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The author offers his opinion on the current state of artificial intelligence (AI) and the so-called "AI winter" that happened after the 1980s. He explores the use of neural networks and notes that until recently they would have been grouped with machine learning, deep learning, or big data, rather than AI.

The article discusses the importance of protecting sensitive information in large datasets and the limitations of de-identification techniques, using the example of Chilean Electoral Service data being re-identified. Differential Privacy (DP) is introduced as a robust solution to privacy concerns, ensuring that adding or removing an individual's data has a negligible impact on the outcome. The article highlights the development of programming languages and type systems, like Fuzz and Jazz, which help achieve DP by incorporating mechanisms to handle sensitivity and noise addition, with a focus on gradual typing to ease the adoption of these techniques.

Purpose: Artificial intelligence (AI) is increasingly applied by businesses to optimize their processes and decision-making, develop effective and efficient strategies, and positively influence customer behaviors. Businesses use AI to generate behaviors such as customer retention (CR). The existing literature on "AI and CR" is vastly scattered. The paper aims to review the present research on AI in CR systematically and suggest future research directions to further develop the field. Design/methodology/approach: The Scopus database is used to collect the data for systematic review and bibliometric analysis using the VOSviewer tool. The paper performs the following analysis: (1) year-wise publications and citations, (2) co-authorship analysis of authors, countries, and affiliations, (3) citation analysis of articles and journals, (4) co-occurrence visualization of binding terms, and (5) bibliographic coupling of articles. Findings: Five research themes are identified, namely, (1) AI and customer churn prediction in CR, (2) AI and customer service experience in CR, (3) AI and customer sentiment analysis in CR, (4) AI and customer (big data) analytics in CR, and (5) AI privacy and ethical concerns in CR. Based on the research themes, fifteen future research objectives and a future research framework are suggested. Research limitations/implications: The paper has important implications for researchers and managers as it reveals vital insights into the latest trends and paths in AI-CR research and practices. It focuses on privacy and ethical issues of AI; hence, it will help the government develop policies for sustainable AI adoption for CR. Originality/value: To the author's best knowledge, this paper is the first attempt to comprehensively review the existing research on "AI and CR" using bibliometric analysis. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to determine the role of intellectual capital (IC) on financial performance (FP), the mediating effect of high-performance work practices (HPWPs) and the moderating role of big data analytics on the banking sector operating in Pakistan. Design/methodology/approach: This study collected quantitative and cross-sectional data using structured questionnaire forms distributed to selected targeted respondents using a convenient sampling technique. The 518 useable questionnaires were analysed using the SmartPLS software through a structural equation modelling technique to test the proposed research hypotheses. Findings: The study results revealed that IC has shown an impact on FP. The role of HPWP significantly mediates between IC and FP, while the moderating role of big data analytics remains insignificant. Practical implications: This study highlights IC's role in enhancing FP in the Pakistani banking sector. It will bring a higher quality IC in the banking sector, and they will be better positioned to serve the community. Policymakers need to invest in IC and HPWP and utilise BDA, which will boost FP and uplift the quality of the banking sector. Originality/value: This study extends the concept of IC and adds the theoretical role of HPWPs and big data analytics in the literature on IC, along with the contextual application. The study also develops a unique role of the integrated IC model theory with a relationship to the banking sector of Pakistan. [ABSTRACT FROM AUTHOR]

The AFS Industry 4.0 Survey provides insights into the current state of Industry 4.0 practices in the foundry and casting industry. The survey found that automation/robots and data analytics are the top priorities for implementing Industry 4.0 technology in foundries. Other tools and technologies mentioned include simulation, system integration, IoT, cybersecurity, the cloud, additive manufacturing, AR, and big data. The survey also highlights the departments involved in implementing Industry 4.0, the areas where data is collected, and the tools used for data analysis and visualization. The use of Industry 4.0 technologies can help foundries increase efficiency and improve decision-making, but there are challenges to implementation such as a lack of qualified employees and cost justification. Management involvement and education on Industry 4.0 are also important factors. [Extracted from the article]

This article presents a discussion with textbook authors Alfred Spector, Peter Norvig, Chris Wiggins and Jeanette Wing with venture partner Ben Fried and software engineer Michael Tingley about data science. Topics include the potential problems when data science is not utilized properly, establishing and executing an analysis rubric, and the authors’ upcoming book, Data Science in Context.

The article explores processing-in-memory (PiM), an emerging means of avoiding data movement bottlenecks. Particular focus is given to convolutional neural networks (CNNs). Other topics discussed include the increasing popularity of IoT (internet of things) technology, which will require inexpensive computation capable of crunching large volumes of data, and insights on machine learning (ML) solutions for data challenges.

The Department of Defense (DOD) faces a crucial challenge in achieving its goal of joint all-domain operations due to the inability to achieve foundational, fast-paced evolution of data storage, management, [...]

Using complexity economics and chaos theory, J. Doyne Farmer leverages big data to forecast everything from the stock market to roulette. What if we could predict the economy the way [...]

EN PORTADA / La mejor manera de tomar decisiones / PERCEPCIÓN LIDIANDO CON EL BIG DATA El gran caudal de datos sensoriales es cocinado por el cerebro, que a veces [...]

Byline: Brian Johnson After years of planning, a major data center project appears to be on the cusp of construction in Chaska. CloudHQ will go before the Chaska Planning Commission [...]

2024 SEP 28 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- New research on Information Technology - Data Analytics is the subject of [...]

Byline: Saurabh Maral The Digital Twins concept that necessitated building a virtual replica of real assets, wait, no, of processes, or systems has become one of the most adopted technologies [...]

Envision Pharma Group, a leading global technology solutions partner, has launched its new data and analytics management solution, EnvisionOne Insights, to harness the full power of big data in medical [...]

Economic Responsibility Audit (ERA) of China is a unique audit system in the world. To explore and predict the Chinese economy responsibility audit, this study conducts a multi-dimensional analysis of research literature on ERA from 1986 to 2022 based on knowledge graph. Using Citespace software, we established a sound theoretical and a scientific evaluation system of ERA, which carry out an effective data application. This analysis unveils the intricate tapestry of research themes within the realm of ERA, with particular emphasis on the establishment of a robust theoretical foundation, the identification of pivotal audit focal points, the construction of a rigorous evaluation framework, and the adept utilization of data for effective decision-making. The research concluded that the trend of ERA is deeply integrated with performance management, national governance, Big data, etc.​ In addition, we also point out that applying interdisciplinary knowledge system to establish an innovative thinking system is the key point to the ERA's future research. The research results also demonstrate that learning from the mature experience of international audit field and the forefront of theoretical research can effectively promote relevant empirical research. [ABSTRACT FROM AUTHOR]

The article focuses on the implications and potential of Artificial Intelligence (AI) in genealogical research, particularly its role in assisting patrons and professionals in library settings. Topics include the integration of AI in genealogy through tools like large language models and deep learning, the opportunities AI offers for processing vast data sets and creating family histories, and the challenges of verifying AI-generated outputs to ensure accuracy and reliability.

The article focuses on the importance of effective data management in enhancing business operations, decision-making, and innovation. Topics include challenges in data management such as data silos and regulatory compliance, the growth and trends in global data creation and management, and the essential components of next-generation data capabilities like agile data delivery, real-time analytics, and data governance.

The article offers insights on how to overcome the "data bottleneck" of obtaining data for machine-learning (ML) applications. Particular focus is given to a comprehensive taxonomy of ways to tackle this "data bottleneck." Methods discussed include dataset repurposing (using a preexisting dataset for a different task than it was originally constructed for), data augmentation (artificial inflation of the training set through the application of modifications) and multimodal learning (attempts to enrich the input to the learning algorithm).

Purpose: With big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM). Design/methodology/approach: The factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM). Findings: A total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM. Research limitations/implications: This research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model. Practical implications: This research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project. Social implications: The result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society. Originality/value: This research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions. [ABSTRACT FROM AUTHOR]

The article focuses on expanding the discussion from previous articles to include the Feature Effectiveness Measure (FEM), which evaluates software feature performance across key categories like daily active users (DAU), session duration (SD), conversion rate (CR), and customer lifetime value (CLV). It provides a comprehensive assessment of feature success, serving as a benchmark for evaluating performance over time.

Whenever a new technology alters the relations of knowledge production, we are met with a dispute over the political and epistemological meanings of the change. This article offers an alternative look, neither fatalistic nor messianic, at the relationship between information technology and education. In the first part we demonstrate the presence of a widespread governmentality in the contemporary society, resulting from a totalizing collection of data, which we access through AI-mediated interfaces such as ChatGPT. In the second part we argue for the indiscernibility between the production of governmentality and the exercise of power. In the third part we examine how the omnipresence of machines and their improvisational inability make a change of meaning in educational practice indispensable. We conclude with the belief that there is always something uncatchable in human action and to this education must pay attention to. [ABSTRACT FROM AUTHOR]

The article delves into the significance of Big Data and Hadoop in handling the massive volume of data generated worldwide, emphasizing their roles in gaining insights for businesses across various sectors. It also discusses the key components of the Hadoop ecosystem, such as HDFS, MapReduce, and YARN, highlighting their functionalities in distributed data processing and storage.

This article discusses the need for privacy laws specifically designed for artificial intelligence (AI) systems. The U.S. president signed an Executive Order recognizing the importance of responsible AI and outlining principles for trustworthy AI, including protecting privacy and civil liberties. The National Institute of Standards provides guidelines for AI trustworthiness, which include privacy as a key factor. The article emphasizes the importance of transparency, accountability, and explainability in AI systems, as well as addressing issues such as bias, data minimization, and cross-border data transfers. It also highlights the need for proactive and dynamic regulatory frameworks to protect user privacy in the face of AI advancements. [Extracted from the article]

This essay discusses challenges facing energy policy makers in the Big Data analytics era, including how best to promote new approaches to energy delivery arising from the industry's embrace of digitalization, and how to reconcile differences in resource reporting arising from disparate rates of digitization and inconsistent data handling. [ABSTRACT FROM AUTHOR]

Byline: Hariom Seth Most companies are applying data driven decision making to boost the organisation's productivity. An increasing number of companies are utilising big data and predictive analytics. This has [...]

Byline: kgi-admin The global consumer industry experienced a 20% drop in new job postings related to big data in Q2 2024 compared with the previous quarter, according to GlobalData’s Job [...]

Byline: admin India's healthcare sector is undergoing a surge in digital transformation, driven by a strong digital infrastructure and a growing economy. According to the latest 'SME Digital Insights' study [...]

Byline: Kajal Mehra Routematic, under the leadership of Founder & CEO Sriram Kannan, is revolutionizing employee transportation in Indian cities. By leveraging AI, big data, and sustainable practices, Routematic not [...]

Big data and artificial intelligence are rapidly transforming public education in ways that hold promise and peril. Educators increasingly see the potential of AI and big data--defined as complex information [...]

There has been so much talk lately about the importance of harvesting 'big data' that it seems people may feel that if they don't have big data, cannot process big [...]

Customer data needs to be firewalled and if protected properly can still be used for valuable analytics In 2006, British mathematician Clive Humby declared that data is the new oil--and [...]

La Antigua Guatemala, Sacatepéquez. While Guatemala’s well-known volcanoes and Mayan archeological treasures will continue to pull in the crowds, the country’s tourism authorities say it is time to modernize the [...]

PAR KHEIRA BETTAYEB (SHUTTERSTOCK) Dix années! C’est l’espérance de vie gagnée en adoptant une alimentation plus saine à partir de l’âge de 40 ans, selon une étude parue fin 2023 [...]

This article discusses the challenges faced by industrial companies in integrating operational technology (OT) and information technology (IT) systems to leverage artificial intelligence (AI) and big data analytics. The article highlights the differences in data requirements between OT and IT and emphasizes the need for a solution that can bridge this gap. The use of the MQTT Sparkplug protocol is proposed as a way to enable data connectivity and facilitate the flow of data from the edge to the cloud. By leveraging open standards like MQTT Sparkplug, industrial companies can achieve interoperability and integration without extensive programming, leading to improved operations and increased productivity. [Extracted from the article]

We’ve all seen the signs in front of McDonald’s announcing “Over X Billion Served” and have watched the number rise over the years. But tracking how many burgers are sold every day, month, or year is a relic of the past. Today ask: Do we know where each consumer buys her burgers? At what time? What does she drink with it? What does she do before or after buying a burger? How can we satisfy more of her needs so that she keeps coming back? Datagraphs capture this information, helping to reshape competition in every sector. Leaders must invest in upgrading their data architecture to enable a real-time, comprehensive view of how consumers interact with their products and services so that they can develop unique ways to solve customer problems. [ABSTRACT FROM AUTHOR]