The drive to discover the next big thing in AI has funded some pretty ambitious projects — but one company is taking it as a chance to rebuild computing architecture from the ground up. Led by Naveen Rao, formerly the head of AI at Databricks, Unconventional AI promises to make inference processing vastly more power
Zhou Ductal carcinoma in situ (DCIS) is early-stage, non-invasive breast cancer that has variable potential for invasive progression. Approximately 20% of patients with biopsy-proven DCIS are found only on surgical pathology to have invasive breast cancer occult to current clinical, imaging and pathologic methods of preoperative assessment. Current practice is to treat all DCIS cases
The Western Geothermal Plant serves to cool the new servers and containment area, and reuse the excess heat by Susan Meikle, University Communications and Marketing • Published 6/25/26 Mike Ritcher (left) and David Seidl walk through the Bird Cage located within the data center at Hoyt Hall. The cooled containment area houses the new Talon
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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