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This paper presents a novel implementation of a three-dimensional Virtual Librarian Chatbot using IBM Watson artificial intelligence technology and virtual reality. In this method, participants interact with virtual librarian chatbots by asking specific questions about the library system. This research investigated the factors used in the Technology Acceptance Model, mainly Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, Intention to Use, and Curiosity, to examine the effectiveness of the suggested Virtual Librarian Chatbot. These results highlight the potential for integrating Virtual Librarian Chatbots in academic libraries to enhance user experience, support remote learning and research, and streamline library services. The successful implementation of such chatbots could transform user interactions with academic libraries in the digital age, offering instant access to information and reducing the workload of human librarians. [ABSTRACT FROM AUTHOR]

Letters of Recommendation (LORs) are widely utilized for admission to both undergraduate and graduate programs, and are becoming even more important with the decreasing role that standardized tests play in the admissions process. However, LORs are highly subjective and thus can inject recommender bias into the process, leading to an inequitable evaluation of the candidates' competitiveness and competence. Our study utilizes natural language processing methods and manually determined ratings to investigate gender and cultural differences and biases in LORs written for STEM Master's program applicants. We generate features to measure important characteristics of the LORs and then compare these characteristics across groups based on recommender gender, applicant gender, and applicant country of origin. One set of features, which measure the underlying sentiment, tone, and emotions associated with each LOR, is automatically generated using IBM Watson's Natural Language Understanding (NLU) service. The second set of features is measured manually by our research team and quantifies the relevance, specificity, and positivity of each LOR. We identify and discuss features that exhibit statistically significant differences across gender and culture study groups. Our analysis is based on approximately 4000 applications for the MS in Data Science and MS in Computer Science programs at Fordham University. To our knowledge, no similar study has been performed on these graduate programs. [ABSTRACT FROM AUTHOR]

From I Birth of a Nation i to Leni Reifensthal's propaganda films, to the rise of the Hollywood blockbuster, to the spread of popular cinema across the world, the technology of the motion picture has changed the world at large, a world within which education functions. Keywords: Creativity; Technology; Education; Research; Future; Learning EN Creativity Technology Education Research Future Learning 207 212 6 03/27/23 20230301 NES 230301 I Progress, far from consisting in change, depends on retentiveness. Similarly, the invention of the diode in 1904 and the triode in 1906 may not impacted education directly within their immediate years (or even decades). [Extracted from the article]

That is, clinicians give discretionary authority to medical AI applications over some matter of value on the basis of normative and predictive expectations about that medical AI application. The discretionary account, Nickel states, best explains the way that AI developers describe what they are doing and the moral commitments they take on by inviting clinicians to trust in medical AI applications. They argue that there is an asymmetry between human and AI epistemology, which poses a challenge for the integration of AI output in decision-making processes. AI is believed to have the potential to radically change modern medicine. [Extracted from the article]

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th Conference on Uncertainty in Artificial Intelligence (Watson et al., 2021), we attempt to fill this gap. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We propose a novel formulation of these concepts, and demonstrate its advantages over leading alternatives. We present a sound and complete algorithm for computing explanatory factors with respect to a given context and set of agentive preferences, allowing users to identify necessary and sufficient conditions for desired outcomes at minimal cost. Experiments on real and simulated data confirm our method's competitive performance against state of the art XAI tools on a diverse array of tasks. [ABSTRACT FROM AUTHOR]

The final issue of TechTrends for 2023 features two columns and ten original papers that focus on the use of educational technologies to enhance teaching and learning. The articles cover topics such as artificial intelligence (AI), analytics, personalized learning, and broadening access to computer science education. One paper discusses a virtual librarian chatbot that uses IBM Watson to provide customized assistance to library users, while another explores an automated classroom analytics system called TEACHActive. The issue showcases emerging AI technologies and their potential applications in education, as well as learner-focused approaches enabled by education technology. The findings presented in this issue have implications for the design of responsive learning environments that prepare all students for future success. [Extracted from the article]

Artificial intelligence (AI) is increasingly used to extract knowledge from complex and large data sets such as those generated by high-throughput molecular technologies. While linear regression models have a simple interpretation in terms of linear effects of covariates on the dependent variable, data-driven machine learning models are often perceived as a "black box". Couckhuyt et al. provide an overview of the whole process from exploratory data analyses using machine learning to translation into a clinical setting, which they define as "translational machine learning". [Extracted from the article]

In this article, we start with John McCarthy's definition of Artificial Intelligence and describe select approaches to its sub-areas: natural language processing and question answering. We then outline the challenge that IBM Research undertook to build Watson, a question-answering computer system that would compete at the level of a human champion in real time on the American TV quiz show Jeopardy!. We describe the rules of Jeopardy! and list issues that any contestant faces in competing on Jeopardy!. We describe how IBM's Watson system addresses these issues with its metrics and DeepQA architecture. We briefly describe how Watson performed in the Jeopardy! Challenge shows, and conclude with some thoughts about Watson and its potential applications. [ABSTRACT FROM AUTHOR]

The article discusses the role of big data in cancer research and treatment. Topics include the use of a database of genomic factors in cancer by the analytics firm Foundation Medicine Inc., the creation of the database platform CancerLinQ by the organization American Society of Clinical Oncology (ASCO), and the costs of data analysis in oncology in comparison to cancer drug costs. The business unit IBM Watson Health of the firm International Business Machines Corp. (IBM) is noted.

The article focuses on partnerships of International Business Machines Corp. (IBM)'s Watson computer system with the New York Genome Center, the Cleveland Clinic and the Mayo Clinic to improve the interaction between physicians and speed decision making and its Watson Genomics project. It mentions the aim of the project which is to examine patient's molecular profile and identify drugs for treatment, and the firm's partnership with Pathway Genomics for mobile health application for consumers.