The Download: Quantum computing for health, and why the world doesn't recycle more nuclear waste Plus: The FBI has admitted it's buying Americans' location data. In a laboratory on the outskirts of Oxford, a quantum computer built from atoms and light awaits its moment. The device is small but powerful--and also very valuable. Infleqtion, the company that owns it, is hoping its abilities will win $5 million at a competition next week. The prize will go to the quantum computer that can solve real health care problems that conventional "classical" computers are unable to solve. But there can be only one big winner--if there is a winner at all.

Reviving the Claymills Pumping Station in Staffordshire, England has been a labor of love. Restoration work has progressed steadily for over 30 years. Breakthroughs, discoveries, and DIY tips sent six days a week. It's always good to have a passion project, but what's going on in Staffordshire, England, is likely a one-of-a-kind endeavor. In the town of Burton upon Trent, a rotating team of volunteers has spent over 30 years restoring a Victorian pump house.

The Download: The Pentagon's new AI plans, and next-gen nuclear reactors Plus: The OpenClaw frenzy has led to a new Nvidia product. The Pentagon plans to set up secure environments for generative AI companies to train military-specific versions of their models on classified data, MIT Technology Review has learned. AI models like Anthropic's Claude are already used to answer questions in classified settings, including for analyzing targets in Iran. But allowing them to train on and learn from classified data is a major new development that presents unique security risks. It would also bring AI firms closer to classified data than ever before. What do new nuclear reactors mean for waste?

In a world of plastic water filter pitchers, I tested two of the new generation of stainless-steel filter systems. I will admit that the popularity of those giant, stainless steel, gravity-fed water filters remained a mystery to me for some years--even as multi-gallon water filter systems from brands like British Berkefeld and Berkey seemed to proliferate equally among lovers of doomsday prepping and holistic wellness retreats. I have been testing much different breeds of water filters for more than a year now, including reverse osmosis filters and water pitchers. But often, the big water filter tanks have seemed as much like status symbols as functional items. If you see a big gravity-fed filter, you know the person in question is serious about wellness, survival, or both. What changed my mind about these big stainless steel filters was microplastics . Most water filter pitchers are made of BPA-free plastic. But as new research shows that bottled-water drinkers ingest tens of thousands of excess microplastic particles, wellness lovers have begun to look askance at water filters that are themselves made of plastic.

Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-output space and define efficiency as distance to this frontier, but rely on restrictive assumptions on the production set and only indirectly address heterogeneity and scale effects. We propose Geometric Manifold Analysis (GeMA), a latent manifold frontier framework implemented via a productivity-manifold variational autoencoder (ProMan-VAE). Instead of specifying a frontier function in the observed space, GeMA represents the production set as the boundary of a low-dimensional manifold embedded in the joint input-output space. A split-head encoder learns latent variables that capture technological structure and operational inefficiency. Efficiency is evaluated with respect to the learned manifold, endogenous peer groups arise as clusters in latent technology space, a quotient construction supports scale-invariant benchmarking, and a local certification radius, derived from the decoder Jacobian and a Lipschitz bound, quantifies the geometric robustness of efficiency scores. We validate GeMA on synthetic data with non-convex frontiers, heterogeneous technologies and scale bias, and on four real-world case studies: global urban rail systems (COMET), British rail operators (ORR), national economies (Penn World Table) and a high-frequency wind-farm dataset. Across these domains GeMA behaves comparably to established methods when classical assumptions hold, and provides additional insight in settings with pronounced heterogeneity, non-convexity or size-related bias.

Solar power is a critical source of renewable energy, offering significant potential to lower greenhouse gas emissions and mitigate climate change. However, the cloud induced-variability of solar radiation reaching the earth's surface presents a challenge for integrating solar power into the grid (e.g., storage and backup management). The new generation of geostationary satellites such as GOES-16 has become an important data source for large-scale and high temporal frequency solar radiation forecasting. However, no machine-learning-ready dataset has integrated geostationary satellite data with fine-grained solar radiation information to support forecasting model development and benchmarking with consistent metrics.

With the increasing popularity of machine learning techniques, it has become common to see prediction algorithms operating within some larger process. However, the criteria by which we train these algorithms often differ from the ultimate criteria on which we evaluate them. This paper proposes an end-to-end approach for learning probabilistic machine learning models in a manner that directly captures the ultimate task-based objective for which they will be used, within the context of stochastic programming. We present three experimental evaluations of the proposed approach: a classical inventory stock problem, a real-world electrical grid scheduling task, and a real-world energy storage arbitrage task. We show that the proposed approach can outperform both traditional modeling and purely black-box policy optimization approaches in these applications.

Missing values are common in photovoltaic (PV) power data, yet the uncertainty they induce is not propagated into predictive distributions. We develop a framework that incorporates missing-data uncertainty into short-term PV forecasting by combining stochastic multiple imputation with Rubin's rule. The approach is model-agnostic and can be integrated with standard machine-learning predictors. Empirical results show that ignoring missing-data uncertainty leads to overly narrow prediction intervals. Accounting for this uncertainty improves interval calibration while maintaining comparable point prediction accuracy. These results demonstrate the importance of propagating imputation uncertainty in data-driven PV forecasting.

Your Out-of-Distribution Detection Method is Not Robust!

We propose a novel Bayesian approach to modelling nonlinear alignments of time series based on latent shared information. We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field. The proposed model allows for both arbitrary alignments of the inputs and non-parametric output warpings to transform the observations. This gives rise to multiple deep Gaussian process models connected via latent generating processes. We present an efficient variational approximation based on nested variational compression and show how the model can be used to extract shared information between dependent time series, recovering an interpretable functional decomposition of the learning problem. We show results for an artificial data set and real-world data of two wind turbines.