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Category: robotics/AI – Page 417

Unlocking the timecode of the Dead Sea Scrolls

Since their discovery, the historically and biblically hugely important Dead Sea Scrolls have transformed our understanding of Jewish and Christian origins. However, while the general date of the scrolls is from the third century BCE until the second century CE, individual manuscripts thus far could not be securely dated.

Now, by combining radiocarbon dating, paleography, and , an international team of researchers led by the University of Groningen has developed a date-prediction model, called Enoch, that provides much more accurate date estimates for individual manuscripts on empirical grounds.

Using this model, the researchers demonstrate that many Dead Sea Scrolls are older than previously thought. And for the first time, they establish that two biblical scroll fragments come from the time of their presumed biblical authors. They present their results in the journal PLOS One.

Chain-of-Zoom framework enables extreme super-resolution zoom without retraining

A trio of AI researchers at KAIST AI, in Korea, has developed what they call a Chain-of-Zoom framework that allows the generation of extreme super-resolution imagery using existing super-resolution models without the need for retraining.

In their study published on the arXiv preprint server, Bryan Sangwoo Kim, Jeongsol Kim, and Jong Chul Ye broke down the process of zooming in on an image and then used an existing super-resolution model at each step to refine the image, resulting in incremental improvements in resolution.

The team in Korea began by noting that existing frameworks for improving the resolution of pictures tend to use interpolation or regression when zooming, resulting in blurry imagery. To overcome these problems, they took a new approach—using a stepwise zooming process, in which subsequent steps improve on those that came before.

Diagnostic pen converts handwriting into electrical signals to detect Parkinson’s

A team at the University of California, Los Angeles has developed a low-cost diagnostic pen that converts handwriting into electrical signals for early detection of Parkinson’s disease, achieving 96.22% accuracy in a pilot study.

Parkinson’s disease impairs the , leading to tremors, stiffness, and slowed movements that impair fine motor functions such as . Clinical diagnosis today largely relies on subjective observations, which are prone to inconsistency and often inaccessible in . Biomarker-based diagnostics, while objective, remain constrained by cost and technical complexity.

In the study, “Neural network-assisted personalized handwriting analysis for Parkinson’s disease diagnostics,” published in Nature Chemical Engineering, researchers engineered a diagnostic pen to capture real-time motor signals during handwriting and convert them into quantifiable electrical outputs for disease classification.

If AI Gave You All The Answers, How Would You Change The World?

What if accessing knowledge, which used to require hours of analyzing handwritten scrolls or books, could be done in mere moments?

Throughout history, the way humans acquire knowledge has experienced great revolutions. The birth of writing and books altered learning, allowing ideas to be preserved and shared across generations. Then came the Internet, connecting billions of people to vast information at their fingertips.

Today, we stand at another shift: the age of AI tools, where AI doesn’t just give us answers—it provides reliable, tailored responses in seconds. We no longer need to gather and evaluate the correct information for our problems. If knowledge is now a tool everyone can hold, the real revolution starts when we use this superpower to solve problems and improve the world.

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