force Office of Scientific Research’s Young Investigator Research Program. More

Researchers at North Carolina State University have demonstrated a caterpillar-like soft robot that can move forward, backward and dip under narrow spaces. The caterpillar-bot’s movement is driven by a novel pattern of silver nanowires that use heat to control the way the robot bends, allowing users to steer the robot in either direction.
“A caterpillar’s movement is controlled by local curvature of its body — its body curves differently when it pulls itself forward than it does when it pushes itself backward,” says Yong Zhu, corresponding author of a paper on the work and the Andrew A. Adams Distinguished Professor of Mechanical and Aerospace Engineering at NC State. “We’ve drawn inspiration from the caterpillar’s biomechanics to mimic that local curvature, and use nanowire heaters to control similar curvature and movement in the caterpillar-bot.
“Engineering soft robots that can move in two different directions is a significant challenge in soft robotics,” Zhu says. “The embedded nanowire heaters allow us to control the movement of the robot in two ways. We can control which sections of the robot bend by controlling the pattern of heating in the soft robot. And we can control the extent to which those sections bend by controlling the amount of heat being applied.”
The caterpillar-bot consists of two layers of polymer, which respond differently when exposed to heat. The bottom layer shrinks, or contracts, when exposed to heat. The top layer expands when exposed to heat. A pattern of silver nanowires is embedded in the expanding layer of polymer. The pattern includes multiple lead points where researchers can apply an electric current. The researchers can control which sections of the nanowire pattern heat up by applying an electric current to different lead points, and can control the amount of heat by applying more or less current.
“We demonstrated that the caterpillar-bot is capable of pulling itself forward and pushing itself backward,” says Shuang Wu, first author of the paper and a postdoctoral researcher at NC State. “In general, the more current we applied, the faster it would move in either direction. However, we found that there was an optimal cycle, which gave the polymer time to cool — effectively allowing the ‘muscle’ to relax before contracting again. If we tried to cycle the caterpillar-bot too quickly, the body did not have time to ‘relax’ before contracting again, which impaired its movement.”
The researchers also demonstrated that the caterpillar-bot’s movement could be controlled to the point where users were able steer it under a very low gap — similar to guiding the robot to slip under a door. In essence, the researchers could control both forward and backward motion as well as how high the robot bent upwards at any point in that process.
“This approach to driving motion in a soft robot is highly energy efficient, and we’re interested in exploring ways that we could make this process even more efficient,” Zhu says. “Additional next steps include integrating this approach to soft robot locomotion with sensors or other technologies for use in various applications — such as search-and-rescue devices.”
The work was done with support from the National Science Foundation, under grants 2122841, 2005374 and 2126072; and from the National Institutes of Health, under grant number 1R01HD108473. More