Global Artificial Intelligence (AI) in BFSI Sector market report provides information from major key players, geography, segmentation, competitor analysis, sales, revenue, price, gross margin, market share, import-export, trends and forecast 2021-2027. The Artificial Intelligence (AI) in BFSI Sector market Research is an intelligent report with careful efforts to study accurate and valuable information. The data that has been examined is made with regard to both the best existing players and future competitors. The business strategies of the major players and new industries in the emerging market are studied in detail. A well-explained SWOT analysis, revenue sharing and contact information are shared in this report analysis.

RST-style discourse parsing plays a vital role in many NLP tasks, revealing the underlying semantic/pragmatic structure of potentially complex and diverse documents. Despite its importance, one of the most prevailing limitations in modern day discourse parsing is the lack of large-scale datasets. To overcome the data sparsity issue, distantly supervised approaches from tasks like sentiment analysis and summarization have been recently proposed. Here, we extend this line of research by exploiting distant supervision from topic segmentation, which can arguably provide a strong and oftentimes complementary signal for high-level discourse structures. Experiments on two human-annotated discourse treebanks confirm that our proposal generates accurate tree structures on sentence and paragraph level, consistently outperforming previous distantly supervised models on the sentence-to-document task and occasionally reaching even higher scores on the sentence-to-paragraph level.

Population-level societal events, such as civil unrest and crime, often have a significant impact on our daily life. Forecasting such events is of great importance for decision-making and resource allocation. Event prediction has traditionally been challenging due to the lack of knowledge regarding the true causes and underlying mechanisms of event occurrence. In recent years, research on event forecasting has made significant progress due to two main reasons: (1) the development of machine learning and deep learning algorithms and (2) the accessibility of public data such as social media, news sources, blogs, economic indicators, and other meta-data sources. The explosive growth of data and the remarkable advancement in software/hardware technologies have led to applications of deep learning techniques in societal event studies. This paper is dedicated to providing a systematic and comprehensive overview of deep learning technologies for societal event predictions. We focus on two domains of societal events: \textit{civil unrest} and \textit{crime}. We first introduce how event forecasting problems are formulated as a machine learning prediction task. Then, we summarize data resources, traditional methods, and recent development of deep learning models for these problems. Finally, we discuss the challenges in societal event forecasting and put forward some promising directions for future research.

Natural Language Interfaces to Databases (NLIDBs), where users pose queries in Natural Language (NL), are crucial for enabling non-experts to gain insights from data. Developing such interfaces, by contrast, is dependent on experts who often code heuristics for mapping NL to SQL. Alternatively, NLIDBs based on machine learning models rely on supervised examples of NL to SQL mappings (NL-SQL pairs) used as training data. Such examples are again procured using experts, which typically involves more than a one-off interaction. Namely, each data domain in which the NLIDB is deployed may have different characteristics and therefore require either dedicated heuristics or domain-specific training examples. To this end, we propose an alternative approach for training machine learning-based NLIDBs, using weak supervision. We use the recently proposed question decomposition representation called QDMR, an intermediate between NL and formal query languages. Recent work has shown that non-experts are generally successful in translating NL to QDMR. We consequently use NL-QDMR pairs, along with the question answers, as supervision for automatically synthesizing SQL queries. The NL questions and synthesized SQL are then used to train NL-to-SQL models, which we test on five benchmark datasets. Extensive experiments show that our solution, requiring zero expert annotations, performs competitively with models trained on expert annotated data.

It may be smart, but it's not that clever. Artificial intelligence is nothing without human input. The algorithms that drive AI rely on the expertise of programmers and it's still no more than a tool – albeit a powerful one – that scientists and engineers can use to solve problems. Yet this is not to say that AI isn't the fastest-growing deep technology in the world, with the potential to transform people's lives and boost nations' economies. Facilitating AI innovation has even become a priority for the UK government, as laid out in the National AI Strategy it published in September.

Distributional semantic models represent the meaning of words as vectors. We introduce a selection method to learn a vector space that each of its dimensions is a natural word. The selection method starts from the most frequent words and selects a subset, which has the best performance. The method produces a vector space that each of its dimensions is a word. This is the main advantage of the method compared to fusion methods such as NMF, and neural embedding models. We apply the method to the ukWaC corpus and train a vector space of N=1500 basis words. We report tests results on word similarity tasks for MEN, RG-65, SimLex-999, and WordSim353 gold datasets. Also, results show that reducing the number of basis vectors from 5000 to 1500 reduces accuracy by about 1.5-2%. So, we achieve good interpretability without a large penalty. Interpretability evaluation results indicate that the word vectors obtained by the proposed method using N=1500 are more interpretable than word embedding models, and the baseline method. We report the top 15 words of 1500 selected basis words in this paper.

This paper uses techniques from Random Matrix Theory to find the ideal training-testing data split for a simple linear regression with m data points, each an independent n-dimensional multivariate Gaussian. It defines "ideal" as satisfying the integrity metric, i.e. the empirical model error is the actual measurement noise, and thus fairly reflects the value or lack of same of the model. This paper is the first to solve for the training and test size for any model in a way that is truly optimal. The number of data points in the training set is the root of a quartic polynomial Theorem 1 derives which depends only on m and n; the covariance matrix of the multivariate Gaussian, the true model parameters, and the true measurement noise drop out of the calculations. The critical mathematical difficulties were realizing that the problems herein were discussed in the context of the Jacobi Ensemble, a probability distribution describing the eigenvalues of a known random matrix model, and evaluating a new integral in the style of Selberg and Aomoto. Mathematical results are supported with thorough computational evidence. This paper is a step towards automatic choices of training/test set sizes in machine learning.

Europe was the fastest growing region for machine learning hiring among tech industry companies in the three months ending October. The number of roles in Europe made up 9.4% of total machine learning jobs – up from 7.7% in the same quarter last year. That was followed by Middle East & Africa, which saw a -0.2 year-on-year percentage point change in machine learning roles. The figures are compiled by GlobalData, who track the number of new job postings from key companies in various sectors over time. Using textual analysis, these job advertisements are then classified thematically.

Christmas Spirit Foundation executive director Rick Dungey on bringing cheer to military families and how viewers can help. The top U.S. commander for the Middle East said Thursday that the United States will keep the current 2,500 troops in Iraq for the foreseeable future, and he warned that he expects increasing attacks on U.S. and Iraqi personnel by Iranian-backed militias determined to get American forces out. Marine Gen. Frank McKenzie said in an interview with The Associated Press at the Pentagon that despite the shift by U.S. forces to a non-combat role in Iraq, they will still provide air support and other military aid for Iraq's fight against the Islamic State. Noting that Iranian-backed militias want all Western forces out of Iraq, he said an ongoing uptick in violence may continue through December. Gen. McKenzie, commander of the United States Central Command, testifies before the House Armed Services Committee on the conclusion of military operations in Afghanistan and plans for future counterterrorism operations on Wednesday, Sept. 29, 2021, on Capitol Hill in Washington.

It is surrounded on all sides, regularly bombed, and plagued by shortages of vital medicines. Yet in the lead-up to Christmas, the isolated Gaza Strip has – for once – ample supplies of something the rest of the world craves but can rarely find: a brand new PlayStation 5. Sony's flagship video game console is hot property this holiday season, although most people who have asked for one will be sorely disappointed on Christmas morning. A global supply-chain crisis twinned with a shortage of semiconductors – vital computer chips used in the console – has kept stock scarce. While secondhand, resold and stolen PS5's can be found, they are rare and often cost much more than the £359-£449 retail price tag. In downtown Gaza City, the price is also way above the official figure.