Supply Lines is a daily newsletter that tracks Covid-19's impact on trade. Sign up here, and subscribe to our Covid-19 podcast for the latest news and analysis on the pandemic. ByteDance Ltd. will be required to seek Chinese government approval to sell the U.S. operations of its short-video TikTok app under new restrictions Beijing imposed on the export of artificial intelligence technologies, according to a person familiar with the matter. AI interface technologies such as speech and text recognition, and those that analyze data to make personalized content recommendations, were added to a revised list of export-control products published on the Ministry of Commerce's website late Friday. Government permits will be required for overseas transfers to "safeguard national economic security," it said.

Supply Lines is a daily newsletter that tracks Covid-19's impact on trade. Sign up here, and subscribe to our Covid-19 podcast for the latest news and analysis on the pandemic. China said it will restrict the exports of some artificial intelligence technology to "safeguard national economic security," and will require government permits for overseas transfers. AI interface technologies such as speech and text recognition, and those that analyze data to make personalized content recommendations, were added to a revised list of export-control products published on the Ministry of Commerce's website late Friday. The new restrictions could encompass technologies used by China's ByteDance Ltd., the official Xinhua News Agency reported, citing a trade expert.

This paper examines and extends the legal microdirectives Recent research by legal scholars suggests that the law theories in three crucial respects: might inevitably be transformed into legal microdirectives consisting of legal rules that are derived (1) By indicating that legal micro-directives are from legal standards or that are otherwise produced likely to be AIenabled and evolve over time in automatically or via the consequent derivations of scope and velocity across the autonomous levels of legal goals and then propagated via automation for AI Legal Reasoning [20] [22], everyday use as readily accessible lawful directives throughout society. This paper examines and extends (2) By exploring the tradeoffs between legal the legal micro-directives theories in three crucial standards and legal rules as the imprinters of the respects: (1) By indicating that legal micro-directives micro-directives, and are likely to be AIenabled and evolve over time in scope and velocity across the autonomous levels of AI (3) By illuminating a set of brittleness exposures Legal Reasoning, (2) By exploring the tradeoffs that can undermine legal micro-directives and between legal standards and legal rules as the proffering potential mitigating remedies to seek imprinters of the micro-directives, and (3) By greater robustness in the instantiation and illuminating a set of brittleness exposures that can promulgation of such AIenabled lawful directives.

Detection of military assets on the ground can be performed by applying deep learning-based object detectors on drone surveillance footage. The traditional way of hiding military assets from sight is camouflage, for example by using camouflage nets. However, large assets like planes or vessels are difficult to conceal by means of traditional camouflage nets. An alternative type of camouflage is the direct misleading of automatic object detectors. Recently, it has been observed that small adversarial changes applied to images of the object can produce erroneous output by deep learning-based detectors. In particular, adversarial attacks have been successfully demonstrated to prohibit person detections in images, requiring a patch with a specific pattern held up in front of the person, thereby essentially camouflaging the person for the detector. Research into this type of patch attacks is still limited and several questions related to the optimal patch configuration remain open. This work makes two contributions. First, we apply patch-based adversarial attacks for the use case of unmanned aerial surveillance, where the patch is laid on top of large military assets, camouflaging them from automatic detectors running over the imagery. The patch can prevent automatic detection of the whole object while only covering a small part of it. Second, we perform several experiments with different patch configurations, varying their size, position, number and saliency. Our results show that adversarial patch attacks form a realistic alternative to traditional camouflage activities, and should therefore be considered in the automated analysis of aerial surveillance imagery.

We introduce a new machine-learning-based approach, which we call the Independent Classifier networks (InClass nets) technique, for the nonparameteric estimation of conditional independence mixture models (CIMMs). We approach the estimation of a CIMM as a multi-class classification problem, since dividing the dataset into different categories naturally leads to the estimation of the mixture model. InClass nets consist of multiple independent classifier neural networks (NNs), each of which handles one of the variates of the CIMM. Fitting the CIMM to the data is performed by simultaneously training the individual NNs using suitable cost functions. The ability of NNs to approximate arbitrary functions makes our technique nonparametric. Further leveraging the power of NNs, we allow the conditionally independent variates of the model to be individually high-dimensional, which is the main advantage of our technique over existing non-machine-learning-based approaches. We derive some new results on the nonparametric identifiability of bivariate CIMMs, in the form of a necessary and a (different) sufficient condition for a bivariate CIMM to be identifiable. We provide a public implementation of InClass nets as a Python package called RainDancesVI and validate our InClass nets technique with several worked out examples. Our method also has applications in unsupervised and semi-supervised classification problems.

An infrastructure for multisite, geographically-distributed creation and collection of diverse, high-quality, curated and labeled radiology image data is crucial for the successful automated development, deployment, monitoring and continuous improvement of Artificial Intelligence (AI)/Machine Learning (ML) solutions in the real world. An interactive radiology reporting approach that integrates image viewing, dictation, natural language processing (NLP) and creation of hyperlinks between image findings and the report, provides localized labels during routine interpretation. These images and labels can be captured and centralized in a cloud-based system. This method provides a practical and efficient mechanism with which to monitor algorithm performance. It also supplies feedback for iterative development and quality improvement of new and existing algorithmic models. Both feedback and monitoring are achieved without burdening the radiologist. The method addresses proposed regulatory requirements for post-marketing surveillance and external data. Comprehensive multi-site data collection assists in reducing bias. Resource requirements are greatly reduced compared to dedicated retrospective expert labeling.

In nuclear engineering, modeling and simulations (M&Ss) are widely applied to support risk-informed safety analysis. Since nuclear safety analysis has important implications, a convincing validation process is needed to assess simulation adequacy, i.e., the degree to which M&S tools can adequately represent the system quantities of interest. However, due to data gaps, validation becomes a decision-making process under uncertainties. Expert knowledge and judgments are required to collect, choose, characterize, and integrate evidence toward the final adequacy decision. However, in validation frameworks CSAU: Code Scaling, Applicability, and Uncertainty (NUREG/CR-5249) and EMDAP: Evaluation Model Development and Assessment Process (RG 1.203), such a decision-making process is largely implicit and obscure. When scenarios are complex, knowledge biases and unreliable judgments can be overlooked, which could increase uncertainty in the simulation adequacy result and the corresponding risks. Therefore, a framework is required to formalize the decision-making process for simulation adequacy in a practical, transparent, and consistent manner. This paper suggests a framework "Predictive Capability Maturity Quantification using Bayesian network (PCMQBN)" as a quantified framework for assessing simulation adequacy based on information collected from validation activities. A case study is prepared for evaluating the adequacy of a Smoothed Particle Hydrodynamic simulation in predicting the hydrodynamic forces onto static structures during an external flooding scenario. Comparing to the qualitative and implicit adequacy assessment, PCMQBN is able to improve confidence in the simulation adequacy result and to reduce expected loss in the risk-informed safety analysis.

Patients will benefit from major improvements in technology to speed up the diagnosis of deadly diseases like cancer thanks to further investment in the use of artificial intelligence across the NHS. A £50 million funding boost will scale up the work of existing Digital Pathology and Imaging Artificial Intelligence Centres of Excellence, which were launched in 2018 to develop cutting-edge digital tools to improve the diagnosis of disease. The 3 centres set to receive a share of the funding, based in Coventry, Leeds and London, will deliver digital upgrades to pathology and imaging services across an additional 38 NHS trusts, benefiting 26.5 million patients across England. Pathology and imaging services, including radiology, play a crucial role in the diagnosis of diseases and the funding will lead to faster and more accurate diagnosis and more personalised treatments for patients, freeing up clinicians' time and ultimately saving lives. Technology is a force for good in our fight against the deadliest diseases – it can transform and save lives through faster diagnosis, free up clinicians to spend time with their patients and make every pound in the NHS go further.

Nowadays the term computing is very broad. The definition covers everything that is necessary to handle information with computers, e.g. Whole disciplines like Computer Engineering, Information Technology or Cybersecurity also belong to this term. I want to start this article with a very brief history of computing and based on this extrapolate where the journey could go. Computing is as old as mankind.

One of Wall Street's largest private equity firms, the Blackstone Group, has been making a series of moves that have left mainstream analysts puzzled, with the most recent being Blackstone's hire of David Urban, a Washington lobbyist with close ties to the Trump administration. Blackstone's courting of a Trump ally was not surprising given that the firm's CEO, Steven Schwarzman, recently donated $3 million to Trump's re-election efforts and had previously chaired the President's now-defunct Strategic and Policy Forum of "business leaders" and advisors. The close ties that have developed between Schwarzman and Trump following the latter's election in late 2016 have led mainstream media to describe Schwarzman as a confidant of the President. However, what was odd about Blackstone's hiring of David Urban was its murky reason for doing so, as the firm plans to task Urban with lobbying the Pentagon and State Department on "issues related to military preparedness and training." This is odd, as CNBC noted, because Blackstone "doesn't have any publicly listed government contracts, and its known investments don't appear to have direct links to the defense industry."