Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. American technology is scoring big against Iran-backed threats in the Red Sea region, and it's bad news for China. You know the U.S. Navy destroyer USS Carney was in the news again Sunday, shooting down drones launched from Yemen's Houthi rebels against merchant shipping in the Red Sea. This crew has been taking out drones and missiles supplied by Iran for weeks now, and their tally is over two dozen destroyed so far.

European Union negotiators clinched a deal Friday on the world's first comprehensive artificial intelligence rules, paving the way for legal oversight of AI technology that has promised to transform everyday life and spurred warnings of existential dangers to humanity. Negotiators from the European Parliament and the bloc's 27 member countries overcame big differences on controversial points including generative AI and police use of face recognition surveillance to sign a tentative political agreement for the Artificial Intelligence Act. "The EU becomes the very first continent to set clear rules for the use of AI." The result came after marathon closed-door talks this week, with the initial session lasting 22 hours before a second round kicked off Friday morning. Officials were under the gun to secure a political victory for the flagship legislation.

First, Melissa tells me, there is a lot of disagreement about foundation models, which has taken up most of the energy and space during the latest debates. There are several definitions of the term "foundation model" floating around, which is part of what's causing the discord, but the core concept has to do with general-purpose AI that can do many different things for various applications. You've probably played around with ChatGPT; that interface is essentially powered by a foundation model, in this case a large language model from OpenAI. Making this more complex, though, is that these technologies can also be plugged into various other applications with more narrow uses, like education or advertising. Initial versions of the EU AI Act didn't explicitly consider foundation models, but Melissa notes that the proliferation of generative AI products over the past year pushed lawmakers to integrate them into the risk framework. In the version of the legislation passed by Parliament in June, all foundation models would be tightly regulated regardless of their assigned risk category or how they are used. This was deemed necessary in light of the vast amount of training data required to build them, as well as IP and privacy concerns and the overall impact they have on other technologies. But of course, tech companies that build foundation models have disputed this and advocate for a more nuanced approach that considers how the models are used. France, Germany, and Italy have flipped their positions and gone so far to say that foundation models should be largely exempt from AI Act regulations.

Jessica Melugin, Competitive Enterprise Institute Director of Center for Technology and Innovation, discusses Twitter accusing Meta of stealing trade secrets and a New York City law requiring businesses to audit A.I. hiring tools. Former Google CEO Eric Schmidt recently re-upped his calls for a global body, akin to the Intergovernmental Panel on Climate Change (IPCC), to advise member nations on regulating artificial intelligence (AI). Schmidt first made his case for an "International Panel on AI Safety" – an "IPCC for AI," if you will – in an October 2023 op-ed in the Financial Times. He writes of the AI panel's potential to be an, "an independent, expert-led body empowered to objectively inform governments about the current state of AI capabilities and make evidence-based predictions." He claims that AI policy makers, "are looking for impartial, technically reliable and timely assessments about its speed of progress and impact."

Due to costs and practical constraints, field campaigns in the atmospheric boundary layer typically only measure a fraction of the atmospheric volume of interest. Machine learning techniques have previously successfully reconstructed unobserved regions of flow in canonical fluid mechanics problems and two-dimensional geophysical flows, but these techniques have not yet been demonstrated in the three-dimensional atmospheric boundary layer. Here, we conduct a numerical analogue of a field campaign with spatially limited measurements using large-eddy simulation. We pose flow reconstruction as an inpainting problem, and reconstruct realistic samples of turbulent, three-dimensional flow with the use of a latent diffusion model. The diffusion model generates physically plausible turbulent structures on larger spatial scales, even when input observations cover less than 1% of the volume. Through a combination of qualitative visualization and quantitative assessment, we demonstrate that the diffusion model generates meaningfully diverse samples when conditioned on just one observation. These samples successfully serve as initial conditions for a large-eddy simulation code. We find that diffusion models show promise and potential for other applications for other turbulent flow reconstruction problems.

We propose score dynamics, a general framework for learning accelerated evolution operators with large timesteps from molecular-dynamics simulations. SD is centered around scores, or derivatives of the transition log-probability with respect to the dynamical degrees of freedom. The latter play the same role as force fields in MD but are used in denoising diffusion probability models to generate discrete transitions of the dynamical variables in an SD timestep, which can be orders of magnitude larger than a typical MD timestep. In this work, we construct graph neural network based score dynamics models of realistic molecular systems that are evolved with 10 ps timesteps. We demonstrate the efficacy of score dynamics with case studies of alanine dipeptide and short alkanes in aqueous solution. Both equilibrium predictions derived from the stationary distributions of the conditional probability and kinetic predictions for the transition rates and transition paths are in good agreement with MD. Our current SD implementation is about two orders of magnitude faster than the MD counterpart for the systems studied in this work. Open challenges and possible future remedies to improve score dynamics are also discussed.

Governments around the world aspire to ground decision-making on evidence. Many of the foundations of policy making - e.g. sensing patterns that relate to societal needs, developing evidence-based programs, forecasting potential outcomes of policy changes, and monitoring effectiveness of policy programs - have the potential to benefit from the use of large-scale datasets or simulations together with intelligent algorithms. These could, if designed and deployed in a way that is well grounded on scientific evidence, enable a more comprehensive, faster, and rigorous approach to policy making. Integrated Assessment Models (IAM) is a broad umbrella covering scientific models that attempt to link main features of society and economy with the biosphere into one modelling framework. At present, these systems are probed by policy makers and advisory groups in a hypothesis-driven manner. In this paper, we empirically demonstrate that modern Reinforcement Learning can be used to probe IAMs and explore the space of solutions in a more principled manner. While the implication of our results are modest since the environment is simplistic, we believe that this is a stepping stone towards more ambitious use cases, which could allow for effective exploration of policies and understanding of their consequences and limitations.

Human annotations are vital to supervised learning, yet annotators often disagree on the correct label, especially as annotation tasks increase in complexity. A common strategy to improve label quality is to ask multiple annotators to label the same item and then aggregate their labels. To date, many aggregation models have been proposed for simple categorical or numerical annotation tasks, but far less work has considered more complex annotation tasks, such as those involving open-ended, multivariate, or structured responses. Similarly, while a variety of bespoke models have been proposed for specific tasks, our work is the first we are aware of to introduce aggregation methods that generalize across many, diverse complex tasks, including sequence labeling, translation, syntactic parsing, ranking, bounding boxes, and keypoints. This generality is achieved by applying readily available task-specific distance functions, then devising a task-agnostic method to model these distances between labels, rather than the labels themselves. This article presents a unified treatment of our prior work on complex annotation modeling and extends that work with investigation of three new research questions. First, how do complex annotation task and dataset properties impact aggregation accuracy? Second, how should a task owner navigate the many modeling choices in order to maximize aggregation accuracy? Finally, what tests and diagnoses can verify that aggregation models are specified correctly for the given data? To understand how various factors impact accuracy and to inform model selection, we conduct large-scale simulation studies and broad experiments on real, complex datasets. Regarding testing, we introduce the concept of unit tests for aggregation models and present a suite of such tests to ensure that a given model is not mis-specified and exhibits expected behavior. Beyond investigating these research questions above, we discuss the foundational concept and nature of annotation complexity, present a new aggregation model as a conceptual bridge between traditional models and our own, and contribute a new general semisupervised learning method for complex label aggregation that outperforms prior work.

This work presents an algorithm for scene change detection from point clouds to enable autonomous robotic caretaking in future space habitats. Autonomous robotic systems will help maintain future deep-space habitats, such as the Gateway space station, which will be uncrewed for extended periods. Existing scene analysis software used on the International Space Station (ISS) relies on manually-labeled images for detecting changes. In contrast, the algorithm presented in this work uses raw, unlabeled point clouds as inputs. The algorithm first applies modified Expectation-Maximization Gaussian Mixture Model (GMM) clustering to two input point clouds. It then performs change detection by comparing the GMMs using the Earth Mover's Distance. The algorithm is validated quantitatively and qualitatively using a test dataset collected by an Astrobee robot in the NASA Ames Granite Lab comprising single frame depth images taken directly by Astrobee and full-scene reconstructed maps built with RGB-D and pose data from Astrobee. The runtimes of the approach are also analyzed in depth. The source code is publicly released to promote further development.

Large infrastructure networks (e.g. for transportation and power distribution) require constant monitoring for failures, congestion, and other adversarial events. However, assigning a sensor to every link in the network is often infeasible due to placement and maintenance costs. Instead, sensors can be placed only on a few key links, and machine learning algorithms can be leveraged for the inference of missing measurements (e.g. traffic counts, power flows) across the network. This paper investigates the sensor placement problem for networks. We first formalize the problem under a flow conservation assumption and show that it is NP-hard to place a fixed set of sensors optimally. Next, we propose an efficient and adaptive greedy heuristic for sensor placement that scales to large networks. Our experiments, using datasets from real-world application domains, show that the proposed approach enables more accurate inference than existing alternatives from the literature. We demonstrate that considering even imperfect or incomplete ground-truth estimates can vastly improve the prediction error, especially when a small number of sensors is available.