In the fast-paced world of artificial intelligence, memory is crucial to how AI models interact with users. Imagine talking to a friend who forgets the middle of your conversation—it would be ...

Every conversation you have with an AI — every decision, every debugging session, every architecture debate — disappears when ...

Memento-Skills lets AI agents rewrite their own skills using reinforcement learning, hitting 80% task success vs. 50% for ...

SK Hynix, Samsung and Micron shares fell as investors fear fewer memory chips may be required in the future.

Google introduces TurboQuant, a compression method that reduces memory usage and increases speed ...

At its core, the TurboQuant algorithm minimizes the space required to store memory while also preserving model accuracy. To ...

Google researchers have proposed TurboQuant, a two-stage quantization method that, according to a recent arXiv preprint, can ...

Memory is no longer just supporting infrastructure; it's now become a primary determinant of system performance, cost and ...

Google's TurboQuant combines PolarQuant with Quantized Johnson-Lindenstrauss correction to shrink memory use, raising ...

Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the model ...

Google's new TurboQuant algorithm drastically cuts AI model memory needs, impacting memory chip stocks like SK Hynix and Kioxia. This innovation targets the AI's 'memory' cache, compressing it ...