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"As organizations grapple with the challenges of a dynamic market, the integration of Artificial Intelligence (AI) emerges not only as a technological progression but a strategic necessity. The transformative potential of AI, particularly through OpenAI, holds the promise of redefining operational paradigms, accelerating innovation, and unlocking unprecedented growth opportunities. However, lurking beneath this promise are challenges that demand urgent attention - from tailoring relevance for specific business units to ethical and safe integration practices. The specifics of how OpenAI can amplify labor productivity and enhance decision-making processes remain elusive. Generative AI and Multifactor Productivity in Business offers a guide surrounding the complexities of OpenAI's role in business operations. It contends that understanding OpenAI is not just beneficial; it is essential for organizations seeking to navigate economic uncertainties and unlock high levels of efficiency and growth.The book delves into the effects of OpenAI on business, with a primary objective of illuminating the scholarly and practitioner-based contributions that push the boundaries of OpenAI in business research. This exploration encompasses applications of advanced generative AI tools, language models, and innovative technologies specific to diverse businesses across sectors, scales, and regions. It emphasizes that as AI becomes more seamlessly integrated into business processes, the potential for multifactor productivity to fuel economic growth, new industries, and job opportunities is unparalleled."--

Nowadays, open innovations such as intelligent automation and digitalization are being adopted by every industry with the help of powerful technology such as Artificial Intelligence (AI). This evolution drives systematic running processes, involves less overhead of managerial activities and increased production rate. However, it also gave birth to different kinds of attacks and security issues at the data storage level and process level. The real-life implementation of such AI-enabled intelligent systems is currently plagued by the lack of security and trust levels in system predictions. Blockchain is a prevailing technology that can help to alleviate the security risks of AI applications. These two technologies are complementing each other as Blockchain can mitigate vulnerabilities in AI, and AI can improve the performance of Blockchain. Many studies are currently being conducted on the applicability of Blockchains for securing intelligent applications in various crucial domains such as healthcare, finance, energy, government, and defense. However, this domain lacks a systematic study that can offer an overarching view of research activities currently going on in applying Blockchains for securing AI-based systems and improving their robustness. This paper presents a bibliometric and literature analysis of how Blockchain provides a security blanket to AI-based systems. Two well-known research databases (Scopus and Web of Science) have been examined for this analytical study and review. The research uncovered that idea proposals in conferences and some articles published in journals make a major contribution. However, there is still a lot of research work to be done to implement real and stable Blockchain-based AI systems.

Nowadays, open innovations such as intelligent automation and digitalization are being adopted by every industry with the help of powerful technology such as Artificial Intelligence (AI). This evolution drives systematic running processes, involves less overhead of managerial activities and increased production rate. However, it also gave birth to different kinds of attacks and security issues at the data storage level and process level. The real-life implementation of such AI-enabled intelligent systems is currently plagued by the lack of security and trust levels in system predictions. Blockchain is a prevailing technology that can help to alleviate the security risks of AI applications. These two technologies are complementing each other as Blockchain can mitigate vulnerabilities in AI, and AI can improve the performance of Blockchain. Many studies are currently being conducted on the applicability of Blockchains for securing intelligent applications in various crucial domains such as healthcare, finance, energy, government, and defense. However, this domain lacks a systematic study that can offer an overarching view of research activities currently going on in applying Blockchains for securing AI-based systems and improving their robustness. This paper presents a bibliometric and literature analysis of how Blockchain provides a security blanket to AI-based systems. Two well-known research databases (Scopus and Web of Science) have been examined for this analytical study and review. The research uncovered that idea proposals in conferences and some articles published in journals make a major contribution. However, there is still a lot of research work to be done to implement real and stable Blockchain-based AI systems.

Open-source model regulation is the hottest debate in AI policy. Despite appeals made to national security, proposed constraints on open-source AI distribution might put defense supply chains at risk. This brief assesses those implications for the Joint Force.

AbstractToday, open source intelligence (OSINT), i.e., information derived from publicly available sources, makes up between 80 and 90 percent of all intelligence activities carried out by Law Enforcement Agencies (LEAs) and intelligence services in the West. Developments in data mining, machine learning, visual forensics and, most importantly, the growing computing power available for commercial use, have enabled OSINT practitioners to speed up, and sometimes even automate, intelligence collection and analysis, obtaining more accurate results more quickly. As the infosphere expands to accommodate ever-increasing online presence, so does the pool of actionable OSINT. These developments raise important concerns in terms of governance, ethical, legal, and social implications (GELSI). New and crucial oversight concerns emerge alongside standard privacy concerns, as some of the more advanced data analysis tools require little to no supervision. This article offers a systematic review of the relevant literature. It analyzes 571 publications to assess the current state of the literature on the use of AI-powered OSINT (and the development of OSINT software) as it relates to the GELSI framework, highlighting potential gaps and suggesting new research directions.

Librarian data stewards can propose an update of the SA 8000 standard by integrating as a crosscutting requirement the data stewardship for open science based on the European FAIR data guidelines and the GDPR directives for open data, pursuing the Agenda 2030 targets: Target 4.6 "Ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracy"; Target 16.6 "Develop effective, accountable and transparent institutions at all levels"; Target 16.10 "Ensure public access to information and protect fundamental freedoms, in accordance with national legislation and international agreements".In the evolution of IoT (internet of things) related apps and tools, data driven algorithms are crucial. We therefore speak of IoD (internet of data) as a source for the experimental development of integrated and innovative applications in the environmental and energy services field, the healthcare sector, and also in the context of Smart Cities. For a correct and transparent management of the micro and macrosystemic information flow it becomes essential to share and aggregate different and interoperable sources.The librarian as data steward has the task of promoting cultural change towards shared research, acting on those who produce and reuse data. To this end, it is necessary to facilitate the technology transfer necessary for open science, through the synergy between stakeholders and data producers. ; I data steward bibliotecari possono proporre un aggiornamento dello standard SA 8000 integrando come requisito trasversale la 'data stewardship per la scienza aperta' basata sui principi dei FAIR data e i dettami del GDPR per gli open data, perseguendo i traguardi dell'Agenda 2030 delle Nazioni unite: Traguardo 4.6 "Assicurarsi che tutti i giovani e una parte sostanziale di adulti, uomini e donne, raggiungano l'alfabetizzazione e l'abilità di calcolo"; Traguardo 16.6 "Sviluppare istituzioni efficaci, responsabili e trasparenti a tutti i livelli"; Traguardo 16.10 "Garantire l'accesso del pubblico alle informazioni e proteggere le libertà fondamentali, in conformità con la legislazione nazionale e con gli accordi internazionali".La data stewardship in ambito scientifico oltrepassa la semplice accessibilità e usabilità del dato, puntando a nuovi livelli di interoperabilità con nuovi sistemi e piattaforme.Nell'evoluzione di app e strumenti legati all'IoT (internet of things o internet delle cose) sono determinanti algoritmi alimentati dai dati. Si parla quindi di IoD (internet of data) come fonte per lo sviluppo sperimentale di applicazioni integrate e innovative nel campo ambientale, sanitario, energetico, con implementazioni anche nelle smart city. Per una corretta e trasparente gestione del flusso informativo microsistemico e macrosistemico diviene indispensabile condividere e aggregare fonti diverse e interoperabili.Il bibliotecario come data steward ha il compito di promuovere il cambiamento culturale verso la ricerca condivisa, agendo su chi produce e su chi riusa i dati. Per questo obiettivo serve agevolare il trasferimento tecnologico necessario per la scienza aperta, tramite la sinergia tra stakeholder e produttori di dati.

Open Source Software (OSS) plays an important role in the digital economy. Yet although software production is amenable to remote collaboration and its outputs are digital, software development seems to cluster geographically in places like Silicon Valley, London, or Berlin. And while OSS activity creates positive externalities which accrue locally through knowledge spillovers and information effects, up-to-date data on the geographic distribution of open source developers is limited. This presents a significant blindspot for policymakers, who often promote OSS at the national level as a cost-saving tool for public sector institutions. We address this gap by geolocating more than half a million active contributors to GitHub in early 2021 at various spatial scales. Compared to results from 2010, we find a significant increase in the share of developers based in Asia, Latin America and Eastern Europe, suggesting a more even spread of OSS developers globally. Within countries, however, we find significant concentration in regions, exceeding the concentration of high-tech employment. Social and economic development indicators predict at most half of regional variation in OSS activity in the EU, suggesting that clusters have idiosyncratic roots. We argue for localized policies to support networks of OSS developers in cities and regions.