The space travel company SpaceX has completed its most successful test yet of Starship, the world's most powerful rocket -- but as the unmanned rocket completed its flight, it was destroyed upon re-entry into Earth's atmosphere. Thursday's test flight was the third conducted with Starship rockets, ahead of planned missions with the United States space agency NASA to send astronauts to the moon. SpaceX, a company founded and owned by tech entrepreneur Elon Musk, livestreamed the latest Starship experiment, noting that the vessel flew farther and faster than it had in two previous tests. However, as the rocket returned to Earth, it lost communication with SpaceX engineers. The livestream suddenly cut off, its final image showing the rocket's heat shield flaring with friction.

This article was originally featured in Foreign Policy, the magazine of global politics and ideas. A family snap of the Princess of Wales with her three children has dominated headlines and group chats since its release on U.K. Mother's Day last weekend. Princess Catherine, whom the palace says is recovering from a January abdominal surgery, is known chiefly for never putting a foot wrong during nearly two decades of intense public scrutiny--first as the girlfriend of Prince William, then as a wife and mother to future kings, and an advocate for uncontroversial but important causes, such as early childhood development. Yet, even for a woman defined by her seeming perfection--Hilary Mantel once wrote that the former duchess appeared to have been designed by a committee and built by craftsmen--the Mother's Day photo of Catherine and her family was judged to be a little too perfect. The uncanny valley of the photo was prime territory for conspiracy theories, already circulating, that the princess is missing or perhaps even dead.

Probabilistic graphical models are powerful tools for analyzing constrained, continuous domains. However, finding most-probable explanations (MPEs) in these models can be computationally expensive. In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains. We derive algorithms based on a consensus-optimization framework and demonstrate their superior performance over state of the art. We show empirically that in a large-scale voter-preference modeling problem our algorithms scale linearly in the number of dependencies and constraints.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The northeastern Ukrainian border region of Sumy said parts of its territory had lost television and radio signal on Thursday after Russia launched a mass overnight drone attack that damaged communications infrastructure. The attack with 36 drones hit four cities in Sumy region and television facilities in neighboring Kharkiv region, officials said, suggesting Moscow was trying a new tactic of striking at communications more than two years into its full-scale invasion. "As a result of the damage, part of the territory of the region (temporarily) cannot receive Ukrainian television and radio signal," the region's administration said in a statement on Telegram messenger.

The European Commission has opened a formal investigation into Chinese e-commerce site AliExpress over concerns about illegal and pornographic content on its platform. In a statement on Thursday, the European Union's executive arm said it would investigate the e-commerce giant under its Digital Services Act (DSA), a law requiring companies to do more to tackle illegal and harmful products on their platforms. The probe will determine if AliExpress breached the DSA in "areas linked to the management and mitigation of risks, to content moderation and the internal complaint handling mechanism, to the transparency of advertising and recommender systems, [and] to the traceability of traders and data access for researchers," the commission said. Fake medicines, food, and dietary supplements sales – as well as pornographic material that the commission said minors can still access on the website – are major issues, the statement read. How AliExpress recommends products to shoppers and whether the site complies with a rule requiring a searchable repository of adverts provided on the platform are other areas of investigation.

This paper addresses the problem of category-level 3D object detection. Given a monocular image, our aim is to localize the objects in 3D by enclosing them with tight oriented 3D bounding boxes. We propose a novel approach that extends the well-acclaimed deformable part-based model [1] to reason in 3D. Our model represents an object class as a deformable 3D cuboid composed of faces and parts, which are both allowed to deform with respect to their anchors on the 3D box. We model the appearance of each face in fronto-parallel coordinates, thus effectively factoring out the appearance variation induced by viewpoint.

Diversified ranking is a fundamental task in machine learning. It is broadly applicable in many real world problems, e.g., information retrieval, team assembling, product search, etc. In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples. We formulate it as an optimization problem and show that in general it is NP-hard. Then, we show that for a large volume of the parameter space, the proposed objective function enjoys the diminishing returns property, which enables us to design a scalable, greedy algorithm to find the (1 1/e) near-optimal solution. Experimental results on real data sets demonstrate the effectiveness of the proposed algorithm.

ChatGPT caught regulators by surprise when it set off a new AI race. As companies have rushed to develop and release ever more powerful models, lawmakers and regulators around the world have sought to catch up and rein in development. As governments spin up new AI programs, regulators around the world are urgently trying to hire AI experts. But some of the job ads are raising eyebrows and even chuckles among AI researchers and engineers for offering wages that, amid the current AI boom, look pitiful. The European AI Office, which will be central to the implementation of the EU's AI Act, listed vacancies early this month and wants applicants to begin work in the fall.

Linear chains and trees are basic building blocks in many applications of graphical models, and they admit simple exact maximum a-posteriori (MAP) inference algorithms based on message passing. However, in many cases this computation is prohibitively expensive, due to quadratic dependence on variables' domain sizes. The standard algorithms are inefficient because they compute scores for hypotheses for which there is strong negative local evidence. For this reason there has been significant previous interest in beam search and its variants; however, these methods provide only approximate results. This paper presents new exact inference algorithms based on the combination of column generation and pre-computed bounds on terms of the model's scoring function. While we do not improve worst-case performance, our method substantially speeds real-world, typical-case inference in chains and trees. Experiments show our method to be twice as fast as exact Viterbi for Wall Street Journal part-of-speech tagging and over thirteen times faster for a joint part-of-speed and named-entity-recognition task. Our algorithm is also extendable to new techniques for approximate inference, to faster 0/1 loss oracles, and new opportunities for connections between inference and learning. We encourage further exploration of high-level reasoning about the optimization problem implicit in dynamic programs.

Determinantal point processes (DPPs) have recently been proposed as computationally efficient probabilistic models of diverse sets for a variety of applications, including document summarization, image search, and pose estimation. Many DPP inference operations, including normalization and sampling, are tractable; however, finding the most likely configuration (MAP), which is often required in practice for decoding, is NP-hard, so we must resort to approximate inference. This optimization problem, which also arises in experimental design and sensor placement, involves finding the largest principal minor of a positive semidefinite matrix. Because the objective is log-submodular, greedy algorithms have been used in the past with some empirical success; however, these methods only give approximation guarantees in the special case of monotone objectives, which correspond to a restricted class of DPPs. In this paper we propose a new algorithm for approximating the MAP problem based on continuous techniques for submodular function maximization. Our method involves a novel continuous relaxation of the log-probability function, which, in contrast to the multilinear extension used for general submodular functions, can be evaluated and differentiated exactly and efficiently. We obtain a practical algorithm with a 1/4-approximation guarantee for a more general class of non-monotone DPPs; our algorithm also extends to MAP inference under complex polytope constraints, making it possible to combine DPPs with Markov random fields, weighted matchings, and other models. We demonstrate that our approach outperforms standard and recent methods on both synthetic and real-world data.