The House minority leader blasted Democratic leadership, saying the current policy is'creating another Syria' in the Middle East. The United States military conducted a drone strike in Syria targeting a senior al-Qaeda leader and planner, a CENTCOM spokesperson says. "U.S. forces conducted a kinetic strike near Idlib, Syria, December 3, targeting a senior al-Qaeda leader and planner," CENTCOM spokesperson Captain Bill Urban told Fox News Digital in a statement. "The strike was conducted using a precision strike method from MQ-9 aircraft." Urban added that an "initial review of this strike indicates the potential for possible civilian casualties."

Legend has it a humanoid creature covered in fur inhabits the forested areas along the west coast of the northern US and although stories of this mythical monster have been told since the 1800s, no one has been able to prove its existence. The closest and most compelling evidence of Bigfoot was captured in 1967, when Bob Gimlin and Roger Patterson shot footage of a furry figure walking through Bluff Creek in Northern California. The grainy, one-minute clip has sparked many investigations into its authenticity and DailyMail.com'The Proof is Out There' episode about Bigfoot will run tonight at 10pm ET. The show has brought on a team of experts to use the latest and greatest technology for this mission, including artificial intelligence and computer vision algorithms.

Promobot, a European artificial intelligence company, has offered someone £150,000 (over €175,000) to do just that. The company want to make their robots super realistic. So, they want to base their looks off real people, with the hope of making them more lifelike. You'd fit the role if you were over 25 and have a "kind and friendly" face. The job includes taking selfies and making a 3D model of a persons face and body to be replicated for the robot's physical features.

Just when you thought it was safe to go back to normal -- are you ready for round two? "There are big changes ahead," says Forrester VP Brandon Purcell. "There are a lot of changes that have been brought about by what happened over the last 2 years. The pace of change is very rapid. There are pretty big things happening." Purcell spoke with InformationWeek about the predictions for AI in 2022 and beyond.

Facial-recognition technology (FRT) is mainly deployed in the cybersecurity and surveillance sectors. It has long been in use at airport borders and on smartphones, and as a tool to help police identify criminals. But it is now creeping further into private and public spaces. From Quito to Nairobi, Moscow to Detroit, hundreds of municipalities have installed cameras equipped with FRT, sometimes promising to feed data to central command centres as part of'safe city' or'smart city' solutions to crime. The COVID-19 pandemic might accelerate their spread.

Tensor network, which originates from quantum physics, is emerging as an efficient tool for classical and quantum machine learning. Nevertheless, there still exists a considerable accuracy gap between tensor network and the sophisticated neural network models for classical machine learning. In this work, we combine the ideas of matrix product state (MPS), the simplest tensor network structure, and residual neural network and propose the residual matrix product state (ResMPS). The ResMPS can be treated as a network where its layers map the "hidden" features to the outputs (e.g., classifications), and the variational parameters of the layers are the functions of the features of the samples (e.g., pixels of images). This is different from neural network, where the layers map feed-forwardly the features to the output. The ResMPS can equip with the non-linear activations and dropout layers, and outperforms the state-of-the-art tensor network models in terms of efficiency, stability, and expression power. Besides, ResMPS is interpretable from the perspective of polynomial expansion, where the factorization and exponential machines naturally emerge. Our work contributes to connecting and hybridizing neural and tensor networks, which is crucial to further enhance our understand of the working mechanisms and improve the performance of both models.

This work examines adaptive distributed learning strategies designed to operate under communication constraints. We consider a network of agents that must solve an online optimization problem from continual observation of streaming data. The agents implement a distributed cooperative strategy where each agent is allowed to perform local exchange of information with its neighbors. In order to cope with communication constraints, the exchanged information must be unavoidably compressed. We propose a diffusion strategy nicknamed as ACTC (Adapt-Compress-Then-Combine), which relies on the following steps: i) an adaptation step where each agent performs an individual stochastic-gradient update with constant step-size; ii) a compression step that leverages a recently introduced class of stochastic compression operators; and iii) a combination step where each agent combines the compressed updates received from its neighbors. The distinguishing elements of this work are as follows. First, we focus on adaptive strategies, where constant (as opposed to diminishing) step-sizes are critical to respond in real time to nonstationary variations. Second, we consider the general class of directed graphs and left-stochastic combination policies, which allow us to enhance the interplay between topology and learning. Third, in contrast with related works that assume strong convexity for all individual agents' cost functions, we require strong convexity only at a network level, a condition satisfied even if a single agent has a strongly-convex cost and the remaining agents have non-convex costs. Fourth, we focus on a diffusion (as opposed to consensus) strategy. Under the demanding setting of compressed information, we establish that the ACTC iterates fluctuate around the desired optimizer, achieving remarkable savings in terms of bits exchanged between neighboring agents.

Benchmark datasets play a central role in the organization of machine learning research. They coordinate researchers around shared research problems and serve as a measure of progress towards shared goals. Despite the foundational role of benchmarking practices in this field, relatively little attention has been paid to the dynamics of benchmark dataset use and reuse, within or across machine learning subcommunities. In this paper, we dig into these dynamics. We study how dataset usage patterns differ across machine learning subcommunities and across time from 2015-2020. We find increasing concentration on fewer and fewer datasets within task communities, significant adoption of datasets from other tasks, and concentration across the field on datasets that have been introduced by researchers situated within a small number of elite institutions. Our results have implications for scientific evaluation, AI ethics, and equity/access within the field.

Sparse events, such as malign attacks in real-time network traffic, have caused big organisations an immense hike in revenue loss. This is due to the excessive growth of the network and its exposure to a plethora of people. The standard methods used to detect intrusions are not promising and have significant failure to identify new malware. Moreover, the challenges in handling high volume data with sparsity, high false positives, fewer detection rates in minor class, training time and feature engineering of the dimensionality of data has promoted deep learning to take over the task with less time and great results. The existing system needs improvement in solving real-time network traffic issues along with feature engineering. Our proposed work overcomes these challenges by giving promising results using deep-stacked autoencoders in two stages. The two-stage deep learning combines with shallow learning using the random forest for classification in the second stage. This made the model get well with the latest Canadian Institute for Cybersecurity - Intrusion Detection System 2017 (CICIDS-2017) dataset. Zero false positives with admirable detection accuracy were achieved.

As companies strive toward a frictionless digital experience, they must find ways to improve customer loyalty and trust. How will digital customer experience evolve in the coming year? The pandemic-induced explosion of ecommerce and the acceleration of digital transformation means that most companies will re-examine and revamp their customer experience strategies and capabilities in the coming year. With customer loyalty increasingly difficult to gain and sustain, pioneering, data-powered technologies will improve the seamlessness of these digital experiences and deliver better brand engagement. A dozen leaders in the customer experience (CX) space spanning a range of industries – including healthcare, travel, insurance, and banking – met to discuss challenges and solutions, and debate the direction of travel in the coming year.