IITGN researchers have used machine learning to develop a framework that can support the development of tailored rigid pavements in a climate-resilient and region-specific manner in India and the world. The team identified five microzones in the Indian state of Gujarat, showing that its rigid pavements experience different thermal behaviours, challenging the one-size-fits-all approach used in concrete road design. The proposed framework is a smarter approach that could make roads more durable and reduce maintenance costs. 

Insilico Medicine (“Insilico”, HKEX:3696), a clinical-stage, generative AI-driven drug discovery company, is addressing this critical unmet need by using AI to uncover novel biological mechanisms and develop innovative therapies. The company recently announced the nomination of ISM9528 as a Preclinical Candidate Compound (PCC) for pain management. Identified by PandaOmics, ISM9528 is a potential first-in-class, brain-penetrant, orally available inhibitor of Target Z that offers a novel, non-opioid treatment approach by acting on a previously unexplored biological mechanism. Notably, this marks the 31st PCC nominated by Insilico since 2021, further validating the company’s AI-driven R&D capabilities.

Human biological evolution typically unfolds over millennia, while artificial intelligence can undergo major advances within years. This growing disparity in rates of change raises a fundamental question: How should humanity respond as technological change increasingly outpaces biological adaptation?

Addressing this question, a team led by Professor Tian-Ling Ren of Tsinghua University has published a Position Paper titled “Transcending Natural Evolution in the Age of Artificial Intelligence” in SCIENCE CHINA Information Sciences. The paper describes this disparity as an “evolutionary tempo mismatch” and examines how artificial intelligence could extend from the digital realm into physical environments, the human body, and neural tissue. It also introduces two related concepts, the “embodiment threshold” and “transcending natural evolution”, and outlines how flexible bioelectronics and brain-computer interfaces could support human-machine integration. The paper further considers the risk of “biological-technological stratification” under constraints in compute capacity, energy, materials, and technological access, and proposes a phased, trigger-based framework for adaptive governance.