The United States on Thursday unleashed a volley of actions to censure China's treatment of the Uyghur minority, with lawmakers voting to curb trade and new sanctions slapped on the world's top consumer drone maker. The United States has been ramping up pressure on China amid a crop of disputes, with President Joe Biden's administration a day earlier targeting producers of painkillers that have contributed to America's addiction crisis. The U.S. Senate unanimously voted to make the United States the first country to ban virtually all imports from China's northwestern Xinjiang region over concerns of the prevalence of forced labor. "We know it's happening at an alarming, horrific rate with the genocide that we now witness being carried out," said Senator Marco Rubio, a driver behind the act, which already passed the House of Representatives and which the White House says Biden will sign. After prolonged negotiations to secure its passage, Rubio lifted objections and the Senate confirmed veteran diplomat Nicholas Burns as ambassador to China.

Urbanization has a strong impact on the health and wellbeing of populations across the world. Predictive spatial modeling of urbanization therefore can be a useful tool for effective public health planning. Many spatial urbanization models have been developed using classic machine learning and numerical modeling techniques. However, deep learning with its proven capacity to capture complex spatiotemporal phenomena has not been applied to urbanization modeling. Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization. We treat numerical geospatial data as images with pixels and channels, and enrich the dataset by augmentation, in order to leverage the high capacity of deep learning. Our resulting model can generate end-to-end multi-variable urbanization predictions, and outperforms a state-of-the-art classic machine learning urbanization model in preliminary comparisons.

Artificial intelligence (AI) is beginning to make an impact on manufacturing. Data from predictive maintenance is moving into useful analytics. The manufacturing supply chain is getting optimized. AI is helping manufacturers to improve uptime, increase yield, and reduce downtime. Recently we've seen machine learning bring significant benefits to manufacturing.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. military downed a drone deemed to have hostile intent Tuesday that was headed toward a base in southeast Syria that houses 200 American troops, a senior defense official told Fox News' Jennifer Griffin. Two unmanned aerial systems were spotted entering the At Tanf Garrison Deconfliction Zone located along the Iraq and Jordan-Syria border. One of the two drones traveled deeper into the zone and was shot down after "demonstrating hostile intent," Capt.

Objective To determine whether artificial intelligence (AI) can generate plausible and engaging titles for potential Christmas research articles in The BMJ . Design Observational study. Setting Europe, Australia, and Africa. Participants 1 AI technology (Generative Pre-trained Transformer 3, GPT-3) and 25 humans. Main outcome measures Plausibility, attractiveness, enjoyability, and educational value of titles for potential Christmas research articles in The BMJ generated by GPT-3 compared with historical controls. Results AI generated titles were rated at least as enjoyable (159/250 responses (64%) v 346/500 responses (69%); odds ratio 0.9, 95% confidence interval 0.7 to 1.2) and attractive (176/250 (70%) v 342/500 (68%); 1.1, 0.8 to 1.4) as real control titles, although the real titles were rated as more plausible (182/250 (73%) v 238/500 (48%); 3.1, 2.3 to 4.1). The AI generated titles overall were rated as having less scientific or educational merit than the real controls (146/250 (58%) v 193/500 (39%); 2.0, 1.5 to 2.6); this difference, however, became non-significant when humans curated the AI output (146/250 (58%) v 123/250 (49%); 1.3, 1.0 to 1.8). Of the AI generated titles, the most plausible was “The association between belief in conspiracy theories and the willingness to receive vaccinations,” and the highest rated was “The effects of free gourmet coffee on emergency department waiting times: an observational study.” Conclusions AI can generate plausible, entertaining, and scientifically interesting titles for potential Christmas research articles in The BMJ ; as in other areas of medicine, performance was enhanced by human intervention. Dataset and full reproducible code are available at .

The widespread of offensive content online such as hate speech poses a growing societal problem. AI tools are necessary for supporting the moderation process at online platforms. For the evaluation of these identification tools, continuous experimentation with data sets in different languages are necessary. The HASOC track (Hate Speech and Offensive Content Identification) is dedicated to develop benchmark data for this purpose. This paper presents the HASOC subtrack for English, Hindi, and Marathi. The data set was assembled from Twitter. This subtrack has two sub-tasks. Task A is a binary classification problem (Hate and Not Offensive) offered for all three languages. Task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY offered for English and Hindi. Overall, 652 runs were submitted by 65 teams. The performance of the best classification algorithms for task A are F1 measures 0.91, 0.78 and 0.83 for Marathi, Hindi and English, respectively. This overview presents the tasks and the data development as well as the detailed results. The systems submitted to the competition applied a variety of technologies. The best performing algorithms were mainly variants of transformer architectures.

In recent years, person detection and human pose estimation have made great strides, helped by large-scale labeled datasets. However, these datasets had no guarantees or analysis of human activities, poses, or context diversity. Additionally, privacy, legal, safety, and ethical concerns may limit the ability to collect more human data. An emerging alternative to real-world data that alleviates some of these issues is synthetic data. However, creation of synthetic data generators is incredibly challenging and prevents researchers from exploring their usefulness. Therefore, we release a human-centric synthetic data generator PeopleSansPeople which contains simulation-ready 3D human assets, a parameterized lighting and camera system, and generates 2D and 3D bounding box, instance and semantic segmentation, and COCO pose labels. Using PeopleSansPeople, we performed benchmark synthetic data training using a Detectron2 Keypoint R-CNN variant [1]. We found that pre-training a network using synthetic data and fine-tuning on target real-world data (few-shot transfer to limited subsets of COCO-person train [2]) resulted in a keypoint AP of $60.37 \pm 0.48$ (COCO test-dev2017) outperforming models trained with the same real data alone (keypoint AP of $55.80$) and pre-trained with ImageNet (keypoint AP of $57.50$). This freely-available data generator should enable a wide range of research into the emerging field of simulation to real transfer learning in the critical area of human-centric computer vision.

Unsupervised graph-level representation learning plays a crucial role in a variety of tasks such as molecular property prediction and community analysis, especially when data annotation is expensive. Currently, most of the best-performing graph embedding methods are based on Infomax principle. The performance of these methods highly depends on the selection of negative samples and hurt the performance, if the samples were not carefully selected. Inter-graph similarity-based methods also suffer if the selected set of graphs for similarity matching is low in quality. To address this, we focus only on utilizing the current input graph for embedding learning. We are motivated by an observation from real-world graph generation processes where the graphs are formed based on one or more global factors which are common to all elements of the graph (e.g., topic of a discussion thread, solubility level of a molecule). We hypothesize extracting these common factors could be highly beneficial. Hence, this work proposes a new principle for unsupervised graph representation learning: Graph-wise Common latent Factor EXtraction (GCFX). We further propose a deep model for GCFX, deepGCFX, based on the idea of reversing the above-mentioned graph generation process which could explicitly extract common latent factors from an input graph and achieve improved results on downstream tasks to the current state-of-the-art. Through extensive experiments and analysis, we demonstrate that, while extracting common latent factors is beneficial for graph-level tasks to alleviate distractions caused by local variations of individual nodes or local neighbourhoods, it also benefits node-level tasks by enabling long-range node dependencies, especially for disassortative graphs.

Monumental advances in deep learning have led to unprecedented achievements across a multitude of domains. While the performance of deep neural networks is indubitable, the architectural design and interpretability of such models are nontrivial. Research has been introduced to automate the design of neural network architectures through neural architecture search (NAS). Recent progress has made these methods more pragmatic by exploiting distributed computation and novel optimization algorithms. However, there is little work in optimizing architectures for interpretability. To this end, we propose a multi-objective distributed NAS framework that optimizes for both task performance and introspection. We leverage the non-dominated sorting genetic algorithm (NSGA-II) and explainable AI (XAI) techniques to reward architectures that can be better comprehended by humans. The framework is evaluated on several image classification datasets. We demonstrate that jointly optimizing for introspection ability and task error leads to more disentangled architectures that perform within tolerable error.

A little over a year has passed since Timnit Gebru was fired from Google. The 38-year old Ethiopian-American researcher and former co-lead of the company's Ethical AI unit, believes she was pushed out for working on an academic paper that raised red flags about using large language models in Google's quest to develop "superintelligent" AI systems. The research highlighted the ways AI can misinterpret language on the internet, which can lead to "stereotyping, denigration, increases in extremist ideology, and wrongful arrest," as Gebru and her co-authors put it. Tired of tussling with the internal politics of mega corporations, Gebru has struck out on her own. She recently launched an independent practice called Distributed AI Research Institute or DAIR--a homonym for "dare"--with funding from the MacArthur Foundation, the Ford Foundation, the Kapor Center, the Open Society Foundations and the Rockefeller Foundation.