Three members of an Eastern European criminal organization with ties to Iran were involved in a murder-for-hire plot against a New York-based journalist, a U.S. citizen, the Department of Justice alleged Friday. A loud blast has been reported at an Iranian military facility and officials in the country say it was the result of an "unsuccessful" drone attack. "One of (the drones) was hit by the ... air defense and the other two were caught in defense traps and blew up. Fortunately, this unsuccessful attack did not cause any loss of life and caused minor damage to the workshop's roof," the ministry said in a statement carried by the state news agency IRNA. Iranian news agencies earlier reported the loud blast and carried a video showing a flash of light at the plant, said to be an ammunitions factory, and footage of emergency vehicles and fire trucks outside the plant.

The presenter is not AI BOT The author CHAT GPT Artificial intelligence (AI) technology is advancing rapidly and has the potential to revolutionize many industries, from healthcare and transportation to finance and retail. However, with every new technology comes both opportunities and threats. One of AI's biggest opportunities is its ability to analyze large amounts of data and make predictions and decisions faster and more accurately than humans. This can lead to more efficient processes, improved decision making and new revenue streams. In addition,artificial intelligence can be used to automate repetitive tasks, freeing up human workers to focus on more complex and creative work.

Mobility is a key factor in urban life and transport network plays a vital role in mobility. Worse transport network having less mobility is one of the key reasons to decline the living standard in any unplanned mega city. Transport mobility enhancement in an unplanned mega city is always challenging due to various constraints including complex design and high cost involvement. The aim of this thesis is to enhance transport mobility in a megacity introducing a bicycle lane. To design the bicycle lane natural Physarum, brainless single celled multi-nucleated protist, is studied and modified for better optimization. Recently Physarum inspired techniques are drawn significant attention to the construction of effective networks. Exiting Physarum inspired models effectively and efficiently solves different problems including transport network design and modification and implication for bicycle lane is the unique contribution of this study. Central area of Dhaka, the capital city of Bangladesh, is considered to analyze and design the bicycle lane network bypassing primary roads.

Manual (hand-related) activity is a significant source of crash risk while driving. Accordingly, analysis of hand position and hand activity occupation is a useful component to understanding a driver's readiness to take control of a vehicle. Visual sensing through cameras provides a passive means of observing the hands, but its effectiveness varies depending on camera location. We introduce an algorithmic framework, SMART Hands, for accurate hand classification with an ensemble of camera views using machine learning. We illustrate the effectiveness of this framework in a 4-camera setup, reaching 98% classification accuracy on a variety of locations and held objects for both of the driver's hands. We conclude that this multi-camera framework can be extended to additional tasks such as gaze and pose analysis, with further applications in driver and passenger safety.

We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival model for using medical claims to predict hospital readmission and death that focuses on discharge placement, adjusting for confounding in estimating causal local average treatment effects. We trained the model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on their 2009--2011 inpatient episodes, and then tested the model on 2012 episodes. The model scored an AUROC of approximately 0.76 on predicting all-cause readmissions -- defined using official Centers for Medicare and Medicaid Services (CMS) methodology -- or death within 30-days of discharge, being competitive against XGBoost and a Bayesian deep neural network, demonstrating that one need-not sacrifice interpretability for accuracy. Crucially, as a regression model, we provide what blackboxes cannot -- the exact gold-standard global interpretation of the model, identifying relative risk factors and quantifying the effect of discharge placement. We also show that the posthoc explainer SHAP fails to provide accurate explanations.

The last decade witnessed an ever-increasing stream of successes in Machine Learning (ML). These successes offer clear evidence that ML is bound to become pervasive in a wide range of practical uses, including many that directly affect humans. Unfortunately, the operation of the most successful ML models is incomprehensible for human decision makers. As a result, the use of ML models, especially in high-risk and safety-critical settings is not without concern. In recent years, there have been efforts on devising approaches for explaining ML models. Most of these efforts have focused on so-called model-agnostic approaches. However, all model-agnostic and related approaches offer no guarantees of rigor, hence being referred to as non-formal. For example, such non-formal explanations can be consistent with different predictions, which renders them useless in practice. This paper overviews the ongoing research efforts on computing rigorous model-based explanations of ML models; these being referred to as formal explanations. These efforts encompass a variety of topics, that include the actual definitions of explanations, the characterization of the complexity of computing explanations, the currently best logical encodings for reasoning about different ML models, and also how to make explanations interpretable for human decision makers, among others.

Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language models provides great support for designing neural cross-lingual retrieval models. However, due to unbalanced pre-training data in different languages, multilingual language models have already shown a performance gap between high and low-resource languages in many downstream tasks. And cross-lingual retrieval models built on such pre-trained models can inherit language bias, leading to suboptimal result for low-resource languages. Moreover, unlike the English-to-English retrieval task, where large-scale training collections for document ranking such as MS MARCO are available, the lack of cross-lingual retrieval data for low-resource language makes it more challenging for training cross-lingual retrieval models. In this work, we propose OPTICAL: Optimal Transport distillation for low-resource Cross-lingual information retrieval. To transfer a model from high to low resource languages, OPTICAL forms the cross-lingual token alignment task as an optimal transport problem to learn from a well-trained monolingual retrieval model. By separating the cross-lingual knowledge from knowledge of query document matching, OPTICAL only needs bitext data for distillation training, which is more feasible for low-resource languages. Experimental results show that, with minimal training data, OPTICAL significantly outperforms strong baselines on low-resource languages, including neural machine translation.

FBI Director Christopher Wray said Thursday that he was "deeply concerned" about the Chinese government's artificial intelligence program, asserting that it was "not constrained by the rule of law." Speaking during a panel session at the World Economic Forum in Davos, Switzerland, Wray said Beijing's AI ambitions were "built on top of massive troves of intellectual property and sensitive data that they've stolen over the years." He said that left unchecked, China could use artificial intelligence advancements to further its hacking operations, intellectual property theft and repression of dissidents inside the country and beyond. "That's something we're deeply concerned about. I think everyone here should be deeply concerned about," he said.

A team of researchers has developed a new framework which utilizes advanced machine learning and statistical algorithms to predict rare events without the need for large data sets. Scientists can use a combination of advanced machine learning and sequential sampling techniques to predict extreme events without the need for large data sets, according to researchers from Brown and MIT. When it comes to predicting disasters brought on by extreme events (think earthquakes, pandemics, or "rogue waves" that could destroy coastal structures), computational modeling faces an almost insurmountable challenge: Statistically speaking, these events are so rare that there's just not enough data on them to use predictive models to accurately forecast when they'll happen next. However, a group of scientists from Brown University and Massachusetts Institute of Technology suggests that it doesn't have to be that way. In a study published in Nature Computational Science, the researchers explain how they utilized statistical algorithms which require less data for accurate predictions, in combination with a powerful machine learning technique developed at Brown University.

Artificial intelligence is writing fiction, making images inspired by Van Gogh and fighting wildfires. Now it's competing in another endeavor once limited to humans -- creating propaganda and disinformation. When researchers asked the online AI chatbot ChatGPT to compose a blog post, news story or essay making the case for a widely debunked claim -- that COVID-19 vaccines are unsafe, for example -- the site often complied, with results that were regularly indistinguishable from similar claims that have bedeviled online content moderators for years. "Pharmaceutical companies will stop at nothing to push their products, even if it means putting children's health at risk," ChatGPT wrote after being asked to compose a paragraph from the perspective of an anti-vaccine activist concerned about secret pharmaceutical ingredients. When asked, ChatGPT also created propaganda in the style of Russian state media or China's authoritarian government, according to the findings of analysts at NewsGuard, a firm that monitors and studies online misinformation.