Make sense of what AI is becoming.

Three recent Journal of Artificial Intelligence Research articles present new methods for learning reward machines amid noisy data, Bayesian multi-bandit algorithms that estimate task dependencies, and an extensive survey of explainable reinforcement learning techniques.

Léo Saulières’ new JAIR paper reviews over 250 explainability methods in RL and outlines future research needs.

A new hierarchical Bayesian method better captures task relationships, boosting multi-bandit performance by up to 15%.

A new learning framework called Prob‑IRM can infer reward machine models from imperfect sensor data, improving policy learning in both discrete and continuous environments.

Three newly released JAIR studies explore pricing collusion, democratic AI alignment, and quantum mereology.

A study shows that giving reinforcement‑learning agents a third price option does not raise the chance they learn to collude.

Moscow will begin government backing for artificial intelligence projects in biology starting in 2026, aligning with the European Union’s forthcoming AI legislation while stressing balanced oversight.

Researchers propose a quantum framework using qubits to model vague part–whole relations.

A comprehensive survey unites self‑supervised methods for modeling event streams across domains.

A new JAIR study links classic voting theory with modern machine‑learning tools to improve how large language models reflect societal preferences.

A recent study outlines how AI reshape multiple sectors, though specific developments remain unclear.

The npm package 'nocache', last updated over three years ago, adds HTTP headers that prevent browsers and proxies from storing responses, while query‑string techniques like '?nocache=1' also bypass cache.