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Latest Posts
Professor Anna Erickson highlights the reopening of Three Mile Island Unit 1 as a crucial step in meeting the growing energy demands of AI data centers with carbon-free nuclear power, aligning with Microsoft's sustainability goals.
3 min read
The use of generative artificial intelligence in protein design stands to revolutionize new drug development. EPFL ambitions putting together a consortium to further explore this avenue.
5 min read
In an interview from CES 2025, Vikram Gupta, Chief Product Officer at Synaptics, discusses the company's strategic collaboration with Google to enhance Edge AI processors, emphasizing the importance of context-aware computing and AI-native platforms for improving user experience and efficiency.
Infineon’s Power System Reliability Modeling enhances power reliability in data centers by enabling real-time power supply monitoring, predictive maintenance, and lifetime estimation from component to system level.
Texas A&M University is creating advanced mapping technologies to further the evolution of autonomous vehicles.
3 min read
Learn how agnostic systems like Awentia's No-Data Vision Foundation Model addresses key barriers to AI adoption such as data dependency, cost, and complexity across industries like agriculture, robotics and manufacturing.
Specialized microchips that manage signals at the cutting edge of wireless technology are astounding works of miniaturization and engineering. They’re also difficult and expensive to design.
4 min read
In this episode, we recap our favorite episodes from 2024!
25 min read
EPFL researchers have developed 4M, a next-generation, open-sourced framework for training versatile and scalable multimodal foundation models that go beyond language.
4 min read
Using this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
Researchers show how the use of machine learning enormously accelerates the search for new semiconducting molecules for perovskite solar cells
3 min read
An electronic stacking technique could exponentially increase the number of transistors on chips, enabling more efficient AI hardware.
AI is transforming healthcare by enhancing diagnostic accuracy, optimizing patient care, and improving treatment outcomes, while also necessitating careful consideration of ethics, privacy, and data security.
GPUs excel in parallel processing for graphics and AI training with scalability, while NPUs focus on low-latency AI inference on edge devices, enhancing privacy by processing data locally. Together, they complement each other in addressing different stages of AI workloads efficiently.
