This important work introduces an integrated open-source platform for behavioral acquisition and pose estimation that substantially improves the accessibility and speed of real-time animal tracking ...

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

Open-source Python code that follows along with Sanjeev V. Namjoshi's Fundamentals of Active Inference (MIT Press, 2026). The book itself is not open source; this repository provides a clean, ...

A significant shift is under way in artificial intelligence, and it has huge implications for technology companies big and small. For the past half-decade, most of the focus in AI has been on training ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Spencer Judge discusses the architectural ...

Inferring group norms is crucial for adapting behaviors in novel situations, but its underlying basis and computational account remain unclear. This study manipulated the prevalence of norm-consistent ...

The demand for uncertainty quantification in modern sequence modeling tasks has prompted researchers to explore deep integration between Bayesian inference and Transformer architectures, but existing ...

Forbes contributors publish independent expert analyses and insights. I write about the economics of AI. When OpenAI’s ChatGPT first exploded onto the scene in late 2022, it sparked a global obsession ...

As frontier models move into production, they're running up against major barriers like power caps, inference latency, and rising token-level costs, exposing the limits of traditional scale-first ...

Abstract: Naïve Bayesian inference enables classification or prediction of an event given observations of potentially contradictory evidences, and is particularly intriguing in power-limited contexts ...

Probability theory is the mathematical framework for quantifying uncertainty and making decisions under uncertainty. It forms the foundation of many modern AI techniques, including Bayesian inference ...

You’re reading Open Questions, Joshua Rothman’s weekly column exploring what it means to be human. What do you read, and why? A few decades ago, these weren’t urgent questions. Reading was an ...