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Hospital AI tool predicts low blood sugar in patients up to 24 hours in advance

Cedars-Sinai Health Sciences University investigators developed an AI-based model that can identify hospitalized patients at risk of low blood sugar up to 24 hours before the condition occurs. The long short-term memory (LSTM) model, described in npj Digital Medicine, could help clinicians intervene earlier and prevent complications, including, in severe cases, seizures, coma and long-term heart arrhythmias.

The model addresses a longstanding challenge in hospital care. Low blood sugar, also called hypoglycemia, is a common and potentially life-threatening complication among hospitalized patients, including those receiving diabetes treatment, those who are fasting before procedures or those in critical care. However, there are no widely used tools for predicting which hospitalized patients may develop hypoglycemia.

“Today, most hospital care for hypoglycemia is reactive, and we respond after a patient’s blood sugar drops,” said Roma Gianchandani, MD, senior author of the study and vice chair of quality and innovation in the Department of Medicine and program director for diabetes.

AI can be an ally in rooting out ransomware threats

AI can be used to prevent cybersecurity threats linked to ransomware, says University of Cincinnati researcher Nelly Elsayed.

“We are in a hype era of AI,” says Elsayed, associate professor in the UC School of Information Technology. “Some people support it, others fear it, but in general people who design technology are trying to use it for good.”

Elsayed, founder and leader of the Applied Machine Learning and Intelligence Lab at UC, recently published research in the Journal of Information Security and Applications, arguing that generative AI may be an ally in strengthening ransomware defense.

The AI Future No One Wants to Talk About

Go to https://ground.news/sabine to get 40% off the Vantage plan and see through sensationalized reporting. Stay fully informed on events around the world with Ground News.

In today’s video I speculate about the future of artificial intelligence.

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Defect detection automated in diamond, other advanced semiconductors

Materials scientists at Rice University have developed a new workflow methodology for measuring microscopic defects in diamond and other advanced semiconductor materials. By making it easier to spot flaws that can undermine performance, the approach could accelerate the development of more reliable electronic and quantum devices.

The research team developed a custom Python-based software tool to rapidly analyze data from high-resolution X-ray diffraction, a technique that uses X-rays to probe a material’s internal crystal structure. The software analyzes the resulting diffraction patterns, picks up on dislocations and irregularities in the atomic lattice, and calculates their density in a given material.

“Dislocations can disrupt how charge and heat move through the material, which impacts how efficient and reliable a device is and how easy it is to manufacture at scale,” said Xiang Zhang, assistant research professor of materials science and nanoengineering at Rice and a corresponding author on the study published in Advanced Materials.

‘Who is going to pay us when we’re replaced by robots?’ The Indian factory workers told to film themselves for AI

As Lalita sat stitching shirts and trousers, the camera recorded everything: the rhythm of her hands guiding cloth through the sewing machine; the precision with which she aligned collars and seams; the speed at which her fingers corrected folds and imperfections; even interactions with colleagues. ‘We found it funny at first, because of how we all looked with that headgear,’ she says.

But the atmosphere on the factory floor soon started to change. Worried that their productivity was being monitored, workers became more conscious of their movements. Conversations that would ordinarily unfold across sewing lines grew quieter. Some paid greater attention to their work, wary that every mistake, pause or distraction could be captured on camera.

What Lalita and her colleagues did not know was that their daily routines were being captured as part of a growing effort by companies in India to collect first-hand data from factory floors, information increasingly valuable in the race to automate industrial work.

First-person recordings of human movements and interactions are called egocentric data and are vital for training robots that might one day replace humans on the production line.


When workers had cameras attached to them, they found it funny at first. But novelty soon turned to concern.

Manifesting Imagination: The Dawn of the Vibe-Coding Era

The tech world is standing at the edge of a massive shift in how software is built. For decades, bringing an idea to life meant getting bogged down in rigid syntax and manual coding.

But what if you could essentially just talk your software into existence? Welcome to the dawn of the “Vibecoding” era—a space where I believe we are moving past traditional engineering and into the seamless orchestration of human intent.

Instead of hunting for syntax errors on flat screens, imagine an immersive environment where you collaborate with AI to instantly capture the core “vibe” of your idea.

The system takes your conversational guidance and simultaneously synthesizes beautiful user interfaces and robust backend architectures. It’s an absolute game-changer, especially for founders wanting to spin up a production-ready MVP for an investor pitch in just a single afternoon without writing a single line of code.

I just published a new article exploring how this shift is completely reshaping the way I look at the creative lifecycle—from the initial spark of an idea to macro-scale, global digital infrastructures.

Click the link below to read the full breakdown. I’d love to hear your thoughts on where you think this is heading!


Gödel’s Theorem to Gödel AI: The Blueprint for Self-Learning Machines

Gödel’s Mind: How AI Agents Emerged from a Logical Paradox.

The Gödel Agent, a new AI research paper, represents a novel paradigm in self-referential AI agents by leveraging recursive self-improvement inspired by the Gödel machine. Traditional agentic systems have been constrained by human design, either through hand-crafted algorithms or pre-defined meta-learning routines, limiting the scope of optimization. The Gödel Agent framework bypasses these limitations by allowing agents to modify not only their decision-making policies but also their meta-learning algorithms dynamically and autonomously. The self-referential nature of Gödel Agent enables it to modify its own code during runtime, thereby continuously evolving without predefined constraints or bottlenecks imposed by human-designed modules.

Central to the Gödel Agent’s methodology is its use of large language models (LLMs) that drive recursive decision-making and self-modification. The agent operates by analyzing its performance in the environment, retrieving its current codebase from runtime memory, and employing monkey patching to alter its behavior. This process of \.

Machine-intelligent multimodal algebot for intracavitary chemotherapy

A deep learning-guided image-feedback system enables non-invasive real-time navigation and spatiotemporally controlled intravesical drug release from magnetic biohybrid microrobots in a murine bladder tumour model, enhancing tissue penetration and therapeutic efficacy.

Stanford Just Built a Quantum Computer That Needs No Extreme Cooling

Stanford researchers may have just opened the door to a future where quantum technology no longer depends on multi-million-dollar cryogenic systems.

In this video, we break down Stanford University’s groundbreaking 2025 research that demonstrated room-temperature photon-electron quantum entanglement on a silicon-compatible chip. While this is not yet a full quantum computer, it represents a major step toward solving one of the biggest challenges in quantum technology: the extreme cooling requirements that have limited quantum systems for decades.

We’ll explore how twisted light, molybdenum diselenide (MoSe₂), valley states, and silicon nanostructures work together to create stable quantum interactions without dilution refrigerators operating near absolute zero. You’ll also learn what this breakthrough means for the future of quantum computing, quantum communication, quantum cryptography, and the emerging quantum internet.

🔹 What Stanford actually built.
🔹 Why current quantum computers require ultra-cold temperatures.
🔹 How room-temperature quantum entanglement was achieved.
🔹 The role of twisted photons and valley states.
🔹 What this breakthrough can and cannot do today.
🔹 Potential impact on IBM, Google, Microsoft, IonQ, and the broader quantum industry.
🔹 The future of room-temperature quantum networks and computing.

If this technology successfully scales, it could dramatically reduce the cost, complexity, and energy requirements of quantum systems, potentially transforming quantum technology from a specialized laboratory tool into a widely deployable platform.

Subscribe for in-depth analysis of emerging technologies, quantum computing breakthroughs, artificial intelligence, geopolitics, defense innovation, and the technologies shaping the future.

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