As the North American banking landscape continues to evolve, many consumers have noted the growing presence of Toronto-based TD Bank Group in major U.S. cities across the East Coast. TD Bank, which brands itself as "America's Most Convenient Bank", is now the 8th largest U.S. bank by deposits and the 10th largest bank in the United States by total assets. I recently spoke with TD executive, Michael Rhodes, who serves as Group Head, Innovation, Technology, and Shared Services for the bank, and I began our conversation with a direct question -- given that only 37.8% of leading firms report being data-driven, and only 26.8% claim to have established a data culture, would he characterize TD as being "data-driven". His response was quick and emphatic. "Yes, we are data-driven", Rhodes replied, "We have made substantial investments in data and AI capabilities that are providing customer value today".

Mediation Perspectives is a periodic blog entry that's provided by the CSS' Mediation Support Team and occasional guest authors. How is artificial intelligence (AI) affecting conflict and its resolution? Peace practitioners and scholars cannot afford to disregard ongoing developments related to AI-based technologies – both from an ethical and a pragmatic perspective. In this blog, I explore AI as an evolving field of information management technologies that is changing both the nature of armed conflict and the way we can respond to it. AI encompasses the use of computer programmes to analyse big amounts of data (such as online communication and transactions) in order to learn from patterns and predict human behaviour on a massive scale.

The Open University is the UK's largest university, a world leader in flexible part-time education combining a mission to widen access to higher education with research excellence, transforming lives through education. We are seeking to appoint a Lecturer or Senior Lecturer in Artificial Intelligence to join the School of Languages and Applied Linguistics. The candidate will work in one or more of the topics of central importance to the School of Languages and Applied Linguistics, with special attention to workplace communication and/or the interface between social media and AI. The successful candidate will have a master's degree or equivalent in linguistics or a related field, at Senior Lecturer level you will also have a PHD or equivalent in interactional linguistics, preferably ethnomethodological conversation analysis and/or interactional linguistics. Evidence of an emerging research profile in AI and human communication is also essential for this post.

According to recent research by IDC, the total global spending on Artificial Intelligence (AI) systems will touch US$98 billion by 2023. Nearly 61% of marketers also say that AI is the most important aspect of their data strategy. This impact can be felt directly in the marketing communication of brands, helping them understand their key consumers while tailoring their offerings and messaging accordingly. There is a new intelligence in people's midst; chatbots, recommendation engines, algorithms, language processing, etc., are just the tip of the iceberg. AI is helping brands create engaging stories and campaigns.

As technology advances further, numerous companies are realizing the effectiveness of artificial intelligence and investing more in its implementation. The implementation does not necessarily mean adoption rather they are trying to advance each and every aspect of AI. One significant area where even big techs are not leaving loops open is AI talent. Big corporations across every sector, from retail to agriculture, are trying to integrate machine learning into their products. At the same time, there is an acute shortage of AI talent.

Machine learning's ability to perform intellectually demanding tasks across various fields, materials science included, has caused it to receive considerable attention. Many believe that it could be used to unlock major time and cost savings in the development of new materials. The growing demand for the use of machine learning to derive fast-to-evaluate surrogate models of material properties has prompted scientists at the National Institute for Materials Science in Tsukuba, Japan, to demonstrate that it could be the key driver of the "next frontier" of materials science in recently published research. To learn, machines rely on processing data using both supervised and unsupervised learning. With no data, however, there is nothing to learn from.

Shell has a broader strategy to embed AI across its operations, a move that has helped the oil giant lower costs and avoid downtime. Other oil-and-gas companies that have tapped AI to improve operations and reduce costs include Exxon Mobil Corp., BP PLC and Chevron Corp. "Artificial intelligence enables us to process the vast quantity of data across our businesses to generate new insights which can keep us ahead of the competition," said Yuri Sebregts, Shell's chief technology officer, in an email. The initiative at Shell expands a 2019 yearlong pilot program with Udacity, based in Mountain View, Calif., that included about 250 Shell data scientists and software engineers. They picked up AI skills such as reinforcement learning, a type of machine learning where algorithms learn the correct way to perform an action based on trial-and-error and observations. Shell employees could use AI expertise, for example, to better predict equipment failures and automatically identify areas within a facility to reduce carbon emissions, said Dan Jeavons, Shell's general manager of data science.

Technology and trends, go hand in hand these days. From automation to connectivity, artificial intelligence to virtual reality, the decade had it all. As we are about to step in the last year of the decade, we drift our focus from many to the one that is expected to disrupt the global business industry, IoT! Gartner report's that there would 4.8 billion devices installed in work infrastructure towards the end of 2019 and around more than 20 billion interconnected devices would ply across the globe. The gamut of IoT seems to be huge and it would not be wrong to state that IoT is the new norm penetrating deep in organizational infrastructure and across all business verticals. However, technology never remains the same or it never stagnates.

The findings were discussed at the annual meeting of the American Association for the Advancement of Science in Seattle on Friday. Sex robots integrate artificial intelligence and traditional as well as novel technologies that may result in widely unknown and unpredictable risks. Scientists are concerned these sex robots (or love dolls) are being designed to look like children or even programmed to protest and simulate a rape scenario. According to tech expert Chris Riddell, stricter regulation of sex robots is needed immediately, "otherwise it's going to be the wild west." "Until now, we've only had human-to-human relationships. We're heading into an era where humans are having relationships with technology systems, and that's disturbing us," Riddell told 10 daily.

Machine learning shows great performance in various problems of electrocardiography (ECG) signal analysis. However, collecting a dataset for biomedical engineering is a very difficult task. Any dataset for ECG processing contains from 100 to 10,000 times fewer cases than datasets for image or text analysis. This issue is especially important because of physiological phenomena that can significantly change the morphology of heartbeats in ECG signals. In this preliminary study, we analyze the effects of lead choice from the standard ECG recordings, variation of ECG during 24-hours, and the effects of QT-prolongation agents on the performance of machine learning methods for ECG processing. We choose the problem of subject identification for analysis, because this problem may be solved for almost any available dataset of ECG data. In a discussion, we compare our findings with observations from other works that use machine learning for ECG processing with different problem statements. Our results show the importance of training dataset enrichment with ECG signals acquired in specific physiological conditions for obtaining good performance of ECG processing for real applications.