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As technology becomes more sophisticated, there is an increasing demand for flexible operating systems that can automatically respond to emerging challenges. The concept of artificial intelligence (AI) is, therefore, increasingly integral to many of the most exciting technological developments occurring today.

This is a literature review.

Broadly defined, AI is the aspect of computing that enables the machine to take in information about its environment and make choices that help it succeed. This paper will explore some of the key concepts behind AI and demonstrate how they apply to emerging technology.

To get a sense of the importance of AI, we need look no further than Google. Although many companies are working on AI, Google is of particular interest to libraries because its mission to “organize the world’s information and make it universally accessible and useful” shares many similarities with libraries.

AI has been contemplated for as long as humans have considered the nature of their own intelligence and pondered how artificial beings might be able to accomplish similar tasks. The idea of AI can be seen as far back as the ancient Greek myth of Galatea. Philosophers and scientists from René Descarte to Alan Turing have posed questions that are still relevant to modern AI.

Manufacturing small and medium-sized enterprises (SMEs) have already noticed the tangible benefits offered by artificial intelligence (AI). Several approaches have been proposed with a view to support them in the processes entailed in this innovation path. These include multisided platforms created to enable the connection between SMEs and AI developers, making it easier for them to network each other. While such platforms are complex, they facilitate simultaneous interaction with several stakeholders and reaching out to new potential users (both SMEs and AI developers), through a collaboration with supporting ecosystems such as digital innovation hubs (DIHs).

Mixed methods were used. The literature review was performed to identify the existing approaches within and outside the manufacturing domain. Computer-assisted telephonic (in-depth) interviewing , was conducted to include perspectives of AI platform stakeholders and collect primary data from various European countries.

Several challenges and barriers for AI platform stakeholders were identified alongside the corresponding best practices and guidelines on how to address them.

An effective approach was proposed to provide support to the industrial platform managers in this field, by developing guidelines and best practices on how a platform should build its services to support the ecosystem.

This paper aims to center the experiences of three cohorts (n = 40) of Black high school students who participated in a critical race technology course that exposed anti-blackness as the organizing logic and default setting of digital and artificially intelligent technology. This paper centers the voices, experiences and technological innovations of the students, and in doing so, introduces a new type of digital literacy: critical race algorithmic literacy.

Data for this study include student interviews (called “talk backs”), journal reflections and final technology presentations.

Broadly, the data suggests that critical race algorithmic literacies prepare Black students to critically read the algorithmic word (e.g. data, code, machine learning models, etc.) so that they can not only resist and survive, but also rebuild and reimagine the algorithmic world.

While critical race media literacy draws upon critical race theory in education – a theorization of race, and a critique of white supremacy and multiculturalism in schools – critical race algorithmic literacy is rooted in critical race technology theory, which is a theorization of blackness as a technology and a critique of algorithmic anti-blackness as the organizing logic of schools and AI systems.

The diffusion and adoption (D&A) of innovation propels today's technological landscape. Crisis situations, real or perceived, motivate communities of people to take action to adopt and diffuse innovation. The D&A of innovation is an inherently human activity; yet, artificially intelligent techniques can assist humans in six different ways, especially when operating in fifth generation ecosystems that are emergent, complex, and adaptive in nature.

Humans can use artificial intelligence (AI) to match solutions to problems, design for diffusion, identify key roles in social networks, reveal unintended consequences, recommend pathways for scaling that include the effects of policy, and identify trends for fast-follower strategies. The stability of the data that artificially intelligent systems rely upon will challenge performance; nevertheless, the research in this area has positioned several promising techniques where classically narrow AI systems can assist humans. As a result, human and machine interaction can accelerate the D&A of technological innovation to respond to crisis situations.

This chapter provides preliminary evidence to show that financial accounting students would use generative artificial intelligence (AI) tools to improve their learning if given the opportunity to do so by their instructors. Most students who completed the exercises we used in the study did so diligently and modified their answers after using a generative AI tool in a manner that suggests beneficial effects. It appears that the more prior knowledge a student had about the subject matter, the more beneficial was the experience. Pitfalls still exist, however. For example, students without knowledge of the subject matter struggled with crafting queries and judging the efficacy of their answers. Moreover, although a minority, some students tended to duplicate their original answers without utilizing the responses generated by the generative AI tool. Additionally, certain students merely copied the answers generated by the AI tool without providing any additional critique or analysis. Implications for teaching and learning and opportunities for future research are discussed.

Artificial intelligence (AI) might change the communication profession immensely, but the academic discourse is lacking an investigation of the perspective of practitioners on this. This article addresses this research gap. It offers a literature overview and reports about an empirical study on AI in communications, presenting first insights on how professionals in the field assess the technology.

A quantitative cross-national study among 2,689 European communication practitioners investigated four research questions: RQ1 – How much do professionals know about AI and to what extent are they already using AI technologies in their everyday lives? RQ2 – How do professionals rate the impact of AI on communication management? RQ3 – Which challenges do professionals identify for implementing AI in communication management? RQ4 – Which risks do they perceive?

Communication professionals revealed a limited understanding of AI and expected the technology to impact the profession as a whole more than the way their organisations or themselves work. Lack of individual competencies and organisations struggling with different levels of competency and unclear responsibilities were identified as key challenges and risks.

The results highlight the need for communication managers to educate themselves and their teams about the technology and to identify the implementation of AI as a leadership issue.

The article offers the first cross-national quantitative study on AI in communication management. It presents valuable empirical insights on a trending topic in the discipline, highly relevant for both academics and practitioners.

The emergence of the platform economy is reorganizing work, employment, and value creation. The authors argue that the digital platforms are fracturing work itself as the places and types of work are being reorganized into a myriad of platform organized work arrangements with workplaces being potentially anywhere with Internet connectivity. The authors differ from most traditional narratives that focus solely upon either work displacement, a single type of platform-organized value-creating activity, or David Weil’s concentration solely upon the workplace. The authors recognize that even as some work is replaced, other work is being transformed; new work and old work in new arrangements is being created and recreated. The taxonomy begins with the workers employed directly by the platform and its contractors. The authors then introduce the category, platform-mediated work, which we divide into three groups: marketplaces such as Amazon; in-person service provision such as Uber and Airbnb; and remote service provision such as Upwork. The next category, “platform-mediated content creation,” is complex. The authors identify three groups of activities: consignment content creators that include services such as the app stores, YouTube, and Amazon Self-Publishing; non-platform organization content producers, which refers to the enormous number of workers occupied with creating and maintaining websites; and user-generated content which is the non-compensated value creation that ranges from content uploaded to Facebook, Instagram, etc. to reviews on sites such as Yelp. It is only when work and value creation is considered in all of these platform-based manifestations that we can understand the ultimate dimensions of the platform economy and comprehensively understand its implications for work.

Launched in 2014, Amazon's Echo and Echo Dot smart speakers led the category's rapid adoption by households and enabled the penetration of artificial intelligence (AI) voice assistants into the everyday lives of millions of people. By 2019, Alexa the virtual brains behind Amazon's smart speakers was able to play music, create reminders, get weather reports, control lights and other home appliances, shop, and do much more in response to voice commands. Amazon had developed significant new capabilities for Alexa, developed an entire ecosysgtem around it, expanded Alexa's user base to more than 100 million users, and made significant progress in monetizing its digital voice assistant. However, Alexa's progress also created new challenges for Amazon, its Alexa-enabled customers, and society at large. Amazon needed to identify and address these challenges in order to encourage continued consumer acceptance and preclude detrimental government or regulatory action.