Inspired by biological systems, materials scientists have long sought to harness self-assembly to build nanomaterials. The challenge: the process seemed random and notoriously difficult to predict.

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results.

Abstract: This abstract introduces a data-driven approach to managing carbon footprints in global supply chains through the integration of artificial intelligence (AI) algorithms. With the pressing ...

In a sense, it sounds like that’s another facet of computational thinking that’s more relevant in the age of AI—the abstractions of statistics and probability in addition to algorithms and data ...

Elon Musk’s xAI closes a $20 billion funding round backed by Nvidia, blending equity and GPU-backed debt to fuel data centers ...

PAAR Capital Apps Introduces Campaign Management System as Political Operatives Report Widespread Infrastructure Gaps Ahead of Midterms Campaign managers report spending 15 or more hours weekly on ...

When asked about their main challenges in adopting AI over the next two years, C-suite leaders cited data issues as their top ...

In 2026, the world is no longer defined only by military strength, economic size, or diplomatic reach, News.Az reports. A new ...

Author Shawn Peters blends clarity and rigor to make data structures and algorithms accessible to all learners. COLORADO, CO, UNITED STATES, January 2, 2026 /EINPresswire.com/ — Vibrant Publishers ...