When most people think of artificial intelligence (AI), they think of smarty-pants robots that can service our every whim. While real robots may be in the cards, the future of AI will also revolutionize the way we work (in real life and in the metaverse). In fact, AI is already in your workplace: You use AI when you use Google Maps to find your way to an off-site meeting (perhaps in a self-driving car?), or when you use spell-check for a report. The current state of AI and the future of AI goes far beyond simplifying mundane tasks, however. Artificial intelligence, or computers that are taught to "think" like humans, can make us healthier, less stressed and happier through advancements in medicine, manufacturing and more.

France's top court says the consensus among scientists is clear'fantasy.' PARIS -- The Conseil d'État, France's highest administrative court, has pooh-poohed the idea that artificial intelligence poses an existential threat to humanity, while reassuring the public that they themselves are not secretly killer robots. The Conseil d'État provides administrative guidance to the government and was commissioned by then-Prime Minister Jean Castex in June last year to advise on how to develop the use of artificial intelligence in public administration and mitigate its risks. The report, released on Tuesday, is scathing about what it calls the myth of "singularity," where technology outsmarts and controls humanity. The court called on the government to counter this "fantasy" in its AI strategy, saying that "reflection on artificial intelligence is often the victim of parasitic, excessive concentration on artificial general intelligence."

The discovery of thousands of undeclared private swimming pools in France has provided an unexpected windfall for French tax authorities. Following an experiment using artificial intelligence (AI), more than 20,000 hidden pools were discovered. They have amassed some €10m ($9.9; £8.5m) in revenue, French media is reporting. Pools can lead to higher property taxes because they boost property value, and must be declared under French law. The software, developed by Google and French consulting firm Capgemini, spotted the pools on aerial images of nine French regions during a trial in October 2021.

PARIS -- The Conseil d'État, France's highest administrative court, has pooh-poohed the idea that artificial intelligence poses an existential threat to humanity, while reassuring the public that they themselves are not secretly killer robots. The Conseil d'État provides administrative guidance to the government and was commissioned by then-Prime Minister Jean Castex in June last year to advise on how to develop the use of artificial intelligence in public administration and mitigate its risks. The report, released on Tuesday, is scathing about what it calls the myth of "singularity," where technology outsmarts and controls humanity. The court called on the government to counter this "fantasy" in its AI strategy, saying that "reflection on artificial intelligence is often the victim of parasitic, excessive concentration on artificial general intelligence." Artificial general intelligence, or AGI, is a name for the theory that AI could surpass human intelligence.

PENGHU, Taiwan – Taiwan fired warning shots at a Chinese drone which buzzed an offshore islet on Tuesday shortly after Taiwanese President Tsai Ing-wen said she had ordered Taiwan's military to take "strong countermeasures" against what she termed Chinese provocations. It was the first time such warning shots have been fired during a period of heightened tensions between China and Taiwan. Beijing views the island as its own territory, while Taiwan strongly disputes China's sovereignty claims. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Biofilms pose significant problems for engineers in diverse fields, such as marine science, bioenergy, and biomedicine, where effective biofilm control is a long-term goal. The adhesion and surface mechanics of biofilms play crucial roles in generating and removing biofilm. Designing customized nano-surfaces with different surface topologies can alter the adhesive properties to remove biofilms more easily and greatly improve long-term biofilm control. To rapidly design such topologies, we employ individual-based modeling and Bayesian optimization to automate the design process and generate different active surfaces for effective biofilm removal. Our framework successfully generated ideal nano-surfaces for biofilm removal through applied shear and vibration. Densely distributed short pillar topography is the optimal geometry to prevent biofilm formation. Under fluidic shearing, the optimal topography is to sparsely distribute tall, slim, pillar-like structures. When subjected to either vertical or lateral vibrations, thick trapezoidal cones are found to be optimal. Optimizing the vibrational loading indicates a small vibration magnitude with relatively low frequencies is more efficient in removing biofilm. Our results provide insights into various engineering fields that require surface-mediated biofilm control. Our framework can also be applied to more general materials design and optimization.

Purpose: Artificial intelligence (AI) solutions for medical diagnosis require thorough evaluation to demonstrate that performance is maintained for all patient sub-groups and to ensure that proposed improvements in care will be delivered equitably. This study evaluates the robustness of an AI solution for the diagnosis of normal chest X-rays (CXRs) by comparing performance across multiple patient and environmental subgroups, as well as comparing AI errors with those made by human experts. Methods: A total of 4,060 CXRs were sampled to represent a diverse dataset of NHS patients and care settings. Ground-truth labels were assigned by a 3-radiologist panel. AI performance was evaluated against assigned labels and sub-groups analysis was conducted against patient age and sex, as well as CXR view, modality, device manufacturer and hospital site. Results: The AI solution was able to remove 18.5% of the dataset by classification as High Confidence Normal (HCN). This was associated with a negative predictive value (NPV) of 96.0%, compared to 89.1% for diagnosis of normal scans by radiologists. In all AI false negative (FN) cases, a radiologist was found to have also made the same error when compared to final ground-truth labels. Subgroup analysis showed no statistically significant variations in AI performance, whilst reduced normal classification was observed in data from some hospital sites. Conclusion: We show the AI solution could provide meaningful workload savings by diagnosis of 18.5% of scans as HCN with a superior NPV to human readers. The AI solution is shown to perform well across patient subgroups and error cases were shown to be subjective or subtle in nature.

We develop fast algorithms and robust software for convex optimization of two-layer neural networks with ReLU activation functions. Our work leverages a convex reformulation of the standard weight-decay penalized training problem as a set of group-$\ell_1$-regularized data-local models, where locality is enforced by polyhedral cone constraints. In the special case of zero-regularization, we show that this problem is exactly equivalent to unconstrained optimization of a convex "gated ReLU" network. For problems with non-zero regularization, we show that convex gated ReLU models obtain data-dependent approximation bounds for the ReLU training problem. To optimize the convex reformulations, we develop an accelerated proximal gradient method and a practical augmented Lagrangian solver. We show that these approaches are faster than standard training heuristics for the non-convex problem, such as SGD, and outperform commercial interior-point solvers. Experimentally, we verify our theoretical results, explore the group-$\ell_1$ regularization path, and scale convex optimization for neural networks to image classification on MNIST and CIFAR-10.

Prediction of individual's race and ethnicity plays an important role in social science and public health research. Examples include studies of racial disparity in health and voting. Recently, Bayesian Improved Surname Geocoding (BISG), which uses Bayes' rule to combine information from Census surname files with the geocoding of an individual's residence, has emerged as a leading methodology for this prediction task. Unfortunately, BISG suffers from two Census data problems that contribute to unsatisfactory predictive performance for minorities. First, the decennial Census often contains zero counts for minority racial groups in the Census blocks where some members of those groups reside. Second, because the Census surname files only include frequent names, many surnames -- especially those of minorities -- are missing from the list. To address the zero counts problem, we introduce a fully Bayesian Improved Surname Geocoding (fBISG) methodology that accounts for potential measurement error in Census counts by extending the naive Bayesian inference of the BISG methodology to full posterior inference. To address the missing surname problem, we supplement the Census surname data with additional data on last, first, and middle names taken from the voter files of six Southern states where self-reported race is available. Our empirical validation shows that the fBISG methodology and name supplements significantly improve the accuracy of race imputation across all racial groups, and especially for Asians. The proposed methodology, together with additional name data, is available via the open-source software WRU.

Cybersecurity breaches are the common anomalies for distributed cyber-physical systems (CPS). However, the cyber security breach classification is still a difficult problem, even using cutting-edge artificial intelligence (AI) approaches. In this paper, we study the multi-class classification problem in cyber security for attack detection. A challenging multi-node data-censoring case is considered. In such a case, data within each data center/node cannot be shared while the local data is incomplete. Particularly, local nodes contain only a part of the multiple classes. In order to train a global multi-class classifier without sharing the raw data across all nodes, the main result of our study is designing a multi-node multi-class classification ensemble approach. By gathering the estimated parameters of the binary classifiers and data densities from each local node, the missing information for each local node is completed to build the global multi-class classifier. Numerical experiments are given to validate the effectiveness of the proposed approach under the multi-node data-censoring case. Under such a case, we even show the out-performance of the proposed approach over the full-data approach.