Research Open access Published: 22 October 2025 Journal of NeuroEngineering and Rehabilitation volume 22, Article number: 217 (2025) Cite this article Abstract Background This study utilized artificial intelligence (AI)–based machine learning algorithms, alongside the shapley additive explanations (SHAP) framework, to identify lower-limb muscle force patterns associated with recurrent patellofemoral pain (PFP) in the anterior and posterior patellar (APP)

Insider Brief Google Quantum AI’s 65-qubit processor performed a complex physics simulation 13,000 times faster than the Frontier supercomputer, marking measurable progress toward practical quantum advantage. The experiment used a new “Quantum Echoes” algorithm to measure interference effects called OTOC(2), revealing quantum behavior that classical machines cannot efficiently reproduce. The team linked the work to

AI labs are racing to build data centers as large as Manhattan, each costing billions of dollars and consuming as much energy as a small city. The effort is driven by a deep belief in “scaling” — the idea that adding more computing power to existing AI training methods will eventually yield superintelligent systems capable