NVIDIA DSX AI Factory Platform brings together technologies to help the ecosystem design, build, and operate more efficient AI factories, including open source,...
How OpenAI built Pando, a Git-compatible backend designed for the scale of development that Codex makes possible.
OpenAI’s Grid Protectors Program will work with utilities to help future compute campuses support grid reliability and protect consumer electricity costs.
Two startups tackling the data center water problem are joining forces in a $16 million deal.
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This paper reviews control architectures for building heating, ventilation and air-conditioning (HVAC) systems, addressing the tension among energy demand, occupant comfort and equipment operability, and situating proportional-integral-derivative (PID) regulation within a layered control stack that also includes rule-based and model-based supervisory control and data-driven reinforcement learning (RL). It examines the thermostat-PID layer from two-position and scheduled setpoint control through proportional, PI and PID regulation and their tuning, then formulates RL for building control as a Markov decision process. Its central contribution is a taxonomy of where RL meets PID: RL as a gain tuner for an existing loop, as a direct replacement for the regulatory controller, as a hybrid with model predictive control, and as supervisory setpoint and schedule generation. Across these classes, the review establishes that the physical loop remains invariant while the intervention point changes, and that the classes differ in their requirements for actuation authority, state information, modelling, commissioning and constraint handling. It further establishes that PID is inexpensive and well codified but limited by nonlinearity, time variation, discrete mode switching, loop interaction and slow plant dynamics, while RL's admissible position must be argued rather than assumed. The paper concludes that these architectures are not competing philosophies of control but nested points of attachment on one HVAC plant, with hybrid implementations possible, and that deployment trade-offs must be assessed according to the specific control interface and scaffolding required.
Battery degradation remains a central challenge in the development and broad application of sustainable energy technologies. Accurate degradation prediction is challenging, as battery aging emerges from complex and heterogeneous interactions among cycling behavior, operating conditions, and cell chemistry. Existing machine-learning approaches typically focus on deterministic end-of-life predictions or modeling degradation curves within fixed chemistry settings, leaving probabilistic state of health (SOH) trajectory modeling across heterogeneous chemistries underexplored. We introduce FlowBatt, a use-inspired conditional generative framework that adapts flow matching with a diffusion transformer (DiT) backbone to battery degradation trajectory modeling. FlowBatt models full SOH trajectories as probabilistic degradation processes conditioned on early-cycle capacity data. To strengthen this conditioning pathway, we use explainable AI analysis to diagnose and improve the encoder that maps early-cycle capacity matrices into conditioning vectors. We evaluate FlowBatt on five public benchmark settings spanning diverse chemistries and aging conditions, comparing it with supervised and diffusion-based trajectory models as well as established baselines for remaining useful life (RUL) prediction under shared data splits. FlowBatt achieves the best trajectory-wise SOH prediction performance on all five datasets, with errors below 2.5%, and delivers competitive RUL prediction performance. By generating multiple plausible trajectories, FlowBatt provides empirical prediction intervals for battery degradation modeling. These results indicate that flow-matching-based trajectory modeling is a promising framework for probabilistic battery health prediction.
OpenAI said that ChatGPT and Codex will draw usage from the same allocation pool after this change.
