Autonomous driving systems increasingly rely on data-driven approaches, yet many still struggle with reasoning, handling rare scenarios, and transparently explaining their actions. A new study introduces DriveMLM, a multi-modal large language model framework that aligns language-based reasoning with structured behavioral planning states, enabling full closed-loop driving in realistic simulators. By integrating multi-view images, LiDAR inputs, traffic rules, and natural-language instructions, DriveMLM generates both driving decisions and human-readable explanations that map directly to vehicle control. The system significantly improves safety, adaptability, and interpretability, demonstrating how large language models (LLMs) can advance the next generation of autonomous driving technology.

A comprehensive review in Current Molecular Pharmacology highlights how machine learning, molecular dynamics simulations, and hybrid AI-MD platforms are revolutionizing antimicrobial discovery. These computational strategies can drastically reduce development time and costs while enabling the design of novel antibiotics and antimicrobial peptides. The authors propose an integrated “computation-experiment-clinical translation” framework to overcome current bottlenecks and combat the global AMR crisis.

Abu Dhabi, United Arab Emirates – The United Arab Emirates has launched Abu Dhabi’s AI Ecosystem for Global Agricultural Development, a platform designed to bring AI solutions to climate-exposed agricultural regions and support the communities most affected by shifting weather patterns.

Meet the robotic dog with a memory like an elephant and the instincts of a seasoned first responder. Developed by Texas A&M University engineering students, this AI-powered robotic dog doesn’t just follow commands — it sees, remembers and thinks. Designed to navigate chaos with precision, the robot could revolutionize search-and-rescue missions, disaster response and many other emergency operations. With cutting-edge memory and voice-command capabilities, it’s not just a machine. It’s a game-changing partner — and the smartest dog around — in emergencies. 

MUTE-Seq is a new liquid-biopsy method powered by an engineered ultra-precise CRISPR enzyme, FnCas9-AF2, which can distinguish single-base mismatches across all sgRNA positions with near-zero off-target activity. By selectively removing wild-type DNA before sequencing, it boosts true mutant signals up to tens of times and enables detection as low as ~0.005% VAF. The technique improves MRD monitoring and early-stage cancer detection while avoiding the need for costly ultra-deep sequencing.