AI-driven deepfakes, bots, and synthetic identities overwhelm legacy defenses, making identity the key to stopping breaches.
Category: robotics/AI – Page 87
Finding the shadows in a fusion system faster with AI
A public‑private partnership between Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and Oak Ridge National Laboratory has led to a new artificial intelligence (AI) approach that is faster at finding what’s known as “magnetic shadows” in a fusion vessel: safe havens protected from the intense heat of the plasma.
Known as HEAT‑ML, the new AI could lay the foundation for software that significantly speeds up the design of future fusion systems. Such software could also enable good decision‑making during fusion operations by adjusting the plasma so that potential problems are thwarted before they start.
“This research shows that you can take an existing code and create an AI surrogate that will speed up your ability to get useful answers, and it opens up interesting avenues in terms of control and scenario planning,” said Michael Churchill, co‑author of a paper in Fusion Engineering and Design about HEAT‑ML and head of digital engineering at PPPL.
“They’re Building Data Fortresses For AI”: EU Unveils $30 Billion Plan For Gigawatt Centers Housing 100,000 GPUs Each To Rival US And China
IN A NUTSHELL 💡 The European Union plans to invest $30 billion to establish a network of high-capacity AI data centers. 🌍 This initiative aims to enhance the EU’s global standing in the artificial intelligence market. ⚙️ The project involves the development of gigawatt-scale data centers to support millions of AI GPUs. 🔌 Challenges include
Brain cells learn faster than machine learning, research reveals
Researchers have demonstrated that brain cells learn faster and carry out complex networking more effectively than machine learning by comparing how both a Synthetic Biological Intelligence (SBI) system known as “DishBrain” and state-of-the-art RL (reinforcement learning) algorithms react to certain stimuli.
The study, “Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning,” published in Cyborg and Bionic Systems, is the first known of its kind.
The research was led by Cortical Labs, the Melbourne-based startup which created the world’s first commercial biological computer, the CL1. The CL1, through which the research was conducted, fuses lab-cultivated neurons from human stem cells with hard silicon to create a more advanced and sustainable form of AI, known as SBI.
AI-Engineered Hydrogels Achieve Instant and Powerful Underwater Adhesion
Underwater adhesives have long posed a challenge to materials scientists, with few solutions capable of delivering instant, strong, and repeatable adhesion in challenging marine and biomedical environments. Now, a team of researchers has leveraged machine learning (ML) and data mining (DM) —inspired by natural adhesive proteins—to engineer next-generation super-adhesive hydrogels that work instantly underwater.
Published in Nature, the study introduces an end-to-end data-driven framework that starts with protein sequence extraction and ends with a scalable hydrogel synthesis method. The results are materials that can seal high-pressure leaks, attach securely to rough, wet surfaces, and even function in living tissue.
Robots learn human-like movement adjustments to prevent object slipping
To effectively tackle a variety of real-world tasks, robots should be able to reliably grasp objects of different shapes, textures and sizes, without dropping them in undesired locations. Conventional approaches to enhancing the ability of robots to grasp objects work by tightening the grip of a robotic hand to prevent objects from slipping.
Researchers at the University of Lincoln, Toshiba Europe’s Cambridge Research Laboratory, the University of Surrey, Arizona State University and KAIST recently introduced alternative computational strategies for preventing the slip of objects grasped by a robotic hand, which works by modulating the trajectories that a robotic hand follows while performing manipulative movements. Their approach, consisting of a robotic controller and a new bio-inspired predictive trajectory modulation strategy, was presented in a paper published in Nature Machine Intelligence.
“The inspiration for this paper came from a very human experience,” Amir Ghalamzan, senior author of the paper, told Tech Xplore.
Routine AI assistance may lead to loss of skills in health professionals who perform colonoscopies
The introduction of artificial intelligence (AI) to assist colonoscopies is linked to a reduction in the ability of endoscopists (health professionals who perform colonoscopies) to detect precancerous growths (adenomas) in the colon without AI assistance, according to a paper published in The Lancet Gastroenterology & Hepatology.
Colonoscopy enables detection and removal of adenomas, leading to prevention of bowel cancer. Numerous trials have shown the use of AI to assist colonoscopies increases the detection of adenomas, generating much enthusiasm for the technology. However, there is a lack of research into how continuous use of AI affects endoscopist skills, with suggestions it could be either positive, by training clinicians, or negative, leading to a reduction in skills.
Author Dr. Marcin Romańczyk, Academy of Silesia (Poland), says, To our knowledge, this is the first study to suggest a negative impact of regular AI use on health care professionals’ ability to complete a patient-relevant task in medicine of any kind.