When the artificial intelligence boom began, it did not start as an open, interconnected utopia. From day one, it was built in silos.
Category: robotics/AI
A humanoid robot sprints past the human half-marathon world record in Beijing race
The winner from Honor, a Chinese smartphone maker, completed the 21-kilometer (13-mile) race in 50 minutes and 26 seconds, according to a WeChat post by the Beijing Economic-Technological Development Area, also known as Beijing E-Town, where the race kicked off.
That was faster than the human world record holder, Uganda’s Jacob Kiplimo, who finished the same distance in about 57 minutes in March at the Lisbon road race.
The performance by the robot marked a significant step forward from last year’s inaugural race, during which the winning robot finished in 2 hours, 40 minutes and 42 seconds.
How Automation and AI Are Transforming Organoid Research
The life sciences are in the midst of a crucial shift, driven by the emergence of organoid-based models and the power of automation. Organoids—three-dimensional cell cultures that mimic human tissue architecture and function—are enabling researchers to ask and answer questions that were once beyond reach. Paired with advances in automation, robotics, and artificial intelligence (AI), these models are transforming drug discovery and preclinical testing, offering a more human-relevant alternative to outdated 2D cell cultures and animal models. This revolution is reshaping the pharmaceutical industry, while also holding the potential to accelerate progress in personalized medicine.
Beyond 2D: The Rise of Organoids
For decades, preclinical research has relied on 2D cell cultures, single-cell-type 3D spheroid models, and animal models, despite their limitations in replicating human biology. Organoids, which are derived from stem cells, offer a more accurate representation of human tissues, recapitulating complex biological processes such as organ-specific functionality and cellular interactions. These miniature self-organizing biological systems are being used to model diseases, test drug efficacy and toxicity, and even explore regenerative medicine.
How tiny voids could make fusion targets more stable under powerful shockwaves
Picture two materials sandwiched together. The boundary between them may appear flat, but, in reality, it is full of tiny bumps and dents. Suddenly, the materials are hit with a shockwave. If that wave hits a bump in the material interface, it slows down. If it hits a dent, it accelerates forward. This imbalance creates fast, narrow jets of material—called the Richtmyer-Meshkov (RM) instability.
In a recent paper, published in Physical Review Letters, researchers from Lawrence Livermore National Laboratory (LLNL), Imperial College London and their collaborators used AI to optimize and 3D printing to create a target that effectively negates the RM instability.
“Our target reshapes the shockwave, in both space and time, as it travels through the material,” said first author Jergus Strucka, now at the European XFEL. “Instead of a single shock hitting the surface, we introduce voids to break it up into a sequence of smaller pressure pulses that arrive at slightly different times.”
AI model ‘reads’ protein pairs, unlocking new insights into disease and drug discovery
Researchers have developed a new artificial intelligence (AI) model that can more accurately predict how proteins interact with one another—an advancement that could accelerate drug discovery and deepen insights into diseases such as cancer.
Led by Professor Zhang Yang, Senior Principal Investigator from the Cancer Science Institute of Singapore (CSI Singapore) at the National University of Singapore, and published in Nature Communications, the study introduces a paired protein language model (PPLM) that learns from two interacting proteins simultaneously, rather than analyzing them in isolation. This marks a significant shift in how AI is applied to biology, enabling more accurate prediction of protein–protein interactions that underpin nearly all cellular processes.
World’s largest collection of Olympiad-level math problems now available to everyone
Every year, the countries competing in the International Mathematical Olympiad arrive with a booklet of their best, most original problems. Those booklets get shared among delegations, then quietly disappear. No one had ever collected them systematically, cleaned them, and made them available—not for AI researchers testing the limits of mathematical reasoning, and not for the students around the world training for these competitions largely on their own.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), King Abdullah University of Science and Technology (KAUST), and HUMAIN have now done exactly that.
MathNet is the largest high-quality dataset of proof-based math problems ever created, and it is not closed. Comprising more than 30,000 expert-authored problems and solutions spanning 47 countries, 17 languages, and 143 competitions, it is five times larger than the next biggest dataset of its kind. The work will be presented at the International Conference on Learning Representations (ICLR 2026) in Brazil later this month.
AI model accurately predicts the spread of wildfires in real time
USC researchers are developing a computational model that combines satellite data and physics-based simulations to forecast a wildfire’s path, intensity, and growth rate. If you’ve ever been evacuated from your home during a wildfire, you’ll be aware of the terrifying unpredictability of the situation. From your location on the ground—rapidly gathering a few vital belongings and attempting to identify the best route to safety—there’s no way of knowing how fast a fire is growing or which direction it’s likely to take.
That was the experience of Assad Oberai, Hughes Professor of aerospace and mechanical engineering at the USC Viterbi School of Engineering. He was evacuated from his home during the Eaton Fire in January 2025—one of the most destructive wildfires in Southern California history, burning for 24 days before full containment and leaving more than 9,400 structures destroyed and over 1,000 damaged.
“Due to changing climate, we’re seeing more of these extremely intense fires—those that burn very fast and very bright,” he reflected. “We have the data at our fingertips. It all comes down to how we put it to use.”
