As enterprises develop AI agents to tackle complex, multistep tasks, models that can provide strong reasoning accuracy with efficient token generation enable intelligent, autonomous decision-making at scale.
NVIDIA Nemotron is a family of advanced open reasoning models that use leading models, NVIDIA-curated open datasets and advanced AI techniques to provide an accurate and efficient starting point for AI agents.
An international research team led by the University of Waterloo is developing technology to dissolve painful kidney stones in the urinary tract using tiny robots. The research is published in the journal Advanced Healthcare Materials.
The new technique, tested in a life-size, 3D-printed model, features thin, spaghetti-like strips fitted with magnets that can be moved into place near uric acid kidney stones with a robotic arm operated by doctors.
The soft, flexible robot strips are about a centimeter long and contain an enzyme called urease. Once in place, the urease reduces the acidity of the surrounding urine, thereby dissolving stones until they are small enough to pass naturally in just a few days.
A new artificial intelligence tool will help Stanford Health Care physicians inform patients of their test results, with the goal of reducing administrative tasks.
ARC Prize
Traditional drug development methods involve identifying a target protein (e.g., a cancer cell receptor) that causes disease, and then searching through countless molecular candidates (potential drugs) that could bind to that protein and block its function. This process is costly, time-consuming, and has a low success rate.
KAIST researchers have developed an AI model that, using only information about the target protein, can design optimal drug candidates without any prior molecular data—opening up new possibilities for drug discovery. The research is published in the journal Advanced Science.
The research team led by Professor Woo Youn Kim in the Department of Chemistry has developed an AI model named BInD (Bond and Interaction-generating Diffusion model), which can design and optimize drug candidate molecules tailored to a protein’s structure alone—without needing prior information about binding molecules. The model also predicts the binding mechanism (non-covalent interactions) between the drug and the target protein.
What do large language models, cellular automata, and the human brain have in common? In this polymath salon, I talk with Dugan Hammock from the Wolfram Institute to discuss the deep links between these seemingly disparate fields.
Highlights include:
Computational Irreducibility: Why we can’t take shortcuts in complex systems—whether it’s a simple cellular automaton or a sophisticated LLM generating text.
The Power of Autoregression: How the simple, step-by-step process of predicting the next element can give rise to incredible complexity and human-like language.
The Nature of Thinking: Whether our own thought processes are fundamentally autoregressive and sequential, or if there’s a different, parallel mode of cognition at play.
Memory and Consciousness: The critical role of a system’s “memory” or history in shaping its future, and how this relates to our own awareness and sense of self.
