New Google TurboQuant algorithm improves vector search speed

A small error-correction signal keeps compressed vectors accurate, enabling broader, more precise AI retrieval.

LLM-referred traffic converts at 30-40% — and most enterprises aren't optimizing for it

AI agents are replacing traditional search for serious work — and LLM-referred traffic converts at 30-40%, far above SEO and ...

Google unveils TurboQuant, a new AI memory compression algorithm — and yes, the internet is calling it ‘Pied Piper’

Google’s TurboQuant has the internet joking about Pied Piper from HBO's "Silicon Valley." The compression algorithm promises ...
Hackaday

TurboQuant: Reducing LLM Memory Usage With Vector Quantization

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in which the probabilities of tokens occurring in a specific order is ...

Google’s TurboQuant Marks A Turning Point In AI’s Evolution

Google’s TurboQuant could cut LLM memory use sixfold, signaling a shift from brute-force scaling to efficiency and broader AI ...

Matcha model makes drug candidate screening more than 30 times faster

Ligand Pro, founded by Skoltech professors and a Skoltech Ph.D. student, has presented Matcha, an AI-powered molecular docking model that performs virtual drug screening 30 times faster than the large ...
The Johns Hopkins News-Letter

Algorithms of spring: ads, love and language

In early spring, advertisements for dating apps start appearing everywhere. They promise efficiency. Compatibility ...