The trial will begin in the midst of a boom in generative AI -- a wave of new technology that has been pushed by Google's competitors and has thrown the company onto its back foot. Google executives have already begun arguing that the rise of AI companies like OpenAI shows that the tech world is still competitive and that the company doesn't have an unfair grip on who wins and who loses, as some antitrust experts and the company's competitors have argued.

The recent burst of generative artificial intelligence is forcing the US Senate into a debate lawmakers have put off for years: privacy reform. While Americans' personal data is a commodity sold, traded, mined, and even "recycled," passing from second party to third party to digital banana stand, some senators believe your personal data is siloed off from the earth-altering AI work those companies, like OpenAI and Google, are testing, tweaking, and deploying daily. "They want to predict the future for purposes of marketing and selling products, and that's already there," says Florida Republican Marco Rubio, the vice-chair of the Senate Intelligence Committee, dismissing the need for an overhaul of federal privacy laws. Rubio is far from an outlier. Ted Cruz of Texas, the top Republican on the Senate Commerce Committee, agrees.

Ukrainian sea drones have attacked a key Russian port on the Black Sea, damaging a naval ship, according to a Ukrainian official, speaking about the latest in a series of strikes inside Russia after Kyiv promised to bring the fight home to the Kremlin. Moscow said it repelled Friday's attack on Novorossiysk, which marked the first time a commercial Russian port has been targeted in the 18-month war. Olenegorsky Gornyak, a landing ship, suffered a serious breach in the attack, carried out by Ukraine's navy and security service, according to a security service official. As a result, the ship is unable to carry out its combat missions, said the official who spoke on the condition of anonymity because he was not authorised to give the information to the media. Ukrainian news agencies carried footage from social media channels that they suggested showed the Olenegorsky Gornyak listing to one side. The ship is designed to transport troops and heavy equipment and was sent for repairs in 2014, according to Russian media reports.

Notable events in the year the book was taken out include The Beatles' final public performance, the Concorde supersonic airliner's first flight and US astronauts Neil Armstrong and Buzz Aldrin becoming the first people to walk on the Moon.

Almost three years into the pilot, though, it seems the government is still struggling to find compelling applications for it, and adoption has been minimal. Now the goal may be shifting. China appears to be charging ahead with plans to use the e-CNY outside its borders, for international trade. If it's successful, it could challenge the US dollar's position as the world's dominant reserve currency--and in the process shake up the global geopolitical order. This story is from MIT Technology Review's What's Next series, which looks across industries, trends, and technologies to give you a first look at the future.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' Silicon Valley has started scooping up military contracts as the Pentagon turns to private companies to boost artificial intelligence (AI) development and adoption, according to reports. "This kind of change doesn't always move as smoothly or as quickly as I'd like," Defense Secretary Lloyd Austin said during a speech in December to a group that included start-up tech companies. The courtship between tech start-ups and the Department of Defense (DOD) started well before the public engagement with large language models (LLMs) like ChatGPT: Saildrone, a start-up founded in 2013, had started developing an armada of AI systems to conduct surveillance on international waters in 2021. Alexander Karp, CEO and co-founder of Palantir Technologies, wrote an open letter to European leaders just weeks after Russia invaded Ukraine February 2022 and urged them to modernize their armies with Silicon Valley's help.

This is based on announcements by Russian and Ukrainian authorities, and local media reports. Ukrainian defence sources have told CNN that sea drones had also been used in an attack on the Kerch Bridge to Crimea in July.

The Sustainable Development Goals (SDGs) were introduced by the United Nations in order to encourage policies and activities that help guarantee human prosperity and sustainability. SDG frameworks produced in the finance industry are designed to provide scores that indicate how well a company aligns with each of the 17 SDGs. This scoring enables a consistent assessment of investments that have the potential of building an inclusive and sustainable economy. As a result of the high quality and reliability required by such frameworks, the process of creating and maintaining them is time-consuming and requires extensive domain expertise. In this work, we describe a data-driven system that seeks to automate the process of creating an SDG framework. First, we propose a novel method for collecting and filtering a dataset of texts from different web sources and a knowledge graph relevant to a set of companies. We then implement and deploy classifiers trained with this data for predicting scores of alignment with SDGs for a given company. Our results indicate that our best performing model can accurately predict SDG scores with a micro average F1 score of 0.89, demonstrating the effectiveness of the proposed solution. We further describe how the integration of the models for its use by humans can be facilitated by providing explanations in the form of data relevant to a predicted score. We find that our proposed solution enables access to a large amount of information that analysts would normally not be able to process, resulting in an accurate prediction of SDG scores at a fraction of the cost.

One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an AM model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, ultimately reducing the time/cost of AM control and optimization processes. Our adoption of the operator learning (OL) approach enabled us to learn a family of differential equations produced by altering process variables in the laser's Gaussian point heat source. More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses. Furthermore, we benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM. Ultimately, we found that OL methods offer comparable performance and, in terms of accuracy and generalizability, even outperform DNN at predicting scalar model responses. The DNN-based ROM afforded the fastest training time. Furthermore, all the ROMs were faster than the original MOOSE model yet still provided accurate predictions. FNO had a smaller mean prediction error than DeepONet, with a larger variance for time-dependent responses. Unlike DNN, both FNO and DeepONet were able to simulate time series data without the need for dimensionality reduction techniques. The present work can help facilitate the AM optimization process by enabling faster execution of simulation tools while still preserving evaluation accuracy.

In multi-objective optimization, a single decision vector must balance the trade-offs between many objectives. Solutions achieving an optimal trade-off are said to be Pareto optimal: these are decision vectors for which improving any one objective must come at a cost to another. But as the set of Pareto optimal vectors can be very large, we further consider a more practically significant Pareto-constrained optimization problem, where the goal is to optimize a preference function constrained to the Pareto set. We investigate local methods for solving this constrained optimization problem, which poses significant challenges because the constraint set is (i) implicitly defined, and (ii) generally non-convex and non-smooth, even when the objectives are. We define notions of optimality and stationarity, and provide an algorithm with a last-iterate convergence rate of $O(K^{-1/2})$ to stationarity when the objectives are strongly convex and Lipschitz smooth.