A new AI algorithm developed by researchers at the Finnish Center for Artificial Intelligence is aimed at visualizing datasets as clearly as possible. The project demonstrated that the solution chosen independently by the algorithm was often very close to that most commonly favored by humans. 

The human brain has an astounding ability to observe various traits even from extremely large quantities of visual information. This ability is utilized, for example, in the study of large data masses whose content must be compacted into a form understandable to human intelligence. This problem of dimensional reduction is central to visual analytics.

At the Finnish Center for Artificial Intelligence (FCAI), researchers affiliated with Aalto University and the University of Helsinki tested the functionality of the most well-known methods of visual analytics, finding that none worked when the amount of data grew significantly. For example, the t-SNE, LargeViz, and UMAP methods were no longer able to distinguish extremely strong signals of observational groupings in the data when the number of observations was in the hundreds of thousands. 

Higgs boson data inspired the creation of the new algorithm

The dataset for experiments related to the discovery of the Higgs boson contains more than 11 million feature vectors, for instance.

“The visualizations drawn from them resembled a tangle of yarn, revealing none of the notable characteristics of particle behavior included in the data”, says Professor of Statistics and Probability Jukka Corander from the University of Helsinki.

“This finding provided the impetus to develop a new method that utilizes graphical acceleration similarly to modern AI methods for neural network computing. “

The AI algorithm designed by the researchers is aimed at visualization, so that data clusters and other macroscopic features, easily observed by and understandable to humans, are as distinct as possible. 

In the project, several volunteers tested the technique. It turned out that the solution independently chosen by the algorithm was often very close to the solution most typically favored by humans; in this situation, human intelligence clearly distinguishes, according to personal notions, between clusters of data composed of similar observations. When applying the technique to the Higgs boson data, their most important physical characteristics were clearly highlighted. 

“This is a veritable quantum leap in the field of visual analytics. Besides being several orders of magnitude faster than previous methods, our technique also is much more reliable in connection with challenging applications,” says Professor of Computer Science Jukka Corander from the University of Helsinki.

Under the direction of Corander’s group, a separate interface was also designed for utilizing the technique as efficiently as possible in genomics applications. This way, users can even analyze their datasets interactively by uploading files directly into the web browser. Employing global bacterial and SARS-CoV-2 datasets, this further study illustrated how the new tool can be used to quickly examine as many as millions of genomes and identify relevant characteristics.

The study was a collaboration between the Director of FCAI, Professor Sami Kaski, and Jukka Corander’s groups. Professor Zhirong Yang from the Norwegian University of Science and Technology served as the project lead. Professor Yang has a doctoral degree from Aalto University and has subsequently worked as a researcher at both Aalto University and the University of Helsinki in Professor Corander’s group. 

A quick electric pulse completely flips the material’s electronic properties, opening a route to ultrafast, brain-inspired, superconducting electronics.

With some careful twisting and stacking, MIT physicists have revealed a new and exotic property in “magic-angle” graphene: superconductivity that can be turned on and off with an electric pulse, much like a light switch.

The discovery could lead to ultrafast, energy-efficient superconducting transistors for neuromorphic devices — electronics designed to operate in a way similar to the rapid on/off firing of neurons in the human brain.

Magic-angle graphene refers to a very particular stacking of graphene — an atom-thin material made from carbon atoms that are linked in a hexagonal pattern resembling chicken wire. When one sheet of graphene is stacked atop a second sheet at a precise “magic” angle, the twisted structure creates a slightly offset “moiré” pattern or superlattice, that can support a host of surprising electronic behaviors.

In 2018, Pablo Jarillo-Herrero and his group at MIT were the first to demonstrate magic-angle twisted bilayer graphene. They showed that the new bilayer structure could behave as an insulator, much like wood, when they applied a certain continuous electric field. When they upped the field, the insulator suddenly morphed into a superconductor, allowing electrons to flow, friction-free.

That discovery was a watershed in the field of “twistronics,” which explores how certain electronic properties emerge from the twisting and layering of two-dimensional materials. Researchers including Jarillo-Herrero have continued to reveal surprising properties in magic-angle graphene, including various ways to switch the material between different electronic states. So far, such “switches” have acted more like dimmers, in that researchers must continuously apply an electric or magnetic field to turn on superconductivity and keep it on.

Now Jarillo-Herrero and his team have shown that superconductivity in magic-angle graphene can be switched on, and kept on, with just a short pulse rather than a continuous electric field. The key, they found, was a combination of twisting and stacking.

The team reports that by stacking magic-angle graphene between two offset layers of boron nitride — a two-dimensional insulating material — the unique alignment of the sandwich structure enabled the researchers to turn graphene’s superconductivity on and off with a short electric pulse.

“For the vast majority of materials, if you remove the electric field, zzzzip, the electric state is gone,” says Jarillo-Herrero, who is the Cecil and Ida Green Professor of Physics at MIT. “This is the first time that a superconducting material has been made that can be electrically switched on and off, abruptly. This could pave the way for a new generation of twisted, graphene-based superconducting electronics.”

His MIT co-writers are lead researcher Dahlia Klein Ph.D. ’21, graduate student Li-Qiao Xia, and former postdoc David MacNeill, along with Kenji Watanabe and Takashi Taniguchi of the National Institute for Materials Science in Japan.

Flipping the switch

In 2019, a team at Stanford University discovered that magic-angle graphene could be coerced into a ferromagnetic state. Ferromagnets are materials that retain their magnetic properties, even in the absence of an externally applied magnetic field.

The researchers found that magic-angle graphene could exhibit ferromagnetic properties in a way that could be turned on and off. This happened when the graphene sheets were layered between two sheets of boron nitride such that the crystal structure of the graphene was aligned to one of the boron nitride layers. The arrangement resembled a cheese sandwich in which the top slice of bread and the cheese orientations are aligned, but the bottom slice of bread is rotated at a random angle with respect to the top slice. The result intrigued the MIT group.

“We were trying to get a stronger magnet by aligning both slices,” Jarillo-Herrero says. “Instead, we found something completely different.”

In their current study, the team fabricated a sandwich of carefully angled and stacked materials. The “cheese” of the sandwich consisted of magic-angle graphene — two graphene sheets, the top rotated slightly at the “magic” angle of 1.1 degrees with respect to the bottom sheet. Above this structure, they placed a layer of boron nitride, exactly aligned with the top graphene sheet. Finally, they placed a second layer of boron nitride below the entire structure and offset it by 30 degrees with respect to the top layer of boron nitride.

The team then measured the electrical resistance of the graphene layers as they applied a gate voltage. They found as others have, that the twisted bilayer graphene switched electronic states, changing between insulating, conducting, and superconducting states at certain known voltages.

What the group did not expect was that each electronic state persisted rather than immediately disappearing once the voltage was removed — a property known as bistability. They found that, at a particular voltage, the graphene layers turned into a superconductor, and remained superconducting, even as the researchers removed this voltage.  

This bistable effect suggests that superconductivity can be turned on and off with short electric pulses rather than a continuous electric field, similar to flicking a light switch. It isn’t clear what enables this switchable superconductivity, though the researchers suspect it has something to do with the special alignment of the twisted graphene to both boron nitride layers, which enables a ferroelectric-like response of the system. (Ferroelectric materials display bistability in their electric properties.)

“By paying attention to the stacking, you could add another tuning knob to the growing complexity of magic-angle, superconducting devices,” Klein says. 

For now, the team sees the new superconducting switch as another tool researchers can consider as they develop materials for faster, smaller, more energy-efficient electronics.

“People are trying to build electronic devices that do calculations in a way that’s inspired by the brain,” Jarillo-Herrero says. “In the brain, we have neurons that, beyond a certain threshold, they fire. Similarly, we now have found a way for magic-angle graphene to switch superconductivity abruptly, beyond a certain threshold. This is a key property in realizing neuromorphic computing.”  

This research was supported in part by the U.S. Air Force Office of Scientific Research, the U.S. Army Research Office, and the Gordon and Betty Moore Foundation.

When pondering the probability of discovering technologically advanced extraterrestrial life, the question that often arises is, "if they're out there, why haven't we found them yet?" And often, the response is that we have only searched a tiny portion of the galaxy. Further, algorithms developed decades ago for the earliest digital computers can be outdated and inefficient when applied to modern petabyte-scale datasets. Now, research led by an undergraduate student at the University of Toronto, Peter Ma, along with researchers from the SETI Institute, Breakthrough Listen, and scientific research institutions around the world, has applied a deep learning technique to a previously studied dataset of nearby stars and uncovered eight previously unidentified signals of interest.

“In total, we had searched through 150 TB of data of 820 nearby stars, on a dataset that had previously been searched through in 2017 by classical techniques but labeled as devoid of interesting signals," said Peter Ma, lead author. “We're scaling this search effort to 1 million stars today with the MeerKAT telescope and beyond. We believe that work like this will help accelerate the rate we’re able to make discoveries in our grand effort to answer the question ‘are we alone in the universe?’”

The search for extraterrestrial intelligence (SETI) looks for evidence of extraterrestrial intelligence originating beyond Earth by trying to detect technosignatures, or evidence of technology, that alien civilizations could have developed. The most common technique is to search for radio signals. Radio is a great way to send information over the incredible distances between the stars; it quickly passes through the dust and gas that permeate space, and it does so at the speed of light (about 20,000 times faster than our best rockets). Many SETI efforts use antennas to eavesdrop on any radio signals aliens might be transmitting.

This study re-examined data taken with the Green Bank Telescope in West Virginia as part of a Breakthrough Listen campaign that initially indicated no targets of interest. The goal was to apply new deep learning techniques to a classical search algorithm to yield faster, more accurate results. After running the new algorithm and manually re-examining the data to confirm the results, newly detected signals had several key characteristics:

1. The signals were narrow band, meaning they had narrow spectral width, on the order of just a few Hz. Signals caused by natural phenomena tend to be broadband.
2. The signals had non-zero drift rates, which means the signals had a slope. Such slopes could indicate a signal’s origin had some relative acceleration with our receivers, hence not local to the radio observatory.
3. The signals appeared in ON-source observations and not in OFF-source observations. If a signal originates from a specific celestial source, it appears when we point our telescope toward the target and disappears when we look away. Human radio interference usually occurs in ON and OFF observations due to the source being close by.

Cherry Ng, another of Ma’s research advisors and an astronomer at both the SETI Institute and the French National Center for Scientific Research said, “These results dramatically illustrate the power of applying modern machine learning and computer vision methods to data challenges in astronomy, resulting in both new detections and higher performance. Application of these techniques at scale will be transformational for radio techno signature science.”

While re-examinations of these new targets of interest have yet to result in re-detections of these signals, this new approach to analyzing data can enable researchers to more effectively understand the data they collect and act quickly to re-examine targets.  Ma and his advisor Dr. Cherry Ng are looking forward to deploying extensions of this algorithm on the SETI Institute’s COSMIC system.

Since SETI experiments began in 1960 with Frank Drake’s Project Ozma at the Greenbank Observatory, a site now home to the telescope used in this latest work, technological advances have enabled researchers to collect more data than ever. This massive volume of data requires new computational tools to process and analyze that data quickly to identify anomalies that could be evidence of extraterrestrial intelligence. This new machine-learning approach is breaking new ground in the quest to answer the question, “are we alone?”

Intel has reported terrible fourth-quarter sales, and the year-over-year (YoY) comparisons are just as painful.

Fourth-quarter revenue was $14 billion, down 32% YoY, and $63.1 billion for the full year 2022, down 20% YoY. 

Its data center and AI (DCAI) sales of $4.3 billion have plunged by 33% in the quarter. 

The company's net income was painful; down 114% in the quarter and down 60% to $8 billion for the year. 

As a result, Intel shares closed 6.4% lower today. It saw $8 billion wiped off its market value after it baffled Wall Street with dismal earnings projections. They have predicted a surprise loss for the first quarter, and its revenue forecast was $3 billion below estimates as it struggled with growing the data center business.

Intel previously announced several organizational changes to accelerate its execution and innovation by allowing it to capture growth in both large traditional markets and high-growth emerging markets. This includes the reorganization of Intel's business units to capture this growth and provide increased transparency, focus, and accountability. As a result, the company modified its segment reporting in the first quarter of 2022 to align with the previously announced business reorganization. All prior-period segment data has been retrospectively adjusted to reflect the way the company internally manages and monitors operating segment performance starting in the fiscal year 2022.

“Despite the economic and market headwinds, we continued to make good progress on our strategic transformation in Q4, including advancing our product roadmap and improving our operational structure and processes to drive efficiencies while delivering at the low end of our guided range,” said Pat Gelsinger, Intel CEO. “In 2023, we will continue to navigate the short-term challenges while striving to meet our long-term commitments, including delivering leadership products anchored on open and secure platforms, powered by at-scale manufacturing and supercharged by our incredible team.” 

“In the fourth quarter, we took steps to right-size the organization and rationalize our investments, prioritizing the areas where we can deliver the highest value for the long term,” said David Zinsner, Intel CFO. “These actions underpin our cost-reduction targets of $3 billion in 2023, and set the stage to achieve $8 billion to $10 billion by the end of 2025.”