Innovations in assistive AI and, ultimately, increased autonomy with agentic AI will redefine what engineers can achieve within the chip design cycle. For decades, the design of leading-edge chips has been a high-wire act—balancing tight deadlines, sophisticated workflows, and the relentless need to consult scattered, often outdated, sources of truth.

As AI infrastructure scales at an unprecedented rate, a number of outdated assumptions keep resurfacing – especially when it comes to the role of networking in large-scale training and inference systems. Many of these myths are rooted in technologies that worked well for small clusters. But today’s systems are scaling

This article explores the deep impact of Artificial Intelligence on the future of science. It details how AI is transforming research through enhanced data analysis, accelerated discovery processes, predictive modeling, laboratory automation, and hypothesis generation, ultimately pushing the boundaries of human knowledge across all scientific disciplines. Read out the complete

AI is Smart—But Not Smart Enough to Replace Human Judgment. Learn When to Use AI to Your Advantage Key Takeaways: 1. Use AI for Speed and Repetition: AI excels at automating simple, repetitive tasks and analyzing large data sets quickly. 2. Keep Humans in the Loop: For creativity, ethics, and

Artificial intelligence with real impact Machine learning and advanced algorithms have been around for a while, but it’s only in the last five years or so that generative AI models have begun to break into the public’s consciousness. Over the last half-decade, it has evolved from a mildly amusing demo