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This paper embarks on a comprehensive and in-depth comparative analysis of two prominent artificial intelligence applications: ChatGPT and IBM Watson. Developed by OpenAI, ChatGPT excels in natural language processing, leveraging cutting-edge technology to enable robust conversational interfaces. In contrast, IBM Watson offers a multifaceted suite of AI services that encompass machine learning, advanced data analytics, and sophisticated language processing capabilities. This analysis delves into their intricate technical specifications, exploring the nuances of their respective architectures and computational frameworks. It meticulously evaluates their diverse capabilities, ranging from text generation and comprehension to complex data analysis and predictive modeling. Moreover, the paper critically examines the industry-specific use cases where ChatGPT and IBM Watson demonstrate their strengths and applications. By highlighting real-world examples across various sectors such as healthcare, finance, customer service, and education, it elucidates how each platform can be leveraged to enhance operational efficiency, decision-making processes, and user interaction experiences. Additionally, the analysis forecasts emerging trends in AI development, shedding light on how these technologies are expected to evolve and integrate into broader technological landscapes. [ABSTRACT FROM AUTHOR]

This document provides an overview of the need to update Isaac Asimov's three laws of robotics in order to address the risks associated with incorporating artificial intelligence (AI) into mobile robots. It discusses the ten stages of AI evolution, from basic rule-based systems to highly advanced AI capable of creating life. The document also highlights the advancements in mobile robots, including their ability to see, display emotions, and perform dangerous tasks. It emphasizes the importance of programming robots with good attributes and ensuring they adhere to ethical principles in order to serve humanity. Additionally, the document discusses various robots with AI capabilities and their functions, such as Ameca, Stuntronic, Aquanaut, Pepper, Sophia, and HRP-5P. It emphasizes the need for laws and standards to protect the mobile robot industry and customers, as well as the importance of ethical programming and safeguards for AI-driven robots. [Extracted from the article]

Social-educational robotics, such as NAO humanoid robots with social, anthropomorphic, humanlike features, are tools for learning, education, and addressing developmental disorders (e.g., autism spectrum disorder or ASD) through social and collaborative robotic interactions and interventions. There are significant gaps at the intersection of social robotics and autism research dealing with how robotic technology helps ASD individuals with their social, emotional, and communication needs, and supports teachers who engage with ASD students. This research aims to (a) obtain new scientific knowledge on social-educational robotics by exploring the usage of social robots (especially humanoids) and robotic interventions with ASD students at high schools through an ASD student-teacher co-working with social robot-social robotic interactions triad framework; (b) utilize Business Model Canvas (BMC) methodology for robot design and curriculum development targeted at ASD students; and (c) connect interdisciplinary areas of consumer behavior research, social robotics, and human-robot interaction using customer discovery interviews for bridging the gap between academic research on social robotics on the one hand, and industry development and customers on the other. The customer discovery process in this research results in eight core research propositions delineating the contexts that enable a higher quality learning environment corresponding with ASD students' learning requirements through the use of social robots and preparing them for future learning and workforce environments. [ABSTRACT FROM AUTHOR]

In recent years, education has been closely linked to the continued development of technology, especially smart systems based on the use of artificial intelligence with cognitive capabilities. The emphasis here is on the significant potential of cognitive computing in the domain of education and learning. This association implies a transformative impact on how education is delivered, accessed, and personalized through the integration of advanced cognitive systems in the learning and teaching process. This article conducts an overview of the several cognitive computing technologies in the context of education used to enhance learning and teaching activities. This study identifies three conceptual architectures of these systems, Layered Architecture, Agent-Based Architecture, and Hybrid Architecture; then describes their components. Finally, it explores well-known platforms that are used in the education field namely IBM Watson, Kenwton, Carnegie Learning, and DreamBox Learning. [ABSTRACT FROM AUTHOR]

Target enrichment sequencing techniques are gaining widespread use in the field of genomics, prized for their economic efficiency and swift processing times. However, their success depends on the performance of probes and the evenness of sequencing depth among each probe. To accurately predict probe coverage depth, a model called Deqformer is proposed in this study. Deqformer utilizes the oligonucleotides sequence of each probe, drawing inspiration from Watson–Crick base pairing and incorporating two BERT encoders to capture the underlying information from the forward and reverse probe strands, respectively. The encoded data are combined with a feed-forward network to make precise predictions of sequencing depth. The performance of Deqformer is evaluated on four different datasets: SNP panel with 38 200 probes, lncRNA panel with 2000 probes, synthetic panel with 5899 probes and HD-Marker panel for Yesso scallop with 11 000 probes. The SNP and synthetic panels achieve impressive factor 3 of accuracy (F3acc) of 96.24% and 99.66% in 5-fold cross-validation. F3acc rates of over 87.33% and 72.56% are obtained when training on the SNP panel and evaluating performance on the lncRNA and HD-Marker datasets, respectively. Our analysis reveals that Deqformer effectively captures hybridization patterns, making it robust for accurate predictions in various scenarios. Deqformer leads to a novel perspective for probe design pipeline, aiming to enhance efficiency and effectiveness in probe design tasks. [ABSTRACT FROM AUTHOR]

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]

At present, the reliability of interconnection networks of multiprocessing systems has become a hot topic of research concern for parallel computer systems. Conditional connectivity is an important parameter to measure the reliability of an interconnected network. In reality, the failure of one node will inevitably have a negative impact on the surrounding nodes. Often it is the specific structures that fail in an interconnected network. Therefore, we propose two novel kinds of connectivity, called g-extra H-structure connectivity and g-extra H-substructure connectivity, to go for a more accurate measure of the reliability of the network. Hypercube network is the most dominant interconnection network topology used by computer systems today, for example, the famous parallel computing systems Cray T3D, Cray T3E, IBM Blue Gene, etc. are built with it as the interconnection network topology. In this paper, we obtain the results of the g-extra H-structure connectivity and the g-extra H-substructure connectivity of the hypercubes when the specific structure is Pk and g = 1. [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]

The common used goodeness-of-fit tests are based on the empirical distributions functions (EDF) where distances between empirical and theoretical hypothesized distributions are compared to critical values. The aim of this paper is to provide for different sample sizes, tables of goodness-of-fit critical values of modified Kolmogorov-Smirnov statistic D n , Anderson-Darling statistic A2, Cramer-Von Mises statistic W 2 , Liao and Shimokawa statistic Ln, and Watson statistic U2 for the competing risks model of Bertholon which is used to describe the reliability of real systems where failure times can have different risks and in medical studies to characterize the survival time of patients who can have risks of death from different causes. The power of these statistics is studied using some alternatives such as the exponential, the inverse Weibull, the exponentiated Weibull and the exponentiated exponential distributions. All the computation are carried out by using matlab software and Monte Carlo method. [ABSTRACT FROM AUTHOR]

Background: Kampo medicine is widely used in Japan; however, most physicians and pharmacists have insufficient knowledge and experience in it. Although a chatbot-style system using machine learning and natural language processing has been used in some clinical settings and proven useful, the system developed specifically for the Japanese language using this method has not been validated by research. The purpose of this study is to develop a novel drug information provision system for Kampo medicines using a natural language classifier® (NLC®) based on IBM Watson. Methods: The target Kampo formulas were 33 formulas listed in the 17th revision of the Japanese Pharmacopoeia. The information included in the system comes from the package inserts of Kampo medicines, Manuals for Management of Individual Serious Adverse Drug Reactions, and data on off-label usage. The system developed in this study classifies questions about the drug information of Kampo formulas input by natural language into preset questions and outputs preset answers for the questions. The system uses morphological analysis, synonym conversion by thesaurus, and NLC®. We fine-tuned the information registered into NLC® and increased the thesaurus. To validate the system, 900 validation questions were provided by six pharmacists who were classified into high or low levels of knowledge and experience of Kampo medicines and three pharmacy students. Results: The precision, recall, and F-measure of the system performance were 0.986, 0.915, and 0.949, respectively. The results were stable even with differences in the amount of expertise of the question authors. Conclusions: We developed a system using natural language classification that can give appropriate answers to most of the validation questions. [ABSTRACT FROM AUTHOR]

The invariant Galton–Watson (IGW) tree measures are a one-parameter family of critical Galton–Watson measures invariant with respect to a large class of tree reduction operations. Such operations include the generalized dynamical pruning (also known as hereditary reduction in a real tree setting) that eliminates descendant subtrees according to the value of an arbitrary subtree function that is monotone nondecreasing with respect to an isometry-induced partial tree order. We show that, under a mild regularity condition, the IGW measures are attractors of arbitrary critical Galton–Watson measures with respect to the generalized dynamical pruning. We also derive the distributions of height, length, and size of the IGW trees. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is one of the most fast-evolving and most pervasive technological advancements of the last decade. Driven by IoT's promise for an interconnected world for all people, through a shared Internet-based infrastructure, more and more businesses expand their activities to embrace, competitively, this new concept. More importantly, the ubiquitous, highly granular, and of increasing interconnection and computational capabilities nature of the IoT fabric creates a unique ecosystem for developing powerful, intelligent applications not limited to simply gathering and analyzing environmental and people data. This potential combines the IoT's physical capabilities with the achievements of the more mature discipline of Artificial Intelligence (AI). However, there is still a large number of businesses with no clear plans of investing in the IoT-plus-AI potential, citing multiple obstacles mainly related to the difficulty of understanding, adapting, and developing advanced AI techniques. Our goal is to show that advanced AI functionalities can, now, be easily implemented and operated through existing IoT platforms, by developing an intelligent, remote, surveillance application based on IBM's Cloud and Watson platforms as well as the Raspberry Zero W low-cost IoT device. [ABSTRACT FROM AUTHOR]

Bill Meeker is a Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. One of the strengths of the program was the statistics courses taught by the well-known GE statistician adjunct professors Gerry Hahn and Wayne Nelson! This is probably the reason that when I give a short course at a company, the course fills up quickly and there is a high degree of interest and enthusiasm when the course is ongoing! My last course at Union was a Box-Jenkins time series course that Leo Aroian taught. [Extracted from the article]

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]

The primary goal of the experiment is to unsettle established disciplinary perspectives, even where the same questions could have been addressed more authoritatively in another field. i [1] Introduction Artificial Intelligence (AI) is a research pursuit that appears ideally suited to I Interdisciplinary Science Reviews i , given the ambition of AI to advance both the scientific study of humanity, and of computational machines as a "science of the artificial" (Simon [27]). If we accept this argument for two fundamentally different kinds of AI, what should AI researchers and AI engineers do differently? Keywords: Artificial intelligence; reflective essay; cybernetics; cognitive capitalism EN Artificial intelligence reflective essay cybernetics cognitive capitalism 5 14 10 05/05/23 20230301 NES 230301 I The following essay is a stylistic experiment for Interdisciplinary Science Reviews, reflecting on a personal research agenda and trajectory, in relation to the disciplines that the author has engaged with. [Extracted from the article]

For organizations, the development of new business models and competitive advantages through the integration of artificial intelligence (AI) in business and IT strategies holds considerable promise. The majority of businesses are finding it difficult to take advantage of the opportunities for value creation while other pioneers are successfully utilizing AI. On the basis of the research methodology of Webster and Watson (2020), 139 peer-reviewed articles were discussed. According to the literature, the performance advantages, success criteria, and difficulties of adopting AI have been emphasized in prior research. The results of this review revealed the open issues and topics that call for further research/examination in order to develop AI capabilities and integrate them into business/IT strategies in order to enhance various business value streams. Organizations will only succeed in the digital transformation alignment of the present era by precisely adopting and implementing these new, cutting-edge technologies. Despite the revolutionary potential advantages that AI capabilities may promote, the resource orchestration, along with governance in this dynamic environment, is still complex enough and in the early stages of research regarding the strategic implementation of AI in organizations, which is the issue this review aims to address and, as a result, assist present and future organizations effectively enhance various business value outcomes. [ABSTRACT FROM AUTHOR]

ChatGPT adds to the list of artificial intelligence-based systems designed to perform specific tasks and answer questions by interacting with users (Apple's Siri, Amazon's Alexa, Google's Assistant and Bard, Microsoft's Cortana, IBM's Watson, Bixby from Samsung, among others). ChatGPT works using OpenAI's GPT (Generative Pretrained Transformer) language model and is capable of learning from users' preferences and behavior patterns to customize its response. ChatGPT has the potential to be applied in different fields, including education, journalism, scientific writing, communication, cell biology, and biotechnology, where there is already evidence. The aim of this work was to analyze the possible applications of ChatGPT in the agricultural and livestock industry. First, a scientometric analysis was performed with VosViewer and Bibliometrix (Bliblioshiny). 3 clusters were identified: (a) Main characteristics; (b) learning systems you use; and (c) applications. To the question: What are the main applications in which ChatGTP will revolutionize agriculture (or livestock) in the world? ChatGPT responded: (a) in the agricultural field: improvement of agricultural decision-making, optimization of agricultural production, detection and prevention of plant diseases, climate management, and supply chain management; and (b) in the livestock field: improvement of animal health and welfare, optimization of animal production, supply chain management, detection and prevention of zoonotic diseases, and climate management for animal production. ChatGPT does not scientifically support its answer, but from the analysis carried out, we find that there is enough scientific evidence to conclude, in this case, that its answers were correct. While ChatGPT does not necessarily scientifically substantiate its answers, users should. There is a lack of studies on the use of Artificial Intelligence and its relationship with ethics. [ABSTRACT FROM AUTHOR]

This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data (i.e., observations are axes of direction) that can be partitioned into multiple groups, where each observation within a group is sampled from a mixture of Watson distributions with an infinite number of components that are allowed to be shared across different groups. First, we propose a hierarchical nonparametric Bayesian model for modeling grouped axial data based on the hierarchical Pitman-Yor process mixture model of Watson distributions. Then, we demonstrate that by setting the discount parameters of the proposed model to 0, another hierarchical nonparametric Bayesian model based on hierarchical Dirichlet process can be derived for modeling axial data. To learn the proposed models, we systematically develop a closed-form optimization algorithm based on the collapsed variational Bayes (CVB) inference. Furthermore, to ensure the convergence of the proposed learning algorithm, an annealing mechanism is introduced to the framework of CVB inference, leading to an averaged collapsed variational Bayes inference strategy. The merits of the proposed models for modeling grouped axial data are demonstrated through experiments on both synthetic data and real-world applications involving gene expression data clustering and depth image analysis. [ABSTRACT FROM AUTHOR]

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]

This paper deals with the problem of goodness-of-fit tests (GFTs) for the weighted generalized quasi-Lindley distribution (WGQLD) using ranked set sampling (RSS) and simple random sampling (SRS) techniques. The tests are based on the empirical distribution function and sample entropy. These tests include the Kullback–Leibler, Kolomogorov–Smirnov, Anderson–Darling, Cramér–von Mises, Zhang, Liao, and Shimokawa, and Watson tests. The critical values (CV) and power of each test are obtained based on a simulation study by using SRS and RSS methods considering various sample sizes and alternatives. A rain data set is used to investigate the effectiveness of the suggested GFTs. Based on the same number of measured units for the various alternatives taken into consideration in this study, it is discovered that the RSS tests are more effective than those of their rivals in SRS. Additionally, as the set size increases, the GFTs' power increases. [ABSTRACT FROM AUTHOR]

The article presents the author's views on the artificial intelligence-supported medical coding module, which can help select the most appropriate codes for adverse event detection. The progress of the technology and improvement of computer-assisted coding capabilities with the help of machine learning and natural language processing approaches will help evolve the medical coding workflow. It emphasizes addressing the opportunity to evaluate more diseases and clinical trials.

The potential threats of autonomy One of the obvious threats of autonomy, which lies in its form as, prospectively, one of the most powerful expressions of technology, is its influence upon evolution; both human and technical. Any real-world challenges that can present the need for an I exact i and I deterministic i expression of intent will already be an obvious candidate for precise automation and so probably not a candidate for exploratory forms of autonomy (and see Hancock, [41], [42]). The "isles of autonomy" metaphor initially casts humans in the littoral role of the beaches and riparian shorelines that surround such emerging island (i.e., the outer boundary layer of emerging autonomies, as they "rise" above the ocean of extant automation). Challenges to formal methods assessments It would be both desirable and actually rather gratifying then if, in counter to each of these prospective weaknesses, we could specify a variety of provably effective formal methods which would test and indemnify us against any untoward outcomes of a singular or interactive group of autonomous systems. The potential promises of autonomy The vista of autonomy's promise is only circumscribed by the limits of the advocates' imagination that such envisaged autonomous systems can underwrite (Arkin, [4]), see Figure 3. [Extracted from the article]

Over the past decade, corporations such as Microsoft and IBM have become increasingly involved in open source software projects and development platforms. In this study, we examine motivations behind contributions in OSS platforms that have increasingly come under commercial influence. Empirical analysis of 2017 GitHub Open Source Survey data of over 5,500 randomly selected OSS developers indicated that carrots (tangible motivations) and rainbows (value-based factors) play their role. However, carrots are relatively more important. [ABSTRACT FROM AUTHOR]

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]

Advanced medical imaging is necessary for individual Precision Health; qualitative methods are necessary for population Precision Health. The Precision Health movement offers the promise of highly accurate, tailored health solutions for individuals and, potentially, populations. A Practical Path to Take: Qualitative Data on its Own for Population Precision Health Population Precision Health is, arguably, the least defined aspect of the Precision Health movement. [Extracted from the article]

Due to the introduction and rapid ubiquity of artificial intelligence (AI ) and AI -integrated programs that can be used by students and teachers, educational scholarship evaluating the capabilities of AI is needed. This study evaluates the abilities of three prominent AI programs --ChatGPT, Microsoft's Bing, and Google's Bard -- to create high school lesson plans on the subjects of Martin Luther King, Jr., the Indian Removal Act, and climate change. The authors judge the quality of the lessons' content based on scholarship in the education field and document the process of prompting the AI to produce lessons more in line with these criteria. In a 2011 episode of the television game show Jeopardy!, Ken Jennings played against IBM's Watson, an artificial intelligence (AI) designed to assist in the automation of business tasks (IBM, n.d). Jennings still holds the record for the highest average correct responses as well as the longest winning streak on that show with 74 consecutive wins. Despite his human prowess at trivia, he and another high scoring jeopardy champion, Ken Rutter, lost to Watson. Realizing his inevitable defeat, Jennings wrote underneath his Final Jeopardy answer, "I for one welcome our new computer overlords" (Hiskey, 2012). In the years since that Jeopardy match, AI science has continued to evolve, enabling computers to process human language more fluidly and complete a myriad of tasks. In November 2022, a company called Open AI released ChatGPT, an AI "chatbot." ChatGPT describes itself as "a language model developed by OpenAI, which uses deep learning techniques to generate human-like text based on the input it receives. I t is designed to respond to questions, complete text based on the prompt, summarize long text, and perform various other language-related tasks" (OpenAI, 2023a). Soon after, Microsoft released a ChatGPT-based AI integrated into their Bing search engine, and Google released Bard. (At the time of data collection for this study, Microsoft's AI was integrated into the Bing search engine and was not branded as a separate product, but prior to publication the name was changed to Copilot as part of Microsoft's strategy to compete with ChatGPT ; Warren, 2023). In addition, a myriad of AI -based platforms tailored for specific applications have been released. Educators might see these AIs as tools for malfeasance. Many educators, for example, worry that students will use AI to cheat, as it is capable of producing answers sophisticated enough to pass exams in both law schools and business schools (Kelly, 2023). Given that AI is increasingly prevalent in tech circles and is being integrated into everyday technological tools such as search engines, educators will need to adapt to its presence. This paper focuses on the lesson planning abilities of three widely available and free-to-use AIs and examines the extent to which social studies educators can use AIs as a tool to create lesson plans that challenge students to think critically about social studies subjects. Using someone else's lesson plans can be hit or miss -- so much depends on the creator, whom the teacher may or may not know. Online marketplaces like T eachers Pay T eachers, as well as social media platforms like Pinterest, are widely used and can contain not only inadequate but also harmful narratives about topics and issues vital to social studies, such as the Civil Rights Movement (Rodríguez et al., 2020). AI also offers the promise of quickly generated lessons. Early explorations of ChatGPT for teaching purposes by publications such as Education Week have emphasized producing basic outlines to serve as a starting point for lessons, while noting that it can reduce the time burden of some of the more menial teaching tasks, such as grading and writing letters of recommendation (Mallon, 2023; Will, 2023). Y et, with the focus on time-saving affordances of the new technology, little attention has been paid to the quality of the content in lessons produced by AI and whether AI produces lessons that repeat problematic framings. Given that large language models, the basis of the three AI we investigated, are trained using Internet content, there is a risk that AI asked to produce educational content will merely replicate potentially problematic discourses commonly found online on a variety of topics. Our goal in this study was to contribute to emerging understandings of the potential for AI in social studies classrooms. Specifically, we created and used rubrics to assess AI 's lesson planning ability when addressing topics whose coverage in schools has been subject to critique. Since many narratives commonly found in education are seen to serve the interests of dominant social or corporate groups, we chose to focus on how the content of the lessons was framed. We engaged AI to produce lessons on two historical topics, the Civil Rights movement and Indian Removal, where both standards and texts have often reinforced problematic White or settler-colonial narratives and power structures (Sabzalian et al., 2021; Shear et. al, 2015; Woodson, 2016). We also asked ChatGPT, Bing, and Bard to create a lesson on a third topic, climate change, where student learning is often complicated by corporate-initiated discourses of climate denial (Damico & Baildon, 2022). Our investigations were framed around the following questions: 1. To what extent can ChatGPT, Bing, and Bard generate robustly critical lesson plans when prompted to on the subjects of Martin Luther King, Jr., Indian Removal, and climate change? 2. What types of prompting can help AI avoid potentially problematic discourses around these three topics? [ABSTRACT FROM AUTHOR]

The Internet of Things has contributed to make smarter houses and buildings in the last decades. Different existing works already integrate IoT technologies in homes, but end-user needs continuously change and researchers must face this challenge in identifying platforms to fast prototype solutions satisfying these new needs. This paper presents a solution that demonstrates how well-known fast-prototyping technologies like Node-RED, IBM Watson, Telegram, Raspberry Pi 4, and secured MQTT can contribute to develop complex systems facing the challenge. The selected tools are used within a smart home context to support features inspired by people needs and allow users to: a) consult real time conditions (i.e., temperature, humidity, gas), b) remotely manage lights, c) save energy through a light management system based on user movements, d) remotely monitor the house through dedicated webcams, e) generate warning notifications in case of danger. Users can interact with the systems through a web Node-RED dashboard and a Telegram bot. Differently from existing works, the feasibility of the implemented system and the efficacy of the exploited platforms are demonstrated through a running scenario extracted from a consolidated study on user needs in smart homes. The performed experiment can facilitate the fast prototyping of new solutions. [ABSTRACT FROM AUTHOR]

This essay maps IBM's attempts to construct a typology of high-tech 'New Collar' work and leverage policymaking outcomes to underwrite IBM corporate ventures capable of materializing this work. Through a discursive analysis of IBM corporate texts, webpages, and the 2017 New Collar Jobs Act, I argue for New Collar work to be understood through the lens of autonomy, as IBM recasts notions of 'autonomous' technology onto humans by downplaying dystopic associations of technological autonomy and transferring notions of autonomy to human workers. In doing so, I account for IBM's use of 'augmentation' to situate human intelligence as the cognitive force uplifted by work performed with artificial intelligence. By pairing human augmentation with posthumanist conceptions of 'distributed cognition', IBM centers human intelligence through a redistributed cognition that reverses posthumanism's decentering of human supremacy. Following from this, I unpack 'New Collar' as a reinvention of 'white' and 'blue' collar dichotomies and New Collar work as the grounds for tech workers to reinvent themselves. In this way, by minimizing the necessity of 4-year college degrees as pathways to economic and professional mobility, IBM constructs 'New Collar' with embedded notions of enlarged self-determination for applied worker intellect, vocational training, and employability. Under the aegis of creating, training, and employing New Collar workers, IBM pursues policy outcomes to underwrite corporate ventures related to New Collar work and bolster its institutional autonomy amidst marketplaces of cognitive capitalism. By outlining how tax relief provisions of the New Collar Jobs Act correlate with neoliberal ideologies of legislators and IBM investments in public–private vocational models and cybersecurity platforms, I account for IBM's elongated 'economy of learning' that enables the company to more thoroughly capture, underwrite, and commodify New Collar cognition from training to market outputs. [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]