The Artificial Intelligence (AI) Trade Is Splitting in Two. Here's How to Pick the Right Side in 2026.

To keep pace with that shift, Nvidia entered a $20 billion, non-exclusive licensing deal with the AI inference start-up Groq ...
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. Soroosh Khodami discusses why we aren't ready ...
Scientific Research Publishing

MacKay, D.J.C. (2003) Information Theory, Inference, and Learning Algorithms. Cambridge University Press.

ABSTRACT: A new conceptual framework is presented that unifies Gödel’s incompleteness theorems with practical physical modeling through information-theoretic analysis. The method of variables with ...
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

Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems

Abstract: This paper considers a joint scattering environment sensing and data recovery problem in an uplink integrated sensing and communication (ISAC) system. To facilitate joint scatterers ...
Frontiers

A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools

A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...
PNAS

Bayesian phylodynamic inference of population dynamics with dormancy

Dormancy is a widespread bet-hedging strategy across taxa, enabling organisms to survive natural and anthropogenic disturbances. It fundamentally alters eco-evolutionary processes, including ...