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Category: robotics/AI – Page 4

The influencers with millions of followers who don’t actually exist

Lil Miquela has 2.5 million Instagram followers, a high-fashion wardrobe, and a clear political voice. She has advocated for Black Lives Matter and the LGBTQI+ community, fronted major brand campaigns, and built a devoted global fanbase. She also has no pulse.

Lil Miquela is a virtual influencer, a computer-generated character designed to look, sound, and behave like a real person. And she is not alone.

In China, Liu Yexi blends traditional aesthetics with cyberpunk visuals to amass a huge following. Ling, created by Chinese AI startup Xmov, has promoted Tesla, Vogue, and luxury tea brand Nayuki.

Chip-scale light technology could power faster AI and data center communications

Researchers at Trinity have developed a new light-based technology on a tiny chip that could help make the data centers behind cloud computing, artificial intelligence, and global internet services faster and more efficient. In the new research, recently published in Nature Communications, the Trinity team reported one such promising advance with collaborators at the University of Bath and the Swiss Federal Institute of Technology Lausanne (EPFL).

The team developed a new way to generate extremely stable signals of light using microscopic ring-shaped devices called “microresonators.” These signals form what scientists call optical frequency combs, sometimes described as “optical rulers” because they produce a series of evenly spaced colors of light that can be used to measure light with remarkable precision.

The researchers also demonstrated a new type of light pulse called a “hyperparametric soliton.” This stable pulse is the key behind the major advancement in this work, as it allows the comb signals to be produced at different colors of light from the laser that powers the device.

How “mindreading” AI detects hidden suicidal thoughts in the brains of young adults

A recent study published in Human Brain Mapping provides evidence that young adults experiencing suicidal thoughts process concepts related to death differently in their brains compared to healthy individuals. The findings indicate that these individuals reflexively associate death-related ideas with their own sense of self. This research suggests that brain imaging combined with artificial intelligence could eventually help identify people at risk for suicide based on how their brains represent specific words.

If you or someone you know is experiencing suicidal thoughts or a mental health crisis, help is available. Call or text 988 to reach the free and confidential Suicide & Crisis Lifeline, or chat live at 988lifeline.org.

While mental health professionals typically rely on patients to report their feelings, people at risk for suicide do not always disclose their struggles. Finding an objective physical measurement in the brain could help identify those in need of support.

Long-term inflammatory memory driver identified!

The researchers first gave a bout of psoriasis to mice when they were young. They discovered that about 10–15% of the memories that persisted a month later stuck around even to the end of the mouse’s life (~2 years). To see why these long-term memories lingered while their short-term counterparts faded within six months, they analyzed the DNA sequence characteristics within each of the memories by using a deep learning model customized by the third co-first author.

“When we compared the DNA sequences of short and long-term memory domains, they looked very similar in terms of the numbers and kinds of transcription factor binding sites,” says the author. “We realized we needed to develop a new metric that specifically captures memory persistence across time, not just total accessibility at any one point.”

Soto-Ugaldi’s adaptation, called PersistNet, quickly identified a telling trait: The longest lasting memory domains had an unusually high frequency of CpG dinucleotides—short DNA sequences of cytosine followed by guanine, which are known to play a key role in gene regulation. In fact, the model predicted that CpG density hardwires a timer into every memory domain: The more CpG’s, the longer the memory.

When they tested the prediction, that’s exactly what they found. “Looking across all 1,000 memory domains, we discovered that these nucleotide densities alone, and no other DNA sequence pattern, could distinguish how long each memory would linger,” says the author.

Back in the lab, the team discovered that these genetically wired densities enabled a host of epigenetic changes in memory domains, including DNA demethylation (the removal of a methyl group specifically found on CpG dinucleotides); the binding of transcription factors that prefer demethylated states; and the recruitment of a histone variant called H2A.Z, which preferentially seeks out demethylated sites and boosts chromatin accessibility while staving off future re-methylation. Together, these changes stabilized the open chromatin formation and its gene-priming activity. As the authors discovered, this structure could crucially be passed down across cellular generations, essentially keeping the doors open for life. Science Mission sciencenewshighlights.

One of the most puzzling aspects of common chronic inflammatory skin diseases such as psoriasis is how they become chronic. What allows an ongoing condition to stay dormant for months or even years, then seemingly spring back out of nowhere?

Continue reading “Long-term inflammatory memory driver identified!” | >

Legged robot could accelerate resource prospecting on the moon and the search for life on Mars

Planetary surface missions currently operate cautiously. On Mars, communication delays between Earth and rovers (typically between four and 22 minutes), as well as data transfer constraints due to uplink and downlink limitations, force scientists to plan operations in advance. Rovers are designed for energy efficiency and safety, and to move slowly across hazardous terrain.

As a result, exploration is typically limited to only a small portion of the landing site, with rovers typically traveling up to a few hundreds of meters per day, which makes it difficult to collect geologically diverse data.

In a study published in Frontiers in Space Technologies, a team led by Dr. Gabriela Ligeza, former Ph.D. student from the University of Basel and now a postdoctoral researcher at the European Space Agency (ESA), tested a different approach: a semi-autonomous robotic explorer which can investigate multiple targets one-by-one and collect data without constant human intervention.

What’s inside a masterpiece? Laser scans and AI map paint layers molecule by molecule

Paintings are far more than dabs of oil on canvas. They are complex works of art composed of multiple layers, from primer and glues to the pigments and protective varnishes applied by the artists. Being able to see into these layers and map their chemical makeup is essential for art historians and conservators. A new technique developed by an international team of scientists can now probe paint layers in far greater molecular detail than before.

As they describe in a paper published in the journal Science Advances, the researchers combined a technique called MALDI-MSI (matrix-assisted laser desorption/ionization mass spectrometry imaging) with an AI named MSIpredictART to help identify the specific pigments and binders present in each layer of a painting.

Current approaches looking at the internal structure of a painting have to run several different tests on tiny samples. MALDI-MSI reduces the need for multiple separate techniques by using a high-resolution laser scan to map both the pigments and the binder or glue that holds them together.

Three-in-one diode integrates sensing, memory and processing for smart cameras

Think about how easily you recognize a friend in a dimly lit room. Your eyes capture light, while your brain filters out background noise, retrieves stored visual information, and processes the image to make a match. It all happens in a fraction of a second and uses remarkably little energy. Unfortunately, artificial vision systems in smartphones, cameras, and autonomous machines operate more like an assembly line. In our recent paper published in Nature Electronics, we describe how we addressed this challenge by enabling sensing, memory, and processing within the same device, pointing to a possible route toward more efficient machine vision.

The iGaN Laboratory led by Professor Haiding Sun at the School of Microelectronics, University of Science and Technology of China (USTC), in collaboration with multiple institutions, developed the multifunctional semiconductor diode with integrated photosensing, memory, and processing capabilities.

To understand the challenge, it helps to look at the basic building block of modern digital cameras: the semiconductor p-n diode. These tiny junctions act as the light-sensing pixels in imaging systems. However, a conventional diode is usually limited to a single function. It converts light into an electrical signal, and the captured data must then be transferred to separate memory and processing units. Moving this data back and forth consumes time, power, and chip area.

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