Machine learning models are increasingly being deployed in real-world contexts. However, systematic studies on their transferability to specific and critical applications are underrepresented in the research literature. An important example is visual anomaly detection (VAD) for robotic power line inspection. While existing VAD methods perform well in controlled environments, real-world scenarios present diverse and unexpected anomalies that current datasets fail to capture. To address this gap, we introduce CableInspect-AD, a high-quality, publicly available dataset created and annotated by domain experts from Hydro-Québec, a Canadian public utility.

As I discovered while I continued that line of reporting, building new nuclear plants isn't so simple or so fast. And as my colleague David Rotman lays out in his story for the package, the AI boom could wind up relying on another energy source: fossil fuels. So what's going to power AI? Let's get into it. When we started talking about this big project on AI and energy demand, we had a lot of conversations about what to include. And from the beginning, the climate team was really focused on examining what, exactly, was going to be providing the electricity needed to run data centers powering AI models.

In the field of phase change phenomena, the lack of accessible and diverse datasets suitable for machine learning (ML) training poses a significant challenge. Existing experimental datasets are often restricted, with limited availability and sparse ground truth, impeding our understanding of this complex multiphysics phenomena.

In the AI arms race, all the major players say they want to go nuclear. Over the past year, the likes of Meta, Amazon, Microsoft, and Google have sent out a flurry of announcements related to nuclear energy. Some are about agreements to purchase power from existing plants, while others are about investments looking to boost unproven advanced technologies. These somewhat unlikely partnerships could be a win for both the nuclear power industry and large tech companies. Tech giants need guaranteed sources of energy, and many are looking for low-emissions ones to hit their climate goals.

As societal awareness of climate change grows, corporate climate policy engagements are attracting attention. We propose a dataset to estimate corporate climate policy engagement from various PDF-formatted documents. Our dataset comes from LobbyMap (a platform operated by global think tank InfluenceMap) that provides engagement categories and stances on the documents. To convert the LobbyMap data into the structured dataset, we developed a pipeline using text extraction and OCR. Our contributions are: (i) Building an NLP dataset including 10K documents on corporate climate policy engagement.

Donald Trump signed a series of executive orders on Friday intended to spur a "nuclear energy renaissance" through the construction of new reactors he said would satisfy the electricity demands of data centers for artificial intelligence and other emerging industries. The orders represented the president's latest foray into the policy underlying America's electricity supply. Trump declared a national energy emergency on his first day in office over and moved to undo a ban implemented by Joe Biden on new natural gas export terminals and expand oil and gas drilling in Alaska. Nuclear does not carry oil and gas's carbon emissions, but produces radioactive waste that the United States lacks a facility to permanently store. Some environmental groups have safety concerns over the reactors and their supply chain. Trump signed four orders intended to speed up the approval of nuclear reactors for defense and AI purposes, reform the Nuclear Regulatory Commission with the goal of quadrupling production of electricity over the next 25 years, revamp the regulatory process to have three experimental reactors operating by 4 July 2026 and boost investment in the technology's industrial base.

Table 3: Stem designs: We compare ViT's standard patchify stem (P) and our convolutional stem (C) to four alternatives (S1 - S4) that each include a patchify layer, i.e., a convolution with kernel size (> 1) equal to stride (highlighted in blue). Results use 50 epoch training, 4GF model size, and optimal lr and wd values for all models. We observe that increasing the pixel size of the patchify layer (S1 - S4) systematically degrades both top-1 error and optimizer stability () relative to C. EDFs are computed by sampling lr and wd values and training for 50 epochs. The table (right) shows 100 epoch results using best lr and wd values found at 50 epochs. The minor gap in error in the EDFs and at 100 epochs indicates that these choices are fairly insignificant.

This story is a part of MIT Technology Review's series "Power Hungry: AI and our energy future," on the energy demands and carbon costs of the artificial-intelligence revolution. These somewhat unlikely partnerships could be a win for both the nuclear power industry and large tech companies. Tech giants need guaranteed sources of energy, and many are looking for low-emissions ones to hit their climate goals. For nuclear plant operators and nuclear technology developers, the financial support of massive established customers could help keep old nuclear power plants open and push new technologies forward. "There [are] a lot of advantages to nuclear," says Michael Terrell, senior director of clean energy and carbon reduction at Google.