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Category: robotics/AI – Page 14

AlphaFold Changed Science. After 5 Years, It’s Still Evolving

Until AlphaFold’s debut in November 2020, DeepMind had been best known for teaching an artificial intelligence to beat human champions at the ancient game of Go. Then it started playing something more serious, aiming its deep learning algorithms at one of the most difficult problems in modern science: protein folding. The result was AlphaFold2, a system capable of predicting the three-dimensional shape of proteins with atomic accuracy.

Its work culminated in the compilation of a database that now contains over 200 million predicted structures, essentially the entire known protein universe, and is used by nearly 3.5 million researchers in 190 countries around the world. The Nature article published in 2021 describing the algorithm has been cited 40,000 times to date. Last year, AlphaFold 3 arrived, extending the capabilities of artificial intelligence to DNA, RNA, and drugs. That transition is not without challenges—such as “structural hallucinations” in the disordered regions of proteins—but it marks a step toward the future.

To understand what the next five years holds for AlphaFold, WIRED spoke with Pushmeet Kohli, vice president of research at DeepMind and architect of its AI for Science division.

Google AI CEO Demis Hassabis calls Meta AI chief scientist Yann LeCun ‘plain incorrect’, read his long post on why he thinks Yann is ‘wrong’

The TOI Tech Desk is a dedicated team of journalists committed to delivering the latest and most relevant news from the world of technology to readers of The Times of India. TOI Tech Desk’s news coverage spans a wide spectrum across gadget launches, gadget reviews, trends, in-depth analysis, exclusive reports and breaking stories that impact technology and the digital universe. Be it how-tos or the latest happenings in AI, cybersecurity, personal gadgets, platforms like WhatsApp, Instagram, Facebook and more; TOI Tech Desk brings the news with accuracy and authenticity.

Longevity in 2025: The Breakthroughs That Actually Mattered

This year quietly rewired how researchers think about aging, what truly predicts long-term health, and which biohacking ideas deserve serious attention versus skepticism. From brain aging to muscle strength, from AI-driven drug discovery to cooling hype around supplements, 2025 redrew the map of healthspan science.

Here’s the clear-eyed recap of what actually mattered.

Intracranial Aneurysm Rupture Risk Prediction Model

This machine learning model was trained on a large multicenter dataset of unruptured intracranial aneurysms to predict future rupture risk across international cohorts, supporting more precise and personalized treatment decisions.

Question Can a machine-learning model (MLM) predict the rupture risk of unruptured intracranial aneurysms (UIAs) using prerupture data?

Findings In this prognostic study of 11 579 UIAs from a cohort of 8,846 patients, an MLM trained on prerupture clinical and morphological data demonstrated robust performance in both internal and external validation, including on aneurysms smaller than 10 mm.

Meaning The findings of this study suggest that an MLM may improve risk stratification and inform treatment decision-making for patients with UIAs, providing additional guidance even for smaller aneurysms traditionally considered low risk.

AI-based tool predicts future cardiovascular events in patients with angina

Reduced coronary blood flow, measured with an artificial intelligence-based imaging tool, predicted future cardiovascular events in patients with suspected stable coronary artery disease. These findings were presented at EACVI 2025, the congress of the European Association of Cardiovascular Imaging (EACVI).

Stable coronary artery disease (CAD) refers to the common syndrome of recurrent, transient episodes of chest symptoms, often manifesting as angina. Coronary computed tomography angiography (CCTA) is a noninvasive heart scan that is used as the first-line investigation for patients with suspected stable CAD.

AI tools and FFR-CT explained While CCTA clearly shows blockages in coronary arteries, it is limited in its ability to estimate reduced blood flow, which is necessary to diagnose angina. An artificial intelligence-based tool has been developed that analyzes CCTA images and provides an estimate of blood flow, termed CT-derived fractional flow reserve (FFR-CT).

Why Everyone Is Talking About Data Centers In Space

Questions to inspire discussion.

Launch Economics & Viability.

🚀 Q: What launch cost makes space data centers economically competitive? A: Space data centers become cost-competitive with ground systems when launch costs drop to approximately $200/kg, according to Google’s Suncatcher paper, making the economics viable for moving compute infrastructure off-Earth.

💰 Q: Why might SpaceX pursue a $1.5 trillion IPO valuation? A: The projected $1.5 trillion SpaceX IPO valuation is speculated to fund the capital-intensive race to establish space-based data centers and secure the best orbital positions before competitors.

🏢 Q: Which companies can realistically build space data centers first? A: Vertically integrated organizations like SpaceX, Relativity Space, and Blue Origin lead because they control launch infrastructure, can self-fund deployment, and serve as their own customers for space compute capacity.

🛰️ Q: How would space data centers physically connect GPUs across satellites? A: Multiple free-flying satellites in formation (like 20+ Starlink satellites) use inter-satellite optical connections to enable communication between GPUs, creating high-density computing clusters in orbit.

Continue reading “Why Everyone Is Talking About Data Centers In Space” | >

Promising new superconducting material discovered with the help of AI

Tohoku University and Fujitsu Limited have successfully used AI to derive new insights into the superconductivity mechanism of a new superconducting material.

Their findings demonstrate an important use case for AI technology in new materials development and suggest that the technology has the potential to accelerate research and development. This could drive innovation in various industries such as the environment and energy, drug discovery and health care, and electronic devices.

The AI technology was used to automatically clarify causal relationships from measurement data obtained at NanoTerasu Synchrotron Light Source. This achievement was published in Scientific Reports.

Lowering barriers to explainable AI: Control technique for LLMs reduces resource demands by over 90%

Large language models (LLMs) such as GPT and Llama are driving exceptional innovations in AI, but research aimed at improving their explainability and reliability is constrained by massive resource requirements for examining and adjusting their behavior.

To tackle this challenge, a Manchester research team led by Dr. Danilo S. Carvalho and Dr. André Freitas have developed new software frameworks—LangVAE and LangSpace—that significantly reduce both hardware and energy resource needs for controlling and testing LLMs to build explainable AI. Their paper is published on the arXiv preprint server.

Their technique builds compressed language representations from LLMs, making it possible to interpret and control these models using geometric methods (essentially treating the model’s internal language patterns as points and shapes in space that can be measured, compared and adjusted), without altering the models themselves. Crucially, their approach reduces computer resource usage by more than 90% compared with previous techniques.

MIT Engineers Create 3D-Printable Aluminum 5 Times Stronger Than Conventional Alloys

By applying machine learning techniques, engineers at MIT have created a new method for 3D printing metal alloys that produce parts far stronger than those made using traditional manufacturing approaches. MIT engineers have created a new aluminum alloy designed for 3D printing that holds up under

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