Make sense of what AI is becoming.

A new Gaussian process model calibrates overestimated variances using information gain, boosting likelihood scores by up to five percent.

A new product-of-experts Gaussian process model reduces overestimated posterior variances and improves negative log‑likelihood by up to 5% across several datasets.

A new method keeps global constraint propagators active when constraints are flattened, reducing solve time for large models.

A new product‑of‑experts Gaussian process framework reduces overestimated posterior variances, boosting negative log likelihood by up to five percent across benchmark data sets.

Researchers combined synthetic DNA strands with semiconductor materials to create a memory device capable of storing and processing information simultaneously.

Three new studies from JAIR explore constraint reformulation, Gaussian process calibration, and graph game theory.

Researchers show that agents maximizing neighborhood diversity reach equilibrium under specific graph conditions.

A new study shows how adding auxiliary variables preserves global structure when flattening half‑reified constraints, cutting solve time on large constraint programming models.

Researchers introduced GP‑pro‑c, a calibration method that reduces overestimated posterior variances in product‑of‑expert Gaussian processes.

A new study shows an information‑based calibration for product‑of‑experts Gaussian processes that cuts overestimated uncertainty by about 5%.

An overview of how the word “please” functions in everyday requests, academic contexts, and popular music videos.

Large language models launch every six to ten weeks thanks to quicker iteration and instruction tuning.