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Category: robotics/AI – Page 141

Digital twin reveals how eye cells lose their organization in leading cause of vision loss

National Institutes of Health (NIH) researchers have developed a digital replica of crucial eye cells, providing a new tool for studying how the cells organize themselves when they are healthy and affected by diseases. The platform opens a new door for therapeutic discovery for blinding diseases such as age-related macular degeneration (AMD), a leading cause of vision loss in people over 50. The study is published in the journal npj Artificial Intelligence.

“This work represents the first-ever subcellular resolution digital twin of a differentiated human primary cell, demonstrating how the eye is an ideal proving ground for developing methods that could be used more generally in biomedical research,” Kapil Bharti, Ph.D., scientific director at the NIH’s National Eye Institute (NEI).

The researchers created a highly detailed, 3D data-driven digital twin of retinal pigment epithelial (RPE) cells, which perform vital recycling and supportive roles to light-sensing photoreceptors in the retina. In diseases such as AMD, RPE cells die, which eventually leads to the death of photoreceptor cells, causing loss of vision.

Bio-inspired chip helps robots and self-driving cars react faster to movement

Robots and self-driving cars could soon benefit from a new kind of brain-inspired hardware that can allegedly detect movement and react faster than a human. A new study published in the journal Nature Communications details how an international team built their neuromorphic temporal-attention hardware system to speed up automated driving decisions.

The problem with current robotic vision and self-driving vehicles is a significant delay in processing what they see. While today’s top AI programs can recognize objects accurately, the calculations are so complex that they can take up to half a second to complete. That may not sound like a lot, but at highway speeds, even a one-second delay means a car travels 27 meters before it even begins to react. That is too long and too slow a reaction time.

To solve this problem, the team worked on a hardware solution rather than tinkering with software, modeling it on how human vision works. When we view a situation, our visual system doesn’t analyze every detail at once. It first detects changes in brightness and movement, then processes the more complex details later.

Ammonia leaks can be spotted in under two seconds using new alveoli-inspired droplet sensor

Researchers from Guangxi University, China have developed a new gas sensor that detects ammonia with a record speed of 1.4 seconds. The sensor’s design mimics the structure of alveoli—the tiny air sacs in human lungs—while relying on a triboelectric nanogenerator (TENG) that converts mechanical energy into electrical energy. The sensor uses a process that is driven by A-droplets, which are tiny water droplets containing a trapped air bubble. These droplets exploit ammonia’s affinity for water to rapidly capture NH₃ when it is present.

When an ammonia-laden droplet falls onto the sensor, its mechanical impact completes an electrical circuit, generating signals that are converted into accurate gas measurements at a speed that exceeds existing ammonia gas sensors.

To take detection precision a step further, the team integrated an AI model that analyzes electrical signals and converts them into time-frequency images. After training on these images, the system classified ammonia into five concentration levels (0–200 ppm), achieving up to 98.4% detection accuracy.

AI method accelerates liquid simulations by learning fundamental physical relationships

Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the chemical potential—an indispensable quantity for describing liquids in thermodynamic equilibrium. The researchers present their findings in a new study published in Physical Review Letters.

Many common AI methods are based on the principle of supervised machine learning: a model—for instance, a neural network—is specifically trained to predict a particular target quantity directly. One example that illustrates this approach is image recognition, where the AI system is shown numerous images in which it is known whether or not a cat is depicted. On this basis, the system learns to identify cats in new, previously unseen images.

“However, such a direct approach is difficult in the case of the chemical potential, because determining it usually requires computationally expensive algorithms,” says Prof. Dr. Matthias Schmidt, Chair of Theoretical Physics II at the University of Bayreuth. He and his research associate Dr. Florian Sammüller address this challenge with their newly developed AI method. It is based on a neural network that incorporates the theoretical structure of liquids—and more generally, of soft matter—allowing it to predict their properties with great accuracy.

APT36 and SideCopy Launch Cross-Platform RAT Campaigns Against Indian Entities

Indian defense sector and government-aligned organizations have been targeted by multiple campaigns that are designed to compromise Windows and Linux environments with remote access trojans capable of stealing sensitive data and ensuring continued access to infected machines.

The campaigns are characterized by the use of malware families like Geta RAT, Ares RAT, and DeskRAT, which are often attributed to Pakistan-aligned threat clusters tracked as SideCopy and APT36 (aka Transparent Tribe). SideCopy, active since at least 2019, is assessed to operate as a subdivision of Transparent Tribe.

“Taken together, these campaigns reinforce a familiar but evolving narrative,” Aditya K. Sood, vice president of Security Engineering and AI Strategy at Aryaka, said. “Transparent Tribe and SideCopy are not reinventing espionage – they are refining it.”

AI-guided micromachining advances next-generation biocompatible titanium alloys

Researchers have developed a new machine-learning-assisted approach to optimize micro-electro-discharge machining (µ-EDM) of a next-generation biocompatible titanium alloy, potentially improving the manufacturing of advanced medical and aerospace components.

The work is published in the journal Scientific Reports.

Titanium alloys are widely used in biomedical implants, aerospace systems, and automotive engineering due to their strength, corrosion resistance, and low weight. However, the commonly used alloy Ti–6Al–4V contains aluminum and vanadium, elements associated with long-term toxicity risks in biomedical applications.

AI and brain control: New system identifies animal behavior and silences responsible neurons in real time

A male fruit fly in a laboratory chamber extends his wings and vibrates them to produce his species’ version of a love song. A female fly stays nearby listening. Suddenly, a green light flashes across the chamber for a fraction of a second. The male’s song cuts off mid-note and his wings fold. The female, not impressed by the interrupted serenade, walks away. The culprit? An AI system that watched the male begin his courtship dance and shut down his song-producing brain cells.

Developed by scientists at Nagoya University and their collaborators from Osaka University and Tohoku University, the AI can watch and recognize animal behaviors and control the specific brain circuits that drive them.

Published in Science Advances, the study presents an advanced AI system that can identify which animal performs a behavior in a group and selectively target only that animal’s brain cells during social interactions.

Why the economics of orbital AI are so brutal

He’s not alone. xAI’s head of compute has reportedly bet his counterpart at Anthropic that 1% of global compute will be in orbit by 2028. Google (which has a significant ownership stake in SpaceX) has announced a space AI effort called Project Suncatcher, which will launch prototype vehicles in 2027. Starcloud, a startup that has raised $34 million backed by Google and Andreessen Horowitz, filed its own plans for an 80,000 satellite constellation last week. Even Jeff Bezos has said this is the future.

But behind the hype, what will it actually take to get data centers into space?

In a first analysis, today’s terrestrial data centers remain cheaper than those in orbit. Andrew McCalip, a space engineer, has built a helpful calculator comparing the two models. His baseline results show that a 1 GW orbital data center might cost $42.4 billion — almost 3x its ground-bound equivalent, thanks to the up-front costs of building the satellites and launching them to orbit.

JUST RECORDED: Elon Musk Announces MAJOR Company Shakeup

Elon Musk Announces MAJOR Company Changes as XAI/SpaceX ## Elon Musk is announcing significant changes and advancements across his companies, primarily focused on developing and integrating artificial intelligence (AI) to drive innovation, productivity, and growth ## ## Questions to inspire discussion.

Product Development & Market Position.

🚀 Q: How fast did xAI achieve market leadership compared to competitors?

A: xAI reached number one in voice, image, video generation, and forecasting with the Grok 4.20 model in just 2.5 years, outpacing competitors who are 5–20 years old with larger teams and more resources.

📱 Q: What scale did xAI’s everything app reach in one year?

A: In one year, xAI went from nothing to 2M Teslas using Grok, deployed a Grok voice agent API, and built an everything app handling legal questions, slide decks, and puzzles.

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