Through systematic experiments DeepSeek found the optimal balance between computation and memory with 75% of sparse model ...

Shape memory alloys are exotic materials that can be deformed at room temperature and return to their "remembered," ...

MIT’s Recursive Language Models rethink AI memory by treating documents like searchable environments, enabling models to ...

Abstract: Short-term time series forecasting is pivotal in various scientific and industrial fields. Recent advancements in deep learning-based technologies have significantly improved the efficiency ...

The Kulwicki Driver Development Program has officially announced that it is now accepting applications for the 2026 season.

Abstract: Resistive random access memory (RRAM) devices offer a broad range of attractive properties for in-memory computing (IMC) applications, such as nonvolatile storage, low read current, and high ...

The world’s leading chip companies came to CES 2026 this week with some major announcements that will give channel partners a ...

B, an open-source AI coding model trained in four days on Nvidia B200 GPUs, publishing its full reinforcement-learning stack as Claude Code hype underscores the accelerating race to automate software ...

Building Generative AI models depends heavily on how fast models can reach their data. Memory bandwidth, total capacity, and ...

The programming language Rue combines the advantages of Rust with a simpler syntax. The compiler for it is being developed by ...

The Chinese AI lab may have just found a way to train advanced LLMs in a manner that's practical and scalable, even for more cash-strapped developers.

Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.