This is the fourth in a multi-part series examining how artificial intelligence may impact California water. California lacks information on how much water flows through the vast majority of the state’s rivers and streams—only 8% have stream gages, instruments that measure and record this water (also known as “streamflow”). This limits water managers’ ability to efficiently

AI Exposure Discovery is the process of identifying exposed AI assets, insecure AI services, unmanaged integrations, and internet-facing AI infrastructure that increase organizational attack surface exposure. Modern AI environments operate across cloud platforms, APIs, external tools, vector databases, and connected applications. As organizations expand AI adoption, visibility into exposed AI assets often becomes limited. Misconfigured

Corresponding Author: Mirijana Irnich, MSc Abstract Background: Artificial intelligence (AI) technologies for vision-based epilepsy monitoring are advancing rapidly in health care. Despite growing research using various video data sources and analytical approaches, no comprehensive framework exists to classify these technologies. Objective: This scoping review aimed to develop and validate a taxonomy for AI technologies in

In 2026, leading enterprises are investing heavily in agentic AI, multi-agent architectures, and autonomous workflows that can operate across business functions with minimal human intervention. As a result, executives and managers must develop a new blend of technical awareness, governance expertise, and transformation leadership. These courses help bridge that gap. How We Selected These Courses?

Anyone can try to break a chatbot.   That is part of what makes AI red teaming feel accessible. Open a model, write a strange prompt, ask for something the system should refuse, reframe the request, and see what happens. Sometimes the result is funny. Sometimes it is useful. Sometimes it is a little alarming.

Introduction The integration of deep learning (DL) algorithms into clinical decision support systems holds great promise for improving diagnostic accuracy and efficiency. However, for clinicians to trust such systems, their decisions must be transparent, allowing validation against medical knowledge1. In the context of classification of electrocardiograms (ECGs), deep neural networks (DNNs) have demonstrated high accuracy

Published:  June 23, 2026 at 1:14 PM Updated:  June 23, 2026 at 1:59 PM Updates to AI forecast models in 2026 look to build upon the successes of last hurricane season The 2025 hurricane season was a coming-of-age story for AI weather models, which have been around in some capacity since around 2022. Leer en

Building AI systems at scale is demanding, requiring low-latency inference, fast vector search, strong GPU price-performance and infrastructure that can grow without multiplying operational complexity.  NVIDIA’s latest work with Amazon Web Services (AWS) addresses each of those constraints. Across Amazon OpenSearch and Amazon EC2, NVIDIA AI infrastructure is giving enterprises more practical paths to deploy