Noise: Debates over bugs versus features distract from a deeper architectural shift in how Google thinks about the open web.

Google (GOOG)(GOOGL) revealed a set of new algorithms today designed to reduce the amount of memory needed to run large language models and vector search engines. The algorithms introduced by Google ...

Google published a research blog post on Tuesday about a new compression algorithm for AI models. Within hours, memory stocks were falling. Micron dropped 3 per cent, Western Digital lost 4.7 per cent ...

Google says a new compression algorithm, called TurboQuant, can compress and search massive AI data sets with near-zero indexing time, potentially removing one of the biggest speed limits in modern ...

If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what ...

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Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...

Google made a big shakeup to the cryptography world by announcing a public collision in the SHA-1 algorithm. The Wall Street Journal's Robert McMillan breaks down what this means for web security.

Google has unveiled a new memory-optimization algorithm for AI inferencing that researchers claim could reduce the amount of "working memory" an AI model requires by at least 6x. As TechCrunch reports ...

The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” ...

Google’s TurboQuant is making waves in the AI hardware sector by addressing long-standing challenges in memory usage and processing efficiency. Developed with components like the Quantized ...