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 ...
Digi Times

In-depth: Google TurboQuant cuts LLM memory 6x, resets AI inference cost curve

Google has introduced TurboQuant, a compression algorithm that reduces large language model (LLM) memory usage by at least 6x while boosting performance, targeting one of AI's most persistent ...
InfoQ

Google Researchers Propose Bayesian Teaching Method for Large Language Models

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Andrew Harmel-Law and a panel of expert ...
PNAS

Navigating the mysterious space of evolutionary histories

In this issue of PNAS, Gao et al. (1) probe the limits of Bayesian phylodynamic inference, a statistical framework that has revolutionized the study of pathogen evolution and epidemic spread. By ...
eLife

Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

This paper presents a valuable software package, named "Virtual Brain Inference" (VBI), that enables faster and more efficient inference of parameters in dynamical system models of whole-brain ...
GitHub

causal-inference-k2-algorithm

Bayesian network structure learning using hybrid K2 search and hill climbing optimization. Discovers causal relationships in observational data across datasets with 8-50 variables and up to 10K ...
IEEE

High-Performance In-Memory Bayesian Inference With Multi-Bit Ferroelectric FET

Abstract: Conventional neural network-based machine learning algorithms often encounter difficulties in data-limited scenarios or where interpretability is critical. Conversely, Bayesian ...