This volume contains revised versions of the papers selected for the second volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

Leaders from Europe and the US convened to explore exciting leaps forward of AI in oncology at the HIMSS21 & Health 2.0 European Health Conference, though the panel highlighted key barriers to greater acceptance and adoption of AI into mainstream care. The'New Frontiers of AI and Data Analytics in Oncology' session, moderated by Professor Karol Sikora, chief medical officer, Rutherford Health and former chief of the Cancer Programme, WHO, also saw leaders share innovative applications for AI used across the cancer pathway. The panel of experts also included Professor Barbara Alicja Jereczek-Fossa, associate professor of Radiation Oncology, University of Milan and head of Radiotherapy Division, European Institute of Oncology, and her colleague, Eng. Joining from the US was Dr Tufia Haddad, chair of Digital Health, Department of Oncology, Mayo Clinic and chair of Practice Innovation and Platform, Mayo Clinic Cancer Center. While AI is already widely used in oncology in image analysis and other areas, exciting new applications are being trialled at leading cancer centres across the globe.

Over the past two decades, recommender systems have attracted a lot of interest due to the explosion in the amount of data in online applications. A particular attention has been paid to collaborative filtering, which is the most widely used in applications that involve information recommendations. Collaborative filtering (CF) uses the known preference of a group of users to make predictions and recommendations about the unknown preferences of other users (recommendations are made based on the past behavior of users). First introduced in the 1990s, a wide variety of increasingly successful models have been proposed. Due to the success of machine learning techniques in many areas, there has been a growing emphasis on the application of such algorithms in recommendation systems. In this article, we present an overview of the CF approaches for recommender systems, their two main categories, and their evaluation metrics. We focus on the application of classical Machine Learning algorithms to CF recommender systems by presenting their evolution from their first use-cases to advanced Machine Learning models. We attempt to provide a comprehensive and comparative overview of CF systems (with python implementations) that can serve as a guideline for research and practice in this area.

Human interactions with the physical environment are often mediated through information services, and sometimes depend on them. These human interactions with their environment relate to a range of scales,28 in the scenario here from the "west of the city" to the "back of the store," or beyond the scenario to "the cat is under the sofa." These interactions go far beyond references to places that are recorded in geographic gazetteers,37 both in scale (the place where the cat is) and conceptualization (the place that forms the west of the city29), or that fit to the classical coordinate-based representations of digital maps. And yet, these kinds of services have to use such digital representations of environments, such as digital maps, building information models, knowledge bases, or just text/documents. Also, their abilities to interact are limited to either fusing with the environment,44 or using media such as maps, photos, augmented reality, or voice. These interactions also happen in a vast range of real-world contexts, or in situ, in which conversation partners typically adapt their conversational strategies to their interlocutor, based on mutual information, activities, and the shared situation.2 Verbal information sharing and conversations about places may also be more suitable when visual communication through maps or imagery is inaccessible, distracting, or irrelevant, such as when navigating in a familiar shopping mall.

Data Bridge Market Research published a new report, titled, "Artificial intelligence in medical imaging Market". The report offers an extensive analysis of key growth strategies, drivers, opportunities, key segments, and competitive landscape. This study is a helpful source of information for market players, investors, VPs, stakeholders, and new entrants to gain a thorough understanding of the industry and determine steps to be taken to gain a competitive advantage. Businesses can bring about an absolute knowhow of general market conditions and tendencies with the information and data covered in the large scale Artificial intelligence in medical imaging market survey report. To get knowledge of all the above things, this market report is made transparent, wide-ranging and supreme in quality.

Members of IDG's Influencer Network weigh in on the transformative power of these two technologies. As a recent article on CIO.com observed, the pandemic "has seen accelerated interest in process automation as organizations have scrambled to overhaul business processes and double down on digital transformations in response to disruptions brought about by COVID-19. And for IT leaders stepping into or already steeped in such modernization efforts, artificial intelligence -- mainly in the form of machine learning -- holds the promise to revolutionize automation, pushing them closer to their end-to-end process automation dreams." Automation and artificial intelligence (AI): The combination of these two transformative technologies has IT leaders setting their sights on some pretty lofty goals. Robotic process automation leader UiPath has characterized RPA and AI as "two of the most transformative technologies the world has ever known. But bringing AI and RPA together unleashes even more of their potential."

Artificial Intelligence in Fintech Market Size and Forecast 2021-2028 by Verified Market Research specialize in market strategy, market direction, expert opinions, and knowledgeable insight into the global market. The report is a combination of critical information including the competitive landscape; global, regional, and country-specific market size; Market participants; Market growth analysis; Market share; Analysis of opportunities, recent developments, and growth in segmentation. The report also provides other information and thoughtful facts such as historical data, sales, revenue and global market share of Artificial Intelligence in Fintech, product scope, market overview, opportunities, driving force and market share of Artificial Intelligence in Fintech. One of the important factors that make this report interesting is its comprehensive overview of the industry's competitive landscape. The report includes upstream raw materials and downstream needs analyses.

Just a few years ago, there were no legions of deep learning scientists developing intelligent products and services at major companies and startups. When the youngest among us (the authors) entered the field, machine learning did not command headlines in daily newspapers. Our parents had no idea what machine learning was, let alone why we might prefer it to a career in medicine or law. Machine learning was a forward-looking academic discipline with a narrow set of real-world applications. And those applications, e.g., speech recognition and computer vision, required so much domain knowledge that they were often regarded as separate areas entirely for which machine learning was one small component. Neural networks then, the antecedents of the deep learning models that we focus on in this book, were regarded as outmoded tools. In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and statistical modeling. With these advances in hand, we can now build cars that drive themselves with more autonomy than ever before (and less autonomy than some companies might have you believe), smart reply systems that automatically draft the most mundane emails, helping people dig out from oppressively large inboxes, and software agents that dominate the worldʼs best humans at board games like Go, a feat once thought to be decades away. Already, these tools exert ever-wider impacts on industry and society, changing the way movies are made, diseases are diagnosed, and playing a growing role in basic sciences--from astrophysics to biology.

Deep Learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval and more. However, with the progressive improvements in deep learning models, their number of parameters, latency, resources required to train, etc. have all have increased significantly. Consequently, it has become important to pay attention to these footprint metrics of a model as well, not just its quality. We present and motivate the problem of efficiency in deep learning, followed by a thorough survey of the five core areas of model efficiency (spanning modeling techniques, infrastructure, and hardware) and the seminal work there. We also present an experiment-based guide along with code, for practitioners to optimize their model training and deployment. We believe this is the first comprehensive survey in the efficient deep learning space that covers the landscape of model efficiency from modeling techniques to hardware support. Our hope is that this survey would provide the reader with the mental model and the necessary understanding of the field to apply generic efficiency techniques to immediately get significant improvements, and also equip them with ideas for further research and experimentation to achieve additional gains.

Wall Street, venture capitalists, technology executives, data scientists -- all have important reasons to understand the growth and opportunity in the artificial intelligence market to access business growth and opportunities. This gives them insights on funds invested in AI and analytics as well potential revenue growth and turnover. Indeed, the growth of AI, continuing research, development of easier open source libraries and applications in small to large scale industries are sure to revolutionize the industry the next two decades and the impact is getting felt in almost all the countries worldwide. To dive deep into the growth of AI and future trends, an insight into the type and size of the market is essential along with (a) AI-related industry market research forecasts and (b) data from reputable research sources for insight into AI valuation and forecasting. IBM's CEO claims a potential $2 trillion dollar market for "cognitive computing").