Aurelia Butler

For artificial intelligence to get any smarter, it needs first to be as intelligent as one of the simplest creatures in the animal kingdom: the sea slug.
A new study has found that a material can mimic the sea slug’s most essential intelligence features. The discovery is a step toward building hardware that could help make AI more efficient and reliable for technology ranging from self-driving cars and surgical robots to social media algorithms.
The study, publishing this week in the Proceedings of the National Academy of Sciences, was conducted by a team of researchers from Purdue University, Rutgers University, the University of Georgia and Argonne National Laboratory.
“Through studying sea slugs, neuroscientists discovered the hallmarks of intelligence that are fundamental to any organism’s survival,” said Shriram Ramanathan, a Purdue professor of materials engineering. “We want to take advantage of that mature intelligence in animals to accelerate the development of AI.”
Two main signs of intelligence that neuroscientists have learned from sea slugs are habituation and sensitization. Habituation is getting used to a stimulus over time, such as tuning out noises when driving the same route to work every day. Sensitization is the opposite — it’s reacting strongly to a new stimulus, like avoiding bad food from a restaurant.
AI has a really hard time learning and storing new information without overwriting information it has already learned and stored, a problem that researchers studying brain-inspired computing call the “stability-plasticity dilemma.” Habituation would allow AI to “forget” unneeded information (achieving more stability) while sensitization could help with retaining new and important information (enabling plasticity). More

By confining the transport of electrons and ions in a patterned thin film, scientists find a way to potentially enhance material properties for design of next-generation electronics
Like ripples in a pond, electrons travel like waves through materials, and when they collide and interact, they can give rise to new and interesting patterns.
Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have seen a new kind of wave pattern emerge in a thin film of metal oxide known as titania when its shape is confined. Confinement, the act of restricting materials within a boundary, can alter the properties of a material and the movement of molecules through it.
In the case of titania, it caused electrons to interfere with each other in a unique pattern, which increased the oxide’s conductivity, or the degree to which it conducts electricity. This all happened at the mesoscale, a scale where scientists can see both quantum effects and the movement of electrons and molecules.
In all, this work offers scientists more insight about how atoms, electrons and other particles behave at the quantum level. Such information could aid in designing new materials that can process information and be useful in other electronic applications.
“What really set this work apart was the size of the scale we investigated,” said lead author Frank Barrows, a Northwestern University graduate student in Argonne’s Materials Science Division (MSD). “Investigating at this unique length scale enabled us to see really interesting phenomena that indicate there is interference happening at the quantum level, and at the same time gain new information about how electrons and ions interact.”
Altering geometry to change material properties More

Chatting with a robot is now part of many families’ daily lives, thanks to conversational agents such as Apple’s Siri or Amazon’s Alexa. Recent research has shown that children are often delighted to find that they can ask Alexa to play their favorite songs or call Grandma.
But does hanging out with Alexa or Siri affect the way children communicate with their fellow humans? Probably not, according to a recent study led by the University of Washington that found that children are sensitive to context when it comes to these conversations.
The team had a conversational agent teach 22 children between the ages of 5 and 10 to use the word “bungo” to ask it to speak more quickly. The children readily used the word when a robot slowed down its speech. While most children did use bungo in conversations with their parents, it became a source of play or an inside joke about acting like a robot. But when a researcher spoke slowly to the children, the kids rarely used bungo, and often patiently waited for the researcher to finish talking before responding.
The researchers published their findings in June at the 2021 Interaction Design and Children conference.
“We were curious to know whether kids were picking up conversational habits from their everyday interactions with Alexa and other agents,” said senior author Alexis Hiniker, a UW assistant professor in the Information School. “A lot of the existing research looks at agents designed to teach a particular skill, like math. That’s somewhat different from the habits a child might incidentally acquire by chatting with one of these things.”
The researchers recruited 22 families from the Seattle area to participate in a five-part study. This project took place before the COVID-19 pandemic, so each child visited a lab with one parent and one researcher. For the first part of the study, children spoke to a simple animated robot or cactus on a tablet screen that also displayed the text of the conversation. More

The next generation of computing and information processing lies in the intriguing world of quantum mechanics. Quantum computers are expected to be capable of solving large, extremely complex problems that are beyond the capacity of today’s most powerful supercomputers.
New research tools are needed to advance the field and fully develop quantum computers. Now Northwestern University researchers have developed and tested a theoretical tool for analyzing large superconducting circuits. These circuits use superconducting quantum bits, or qubits, the smallest units of a quantum computer, to store information.
Circuit size is important since protection from detrimental noise tends to come at the cost of increased circuit complexity. Currently there are few tools that tackle the modeling of large circuits, making the Northwestern method an important contribution to the research community.
“Our framework is inspired by methods originally developed for the study of electrons in crystals and allows us to obtain quantitative predictions for circuits that were previously hard or impossible to access,” said Daniel Weiss, corresponding and first author of the paper. He is a fourth-year graduate student in the research group of Jens Koch, an expert in superconducting qubits.
Koch, an associate professor of physics and astronomy in Weinberg College of Arts and Sciences, is a member of the Superconducting Quantum Materials and Systems Center (SQMS) and the Co-design Center for Quantum Advantage (C2QA). Both national centers were established last yearby the U.S. Department of Energy (DOE). SQMSis focused on building and deploying a beyond-state-of-the-art quantum computer based on superconducting technologies. C2QA is building the fundamental tools necessary to create scalable, distributed and fault-tolerant quantum computer systems.
“We are excited to contribute to the missions pursued by these two DOE centers and to add to Northwestern’s visibility in the field of quantum information science,” Koch said.
In their study, the Northwestern researchers illustrate the use of their theoretical tool by extracting from a protected circuit quantitative information that was unobtainable using standard techniques.
Details were published today (Sept. 13) in the open access journal Physical Review Research.
The researchers specifically studied protected qubits. These qubits are protected from detrimental noise by designand could yield coherence times (how long quantum information is retained) that are much longer than current state-of-the-art qubits.
These superconducting circuits are necessarily large, and the Northwestern tool is a means for quantifying the behavior of these circuits. There are some existing tools that can analyze large superconducting circuits, but each works well only when certain conditions are met. The Northwestern method is complementary and works well when these other tools may give suboptimal results.
Story Source:
Materials provided by Northwestern University. Original written by Megan Fellman. Note: Content may be edited for style and length. More

A 2D nanomaterial consisting of organic molecules linked to metal atoms in a specific atomic-scale geometry shows non-trivial electronic and magnetic properties due to strong interactions between its electrons.
A new study, published today, shows the emergence of magnetism in a 2D organic material due to strong electron-electron interactions; these interactions are the direct consequence of the material’s unique, star-like atomic-scale structure.
This is the first observation of local magnetic moments emerging from interactions between electrons in an atomically thin 2D organic material.
The findings have potential for applications in next-generation electronics based on organic nanomaterials, where tuning of interactions between electrons can lead to a vast range of electronic and magnetic phases and properties.
STRONG ELECTRON-ELECTRON INTERACTIONS IN A 2D ORGANIC KAGOME MATERIAL
The Monash University study investigated a 2D metal-organic nanomaterial composed of organic molecules arranged in a kagome geometry, that is, following a ‘star-like’ pattern. More

DTU and Graphene Flagship researchers have taken the art of patterning nanomaterials to the next level. Precise patterning of 2D materials is a route to computation and storage using 2D materials, which can deliver better performance and much lower power consumption than today’s technology.
One of the most significant recent discoveries within physics and material technology is two-dimensional materials such as graphene. Graphene is stronger, smoother, lighter, and better at conducting heat and electricity than any other known material.
Their most unique feature is perhaps their programmability. By creating delicate patterns in these materials, we can change their properties dramatically and possibly make precisely what we need.
At DTU, scientists have worked on improving state of the art for more than a decade in patterning 2D materials, using sophisticated lithography machines in the 1500 m2 cleanroom facility. Their work is based in DTU’s Center for Nanostructured Graphene, supported by the Danish National Research Foundation and a part of The Graphene Flagship.
The electron beam lithography system in DTU Nanolab can write details down to 10 nanometers. Computer calculations can predict exactly the shape and size of patterns in the graphene to create new types of electronics. They can exploit the charge of the electron and quantum properties such as spin or valley degrees of freedom, leading to high-speed calculations with far less power consumption. These calculations, however, ask for higher resolution than even the best lithography systems can deliver: atomic resolution.
“If we really want to unlock the treasure chest for future quantum electronics, we need to go below 10 nanometers and approach the atomic scale,” says professor and group leader at DTU Physics, Peter Bøggild. More

Every piece of data that travels over the internet — from paragraphs in an email to 3D graphics in a virtual reality environment — can be altered by the noise it encounters along the way, such as electromagnetic interference from a microwave or Bluetooth device. The data are coded so that when they arrive at their destination, a decoding algorithm can undo the negative effects of that noise and retrieve the original data.
Since the 1950s, most error-correcting codes and decoding algorithms have been designed together. Each code had a structure that corresponded with a particular, highly complex decoding algorithm, which often required the use of dedicated hardware.
Researchers at MIT, Boston University, and Maynooth University in Ireland have now created the first silicon chip that is able to decode any code, regardless of its structure, with maximum accuracy, using a universal decoding algorithm called Guessing Random Additive Noise Decoding (GRAND). By eliminating the need for multiple, computationally complex decoders, GRAND enables increased efficiency that could have applications in augmented and virtual reality, gaming, 5G networks, and connected devices that rely on processing a high volume of data with minimal delay.
The research at MIT is led by Muriel Médard, the Cecil H. and Ida Green Professor in the Department of Electrical Engineering and Computer Science, and was co-authored by Amit Solomon and Wei Ann, both graduate students at MIT; Rabia Tugce Yazicigil, assistant professor of electrical and computer engineering at Boston University; Arslan Riaz and Vaibhav Bansal, both graduate students at Boston University; Ken R. Duffy, director of the Hamilton Institute at the National University of Ireland at Maynooth; and Kevin Galligan, a Maynooth graduate student. The research will be presented at the European Solid-States Device Research and Circuits Conference next week.
Focus on noise
One way to think of these codes is as redundant hashes (in this case, a series of 1s and 0s) added to the end of the original data. The rules for the creation of that hash are stored in a specific codebook. More