A new leap in lab automation is shaking up how scientists discover materials. By switching from slow, traditional methods to real-time, dynamic chemical experiments, researchers have created a self-driving lab that collects 10 times more data, drastically accelerating progress. This new system not only saves time and resources but also paves the way for faster breakthroughs in clean energy, electronics, and sustainability—bringing us closer to a future where lab discoveries happen in days, not years.
Neural networks first treat sentences like puzzles solved by word order, but once they read enough, a tipping point sends them diving into word meaning instead—an abrupt “phase transition” reminiscent of water flashing into steam. By revealing this hidden switch, researchers open a window into how transformer models such as ChatGPT grow smarter and hint at new ways to make them leaner, safer, and more predictable.
An advanced Johns Hopkins AI model called MAARS combs through underused heart MRI scans and complete medical records to spot hidden scar patterns that signal sudden cardiac death, dramatically outperforming current dice-roll clinical guidelines and promising to save lives while sparing patients unnecessary defibrillators.
What if your old chest scans—taken years ago for something unrelated—held a secret warning about your heart? A new AI tool called AI-CAC, developed by Mass General Brigham and the VA, can now comb through routine CT scans to detect hidden signs of heart disease before symptoms strike.
Despite widespread fears, early research suggests AI might actually be improving some aspects of work life. A major new study examining 20 years of worker data in Germany found no signs that AI exposure is hurting job satisfaction or mental health. In fact, there s evidence that it may be subtly improving physical health especially for workers without college degrees by reducing physically demanding tasks. However, researchers caution that it s still early days.
Once a global leader in chipmaking, the U.S. now lags behind. Sandia National Laboratories is spearheading a strategic comeback by joining a powerful new coalition the National Semiconductor Technology Center. Through cutting-edge research, collaborative partnerships, and workforce development, Sandia is aiming to reclaim semiconductor dominance, safeguard national security, and revolutionize tech innovation for everything from self-driving cars to AI processors.
AI has helped astronomers crack open some of the universe s best-kept secrets by analyzing massive datasets about black holes. Using over 12 million simulations powered by high-throughput computing, scientists discovered that the Milky Way's central black hole is spinning at nearly maximum speed. Not only did this redefine theories about black hole behavior, but it also showed that the emission is driven by hot electrons in the disk, not jets, challenging long-standing models.
UC San Diego engineers have created a passive evaporative cooling membrane that could dramatically slash energy use in data centers. As demand for AI and cloud computing soars, traditional cooling systems struggle to keep up efficiently. This innovative fiber membrane uses capillary action to evaporate liquid and draw heat away without fans or pumps. It performs with record-breaking heat flux and is stable under high-stress operation.
Physicists at the University of Colorado Boulder have created a groundbreaking quantum device that can measure 3D acceleration using ultracold atoms, something once thought nearly impossible. By chilling rubidium atoms to near absolute zero and splitting them into quantum superpositions, the team has built a compact atom interferometer guided by AI to decode acceleration patterns. While the sensor still lags behind traditional GPS and accelerometers, it's poised to revolutionize navigation for vehicles like submarines or spacecraft potentially offering a timeless, atomic-based alternative to aging electronics.
A team of researchers has shown that even small-scale quantum computers can enhance machine learning performance, using a novel photonic quantum circuit. Their findings suggest that today s quantum technology isn t just experimental it can already outperform classical systems in specific tasks. Notably, this photonic approach could also drastically reduce energy consumption, offering a sustainable path forward as machine learning s power needs soar.
In a world where over a billion smartphones are produced yearly, a team of researchers is flipping the script on electronic waste. Instead of tossing out older phones, they ve demonstrated a groundbreaking approach: turning outdated smartphones into micro data centers. This low-cost innovation (just 8 euros per phone) offers practical applications from tracking bus passengers to monitoring marine life without needing new tech.
Shrinking silicon transistors have reached their physical limits, but a team from the University of Tokyo is rewriting the rules. They've created a cutting-edge transistor using gallium-doped indium oxide with a novel "gate-all-around" structure. By precisely engineering the material's atomic structure, the new device achieves remarkable electron mobility and stability. This breakthrough could fuel faster, more reliable electronics powering future technologies from AI to big data systems.
The effects of artificial intelligence on adolescents are nuanced and complex, according to a new report that calls on developers to prioritize features that protect young people from exploitation, manipulation and the erosion of real-world relationships.
Human-AI interactions are well understood in terms of trust and companionship. However, the role of attachment and experiences in such relationships is not entirely clear. In a new breakthrough, researchers from Waseda University have devised a novel self-report scale and highlighted the concepts of attachment anxiety and avoidance toward AI. Their work is expected to serve as a guideline to further explore human-AI relationships and incorporate ethical considerations in AI design.
Despite advances in machine vision, processing visual data requires substantial computing resources and energy, limiting deployment in edge devices. Now, researchers from Japan have developed a self-powered artificial synapse that distinguishes colors with high resolution across the visible spectrum, approaching human eye capabilities. The device, which integrates dye-sensitized solar cells, generates its electricity and can perform complex logic operations without additional circuitry, paving the way for capable computer vision systems integrated in everyday devices.