Europe seen to be falling behind in future technologies President Emmanuel Macron is rolling out France's strategy to close an artificial-intelligence gap with the U.S. and China, saying it'll require Europeans to get more comfortable with sharing data. After a buildup that includes naming one of the world's top mathematicians as his point man, Macron is expected to propose higher government funding for research and updated rules on data use in his speech Thursday at a former 13th-century Cistercian college in Paris. He also plans to tout incentives for scientists and companies to move to France. "With the digital revolution, we must be at the side of French industrial companies in the disruptive dynamic of innovation," Macron told French business executives this week. European powers risk falling behind in the global competition for future technologies as Google, Apple, Facebook and China's AI companies plow ahead.

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The Mozambique National Institute for Disaster Management and Risk Reduction (INGD) and World Food Programme (WFP) built the case for drones' capacity to give all responders an accurate picture of cyclone damage and flooding extent. Two back-to-back cyclones battered Mozambique in 2019, destroying more than 800,000 hectares of farmland during harvest season. The devastation to crops and livelihoods left nearly two million people facing acute food insecurity. The United Nations (UN) World Food Programme (WFP) responded quickly, with two helicopters to ferry supplies and rescue stranded people. Given flooded roads, the air support was crucial but not nearly enough to distribute food and find stranded people across such a wide area of impact.

The robustness of the methodology of this particular study is of less interest than the phenomenon in general, only because of the recent ubiquity of the debate. That is, if people see AI-generated images as art, and are moved by them, then what? What do we need human artists for? For Swedish artist Jonas Lund, DALL·E, Midjourney and other existing forms of AI "cannot be art without an artist … Without an artist, it's not art, it's something else." In other words, just because the outputs of these sophisticated AI systems are images, doesn't mean they're art.

The United States will likely face a "great depression" if China seizes Taiwan's semiconductor industry, a hedge fund chief has said. Speaking at the Bloomberg New Economy Forum in Singapore last week, Citadel CEO Ken Griffin said the U.S. GDP would take a hit of between 5% to 10%, causing "an immediate Great Depression." "The United States has no ability to produce anywhere near the number of semiconductors it needs to run its economy," he was quoted as saying by Fortune. "If we lose access to Taiwanese semiconductors, the hit to U.S. GDP is probably in the order of magnitude of 5% to 10%. While it is unclear when, or if, that scenario will happen, Griffin said America's recent export controls, which include measures to cut China off from semiconductor chips and chip-making equipment, amounts to the U.S. "playing with fire." "You can argue that by depriving the Chinese of access to semiconductors, we up the risk that they seize Taiwan," Griffin added. In October, the Biden administration imposed sweeping export controls that banned U.S. companies from selling advanced semiconductors or equipment used to fabricate newer chips to China. Only companies that acquire a license from the Commerce Department will be allowed to export semiconductors and chip-making equipment to Chinese companies, according to The Wall Street Journal. In addition, the Biden administration also banned international companies from exporting chips that were built using U.S. technology. American citizens and green-card holders were also banned from working on certain technology for Chinese companies and entities. China has prioritized the development of semiconductor chips that are used in a variety of technology equipment, including artificial intelligence products. Commerce Secretary Gina Raimondo this month also said China will likely use advanced semiconductor technology "for surveillance." "China is crystal clear," she said, adding, "They will use this technology for surveillance.

The debate is just beginning, and this essay attempts to address the broad ethical issues potentially associated with the development of autonomous weapons, a--... Oct 9, 2022 -- One area of particular concern is weaponization. We believe that adding weapons to robots that are remotely or autonomously operated, widely--... Who is responsible for AI ethics? What is the weaponization of artificial intelligence? Who wrote the article the ethical dilemma of robotics? How do you address ethical issues in AI? Sep 13, 2018 -- In this paper, I examine five AI ethical dilemmas: weapons and military-related applications, law and border enforcement,--...

We introduce a machine-learning framework named statistics-informed neural network (SINN) for learning stochastic dynamics from data. This new architecture was theoretically inspired by a universal approximation theorem for stochastic systems, which we introduce in this paper, and the projection-operator formalism for stochastic modeling. We devise mechanisms for training the neural network model to reproduce the correct \emph{statistical} behavior of a target stochastic process. Numerical simulation results demonstrate that a well-trained SINN can reliably approximate both Markovian and non-Markovian stochastic dynamics. We demonstrate the applicability of SINN to coarse-graining problems and the modeling of transition dynamics. Furthermore, we show that the obtained reduced-order model can be trained on temporally coarse-grained data and hence is well suited for rare-event simulations.

We address the problem of modeling, refining, and repairing formal specifications for robotic missions using assume-guarantee contracts. We show how to model mission specifications at various levels of abstraction and implement them using a library of pre-implemented specifications. Suppose the specification cannot be met using components from the library. In that case, we compute a proxy for the best approximation to the specification that can be generated using elements from the library. Afterward, we propose a systematic way to either 1) search for and refine the `missing part' of the specification that the library cannot meet or 2) repair the current specification such that the existing library can refine it. Our methodology for searching and repairing mission requirements leverages the quotient, separation, composition, and merging operations between contracts.

In the context of structured nonconvex optimization, we estimate the increase in minimum value for a decision that is robust to parameter perturbations as compared to the value of a nominal problem. The estimates rely on detailed expressions for subgradients and local Lipschitz moduli of min-value functions in nonconvex robust optimization and require only the solution of the nominal problem. The theoretical results are illustrated by examples from military operations research involving mixed-integer optimization models. Across 54 cases examined, the median error in estimating the increase in minimum value is 12%. Therefore, the derived expressions for subgradients and local Lipschitz moduli may accurately inform analysts about the possibility of obtaining cost-effective, parameter-robust decisions in nonconvex optimization.

Supermassive black holes (SMBHs) are commonly found at the centers of most massive galaxies. Measuring SMBH mass is crucial for understanding the origin and evolution of SMBHs. Traditional approaches, on the other hand, necessitate the collection of spectroscopic data, which is costly. We present an algorithm that weighs SMBHs using quasar light time series information, including colors, multiband magnitudes, and the variability of the light curves, circumventing the need for expensive spectra. We train, validate, and test neural networks that directly learn from the Sloan Digital Sky Survey (SDSS) Stripe 82 light curves for a sample of 38, 939 spectroscopically confirmed quasars to map out the nonlinear encoding between SMBH mass and multi-band optical light curves. We find a 1 scatter of 0.37 dex between the predicted SMBH mass and the fiducial virial mass estimate based on SDSS singleepoch spectra, which is comparable to the systematic uncertainty in the virial mass estimate. Our results have direct implications for more efficient applications with future observations from the Vera C. Rubin Observatory. Our code, AGNet, is publicly available at https: //github.com/snehjp2/AGNet.