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“A New Periodic Table of Machine Learning: Unlocking AI Discovery and Innovation”

After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a ‘periodic table of machine learning’ that can help scientists combine elements of different methods to improve algorithms or create new ones.

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MIT researchers have created a groundbreaking periodic table that reveals how more than 20 classical machine-learning algorithms are connected. This innovative framework sheds light on how scientists can fuse strategies from different methods to improve existing AI models or come up with new ones.

The researchers used their framework to combine elements of two different algorithms to create a new image-classification algorithm that performed 8 percent better than current state-of-the-art approaches. This breakthrough demonstrates the potential of the periodic table to unlock AI discovery and innovation.

The periodic table stems from one key idea: All these algorithms learn a specific kind of relationship between data points. While each algorithm may accomplish that in a slightly different way, the core mathematics behind each approach is the same. Building on these insights, the researchers identified a unifying equation that underlies many classical AI algorithms.

They used this equation to reframe popular methods and arrange them into a table, categorizing each based on the approximate relationships it learns. Just like the periodic table of chemical elements, which initially contained blank squares that were later filled in by scientists, the periodic table of machine learning also has empty spaces.

These spaces predict where algorithms should exist, but which haven’t been discovered yet. The researchers filled one gap by borrowing ideas from a machine-learning technique called contrastive learning and applying them to image clustering. This resulted in a new algorithm that could classify unlabeled images 8 percent better than another state-of-the-art approach.

The flexible periodic table allows researchers to add new rows and columns to represent additional types of datapoint connections. Ultimately, having I-Con as a guide could help machine learning scientists think outside the box, encouraging them to combine ideas in ways they wouldn’t necessarily have thought of otherwise.

This research was funded, in part, by the Air Force Artificial Intelligence Accelerator, the National Science Foundation AI Institute for Artificial Intelligence and Fundamental Interactions, and Quanta Computer. The researchers’ work will be presented at the International Conference on Learning Representations.

Artificial Intelligence

The Hidden Barrier to Advanced Robotic Touch

Researchers argue that the problem that has been lurking in the margins of many papers about touch sensors lies in the robotic skin itself.

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The development of advanced robotic touch has been hindered by a seemingly innocuous yet critical issue – the insulating layer in robotic skin. Researchers at Northwestern University and Tel Aviv University have successfully overcome this barrier, paving the way for low-cost solutions that enable robots to mimic human touch.

In their study, the researchers observed that inexpensive silicon rubber composites used to make robotic skin host an insulating layer on both top and bottom surfaces. This prevents direct electrical contact between the sensing polymer and the monitoring surface electrodes, making accurate and repeatable measurements impossible. By eliminating this error, cheap robotic skins can now allow robots to sense an object’s curves and edges, essential for proper grasping.

The research team, consisting of electrical engineers and polymer materials scientists, shed light on this problem in a paper published in Advanced Electronic Materials. The study highlights the importance of validating electrical contacts, which might unknowingly obscure device performance.

“A lot of scientists misunderstand their sensor response because they lump together the behavior of the contacts with the behavior of the sensor material, resulting in inconsistent data,” said Matthew Grayson, professor of electrical and computer engineering at Northwestern’s McCormick School of Engineering. “Our work identifies the exact problem, quantifies its extent both microscopically and electrically, and gives a clear step-by-step trouble-shooting manual to fix the problem.”

The researchers detected that adding electrically conducting fillers like carbon nanotubes to rubber composites creates an ideal candidate for touch sensors. However, this material needs electrical signals, which are blocked by the insulating layer. By sanding down the ultrathin insulation layer, the team achieved a stronger electrical contact and calibrated the thickness of the insulating layer.

The collaboration between Northwestern University and Tel Aviv University is essential in addressing the “contact preparation” challenge. The researchers relied on each other’s expertise to prepare materials and study their properties, leading to consistent results across various variables.

As awareness spreads among researchers about the issue of reproducibility in touch sensing literature, new publications can be more rigorously relied upon to advance the field with new capabilities. The research was supported by various organizations, including the U.S. National Science Foundation, Northwestern University, and Tel Aviv University through the Center for Nanoscience & Nanotechnology.

The breakthrough has significant implications for robotics development, enabling robots to sense and interact with their environment more effectively. By overcoming this critical barrier, researchers have opened up new possibilities for advanced robotic touch, paving the way for future innovations in robotics and beyond.

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Aerospace

“Challenging the Classics: Researchers Reveal New Insights into Material Deformation under Stress”

Scientists have expanded on a longstanding model governing the mechanics behind slip banding, a process that produces strain marks in metals under compression, gaining a new understanding of the behavior of advanced materials critical to energy systems, space exploration and nuclear applications.

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Researchers at the University of California, Irvine (UCI) have made a groundbreaking discovery in the field of materials science. By expanding on a classic model developed over 70 years ago, scientists in UCI’s Samueli School of Engineering have gained new insights into the behavior of advanced materials critical to energy systems, space exploration, and nuclear applications.

The traditional Frank-Read theory attributed slip band formation to continuous dislocation multiplication at active sources. However, the UC Irvine team found that extended slip bands emerge from source deactivation followed by the dynamic activation of new dislocation sources. This process was observed at the atomic scale through mechanical compression on micropillars made of a chromium-cobalt-nickel alloy.

Using advanced microscopy techniques and large-scale atomistic modeling, researchers were able to visualize the confined slip band as a thin glide zone with minimal defects and the extended slip band with a high density of planar defects. This understanding has shed new light on collective dislocation motion and microscopic deformation instability in advanced structural materials.

Deformation banding, where strain concentrates in local zones, is a common phenomenon in various substances and systems, including crystalline solids, metals, granular media, and even geologic faults under compressive stress. The discovery of extended slip bands challenges the classic model developed by physicists Charles Frank and Thornton Read in the 1950s.

“This foundational knowledge will accelerate the discovery of materials with tailored and predictable mechanical properties to meet the rising demand for advanced materials resilient to extreme environments across energy and aerospace sectors,” said corresponding author Penghui Cao, UC Irvine associate professor of mechanical and aerospace engineering.

The research was funded by the U.S. Department of Energy, UC Irvine, and the National Science Foundation (through the UC Irvine Center for Complex and Active Materials). The project involved graduate students, research specialists, and other professors from UCI’s Department of Mechanical and Aerospace Engineering and Department of Materials Science and Engineering.

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Biochemistry

Unveiling the Mystery of Crystals: Scientists Discover a New Type and Shed Light on Their Formation

Crystals — from sugar and table salt to snowflakes and diamonds — don’t always grow in a straightforward way. Researchers have now captured this journey from amorphous blob to orderly structures. In exploring how crystals form, the researchers also came across an unusual, rod-shaped crystal that hadn’t been identified before, naming it ‘Zangenite’ for the graduate student who discovered it.

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Unveiling the Mystery of Crystals: Scientists Discover a New Type and Shed Light on Their Formation

Crystals have long been a subject of fascination, from the intricate beauty of snowflakes to the durability of diamonds. However, their growth process has remained somewhat mysterious, with scientists once thinking that they always formed in a straightforward way. A new study published in Nature Communications has shed light on this process and led to an unexpected discovery – a new type of crystal.

Researchers at New York University (NYU) have been exploring how crystals form through experiments and computer simulations. They used colloidal particles, tiny spheres much larger than atoms, to observe the crystallization process at a single-particle level. This allowed them to study the formation of crystals in a way that was previously difficult or impossible.

“The advantage of studying colloidal particles is that we can observe crystallization processes at a single-particle level,” said Stefano Sacanna, professor of chemistry at NYU. “With colloids, we can watch crystals form with our microscope.”

The researchers conducted experiments to carefully observe how charged colloidal particles behave in different growth conditions as they transition from salt water suspensions to fully formed crystals. They also ran thousands of computer simulations led by Glen Hocky, assistant professor of chemistry at NYU, to model how crystals grow and help explain what they observed.

The team determined that colloidal crystals form through a two-step process: amorphous blobs of particles first condense before transforming into ordered crystal structures. This process resulted in a diverse array of crystal types and shapes.

During these experiments, PhD student Shihao Zang came across a rod-shaped crystal that he couldn’t identify. Despite comparing it to more than a thousand crystals found in the natural world, he still couldn’t find a match. However, through computer modeling, the researchers simulated a crystal that was exactly the same, enabling them to study its elongated, hollow shape in even greater detail.

The newly discovered crystal, named Zangenite after the PhD student who discovered it, has hollow channels running along its length. This unique structure creates an opportunity to explore uses for low-density crystals and may pave the way for finding additional new crystals.

“We study colloidal crystals to mimic the real world of atomic crystals, but we never imagined that we would discover a crystal that we cannot find in the real world,” said Zang.

The discovery of Zangenite has significant implications for the development of new materials, including photonic bandgap materials. These materials are foundational for lasers, fiber-optic cables, solar panels, and other technologies that transmit or harvest light.

The study’s authors include Sanjib Paul, Cheuk Leung, Michael Chen, and Theodore Hueckel. The research was supported by the US Army Research Office, the Simons Center for Computational Physical Chemistry at NYU, and the National Institutes of Health.

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