Book Reviews: AI & Emerging Technologies
Published on September 18, 2025
The Economy of Algorithms: AI and the Rise of the Digital Minions
Marek Kowalkiewicz. Bristol, United Kingdom: Bristol University Press. 2024. 224 pages.
Index Terms—artificial intelligence, ethical AI practices, digital economy, adaptive economic strategies
Reviewed by Fahrino Ahmad, Student, Universitas Gadjah Mada, supported by Lembaga Pengelola Dana Pendidikan (LPDP), Indonesia.
The Economy of Algorithms: AI and the Rise of the Digital Minions explores the impact of artificial intelligence (AI) on society, the economy, and human agency aiming to demystify algorithms and enhance accessibility. It begins with examples such as United Kingdom (UK) students protesting algorithmic decisions, illustrating how algorithms influence human destinies. The book highlights a shift from viewing algorithms as tools to understanding their profound social implications, advocating for nuanced engagement. By integrating diverse expert perspectives, it encourages readers to actively engage in AI discussions.
The book bridges the gap between AI’s technical foundations and public understanding showing how seemingly neutral algorithms profoundly affect education, employment, and justice. It delves into AI’s economic impact reshaping industries, labor markets, and the global economy. It calls for critical engagement with AI empowering individuals to navigate the digital landscape with awareness and agency.
Marek Kowalkiewicz employs a multifaceted methodological approach to explore the dynamics between AI and algorithms. His analysis combines qualitative research, case studies like the launch of Amazon Web Services (AWS), and theoretical exploration. The acknowledgment section highlights collaborative feedback from experts demonstrating a qualitative research methodology. Theoretical exploration includes discussions on algorithms’ role in decision-making and their social impacts linking AI ethics, technology policy, and digital sociology. Kowalkiewicz suggests a comprehensive approach integrating qualitative research, case study analysis, and theoretical exploration offering nuanced insights into AI’s roles and impacts across various domains.
Algorithms are transforming the economic landscape by evolving from tools to active decision-makers and transaction facilitators, significantly enhancing competitiveness and profitability for companies. The decision-makers and transaction facilitators not only unlock new economic opportunities and stimulate innovative business models, but they also demonstrate the ability to independently participate in economic activities. This paradigm shift towards an algorithmic economy challenges traditional economic concepts prompting a critical reassessment of theories and practices. Establishing a robust framework of regulation, accountability, and ethics is essential to ensuring fair, responsible economic outcomes from algorithms. Their pervasive influence extends across economic entities, driving efficiency and innovation within organizations, empowering individuals, and reshaping market dynamics. As algorithms continue to advance, their profound impact underscores the ongoing need for adaptive economic strategies and policies ushering in a new era where technology and economics converge to shape future economic activities.
This research offers insights into AI and algorithms’ transformative potential emphasizing productivity, innovation, and their ability to enhance human capabilities. It emphasizes adaptability and ethical considerations in the digital age suggesting more specific guidance on implementing successful AI strategies and a nuanced exploration of AI’s impact across sectors and communities for equitable access.
The Economy of Algorithms addresses a broad audience, exploring topics relevant to diverse groups, including ethical implications for academics, business strategies for industry leaders, and the need for regulatory frameworks for policymakers. The research also aims to educate the public about AI’s transformative influence on everyday life.
Why AI Undermines Democracy and What to Do About It
Mark Coeckelbergh. New York, NY: Polity Press. 2024. 162 pages, including index.
Index Terms—ethical AI practices, political philosophy, AI regulation
Reviewed by Joshua Riddle, Program and Proposal Coordinator, University of Alabama–Huntsville.
In Why AI Undermines Democracy and What to Do About It, Mark Coeckelbergh argues AI imperils democracy, particularly how citizens relate to one another, democracy’s epistemic bases, and the “common good” (p. vii). Through the first five chapters, Coeckelbergh succinctly weaves together political philosophy and AI’s real abuses (including unjust criminal profiling, government surveillance, and manipulating voters’ election choices) to show how the technology has great potential to undermine democratic principles like “liberty, equality, fraternity, rule of law, and tolerance” (p. 40). Chapters six through eight show, however, that AI’s path is not deterministic. By bridging STEM and the humanities, as well as reforming government and more effectively regulating Big Tech, there can be AI that presents civic knowledge and common scientific truths, all forging stronger democratic communities.
A particular concern in this book is what role this more democratic AI will play, and who will create it—the common people, or tech experts and legislators. Coeckelbergh sees AI as a means of communication, which is not merely a means to transfer information, but a way to bring people together. Therefore, AI must be controlled as a civic tool to promote a common reality, and thereby create a “common good,” which is debated and created, not a predetermined ideal. He notes significant legislative steps toward this with the European Union’s AI Act, and also the proposed Blueprint for an AI Bill of Rights in the United States. While Coeckelbergh believes top-down institutional reform is necessary to achieve these goals, democratic AI is our responsibility: “We are the politicians. Restoring trust, changing our political institutions in a more democratic direction, and creating and using democratically good AI (for example, AI that is transparent and explainable) is the responsibility of all of us” (p. 67).
Technical communicators should consider Coeckelbergh’s analysis, even if they disagree with his more philosophical approach and conclusions. While the book relies heavily on political philosophy, argues for sweeping cultural and institutional changes, and focuses more on what AI can do to upend democracy than on real-world examples, it effectively presents the underlying humanistic and social problems AI poses, which effects everyone. Especially significant is the desire to build a newer education model that brings STEM and the humanities together, a goal any technical communicator should welcome.
While so much current discourse predominantly focuses on AI’s ability to generate swaths of text, steal artists’ work, manipulate videos with deepfakes, and alter the technical communication field, Why AI Undermines Democracy and What to Do About It is prescient and welcome, offering a wider perspective on AI’s capabilities at the very heart of our society. If we value democracy, we should care about what AI can do and what we can do to control it for our common good.
How AI Ate the World: A Brief History of Artificial Intelligence – and Its Long Future
Chris Stokel-Walker. Surrey, United Kingdom. Canbury Press. 2024. 320 pages, including index.
Index Terms—artificial intelligence, ethical AI practices, AI regulations
Reviewed by Nikki Ferrell, Adjunct Instructor, Miami University.
Chris Stokel-Walker’s How AI Ate the World: A Brief History of Artificial Intelligence – and Its Long Future, despite its provocative title, is a measured primer on artificial intelligence (AI) as we know it in 2024. His straightforward, articulate writing style offers an accessible and enlightening explanation of how we got to today’s state of AI. Stokel-Walker rationally explains complex ethical issues and includes opposing viewpoints, giving readers space to draw their own conclusions about the impact of AI. How AI Ate the World is a useful, digestible handbook for casual readers and AI scholars alike who want to establish or build on foundational knowledge about AI and gain valuable insights into today’s important debates surrounding AI issues and ethics.
The book’s first part is elevated from dry chronicle to engaging narrative by Stokel-Walker, whose storytelling is peppered with meaningful context and interesting anecdotes. Readers learn AI’s post-World War II origins and follow its ebbs and flows through the Cold War-era arms (and technology) race, its successes (and failures) at the World Computer Chess Championships in the late 1980s and early 1990s, its dramatic advances resulting from the AI chip war of the late 2010s, and its latest turning point: the 2017 introduction of the transformer into AI technology.
The second lays out specific impacts of AI since its explosion into consumer software in the early 2020s. Stokel-Walker explores AI art, the technology’s power-guzzling impact on the environment, biases, and dis- and misinformation by intertwining insight from big names in AI with relatable anecdotes of everyday folks finding themselves impacted by AI.
The book’s final part performs a deeper analysis on AI’s impact on humanity: how will human creativity endure as AI gets closer to producing perfectly passable movies, music, and books? How can governments protect humanity from AI when independent, open-source developers are lapping the big corporations, making regulation a game of whack-a-mole? And how big of a role should governments have in AI regulation, anyway?
Though Stokel-Walker stops short of answering these questions definitively, his examination of AI makes a compelling case for regulating the technology and doing so quickly.
The Theory and Practice of Artificial Intelligence: A Handbook for Beginners to Experts in Machine Learning, Deep Learning, NLP, Generative AI and Agentic AI Using Python
Dibyendu Banerjee, Sourav Kairi, and Shatabdi Mondal. Mumbai, India: Shroff Publishers & Distributors Pvt. Ltd., 2025. 848 pages.
Index Terms — artificial intelligence, deep learning, generative AI, machine learning, natural language processing, agentic AI
Reviewed by Swapan Bhattacharyya – Director, Centre for Distance and Online Education, The University of Burdwan, IEEE member (swapanbhattacharyya@ieee.org).
Artificial intelligence (AI) has become one of the most influential, extensive theories of science and technology today, and is more than just technological knowledge as it requires both multidisciplinary knowledge and application. In this context, The Theory and Practice of Artificial Intelligence: A Handbook for Beginners to Experts in Machine Learning, Deep Learning, NLP, Generative AI and Agentic AI Using Python by Dibyendu Banerjee, Sourav Kairi, and Shatabdi Mondal is a timely, valuable resource for education and professional development in AI. The book effectively addresses theoretical implications while providing context for applications, directly connecting with the IEEE community and other professionals in systems development, technical communication, and applied machine learning.
This book is distinguished by its pedagogical explicitness and progression. It is organized into four units—Machine Learning, Deep Learning, Natural Language Processing (NLP), and Generative AI—and provides a logical, practical learning path, moving readers from basic principles through to complex AI systems. The book has a broad audience, from engineering students and self-taught practitioners to experienced practitioners who want to learn about a quickly changing field.
The authors begin with Unit I: Machine Learning and introduce all the well-known algorithms including linear regression, logistic regression, decision trees, and support vector machines. The reader should first think about the topics in an intuitive way, and then work through formal definitions, mathematical derivations, and illustrations of what the algorithms are needed to do in a practical sense. The Python code is only presented after the theory, but it allows the reader to engage with the formal models immediately and evaluate their own understanding of the theories presented.
Unit II: Deep Learning transitions very nicely into neural networks, convolutional models, and recurrent models. Some topics include backpropagation, activation functions, and loss optimizers, and explanations of these topics are presented on both theory and applied practice. The “Deep Reinforcement Learning” chapter was especially good as it is often described as the most difficult subfield within the broader deep learning field ; however, it was presented in a clear, balanced way so that it helped both beginners and experienced readers with their conceptual intuitions while following a controlled algorithmic process.
Unit III: Natural Language Processing was organized and well-prepared, with a well-defined exploration of the most landmark NLP tasks such as tokenization, sentiment analysis, Named Entity Recognition (NER), summarization, and transformer-based models. The readers are provided with explanations that consider linguistic theory with a computational approach, ensuring that they understand how and why NLP algorithms work the way they do.
The final and most forward-looking section, Unit IV: Generative and Agentic AI, explores innovative themes such as Generative Adversarial Networks (GANs), diffusion models, large language models (LLMs), prompt engineering, LangChain frameworks, and autonomous agentic systems. This unit shows the authors’ keen awareness of emerging directions in AI research and application. Readers are introduced not just to concepts, but to workflow design, tooling, and integration strategies used in production environments.
The book’s pedagogy is one of its most striking aspects. Each chapter is carefully organized in a consistent format (introduction, theory, coding examples, applications), which makes it usable in the classroom and for self-study. The sequential transitions from notions to implementation support, especially for participants who have less mathematical and computer science training.
The book also uses Python code and visual display to help end-to-end project examples, making the theoretical abstract skills of the book more tangible. This is a particularly effective approach to experiential learning for professionals engaged in applied research, data product development, and/or AI system integration.
Another strength is the authors’ ability to remain analytical, while being readable. They provide accurate language with little use of jargon making the book that is easy to read, even when they become close to the bounds of mathematically dense topics. This quality makes it useful across many disciplines which could potentially appeal to engineers, domain experts, policy researchers, and digital designers.
Along with academic rigor, The Theory and Practice of Artificial Intelligence is remarkable for its practical applicability. Readers learn not only distinct, stand-alone algorithms, but how those algorithms are used in larger pipelines, such as those of image recognition, recommendation engines, chatbots, autonomous agents, and multi-modal generative frameworks. Thus, the book is a useful resource for industry practitioners who are building real-world AI applications.
The authors clearly have a strong awareness of the software development life cycle of AI, which ranges from data preprocessing, model evaluation, hyperparameter tuning, and deployment. The chapters equally engage equivalent attention to model accuracy among other considerations such as efficiency, interpretability, and scalability—and this range of concern fits very well with IEEE’s systems thinking focus.
While the book is strong in every regard, a few minor suggestions could further enhance its impact:
- A companion online repository offering downloadable code, extended readings, or interactive Jupyter notebooks would further help readers, especially those using the book in university courses or corporate training programs. Such a digital supplement could also serve as a channel for continuous updates in a rapidly changing field.
- Brief references to contemporary IEEE standards and research articles—even as optional “Further Reading” sections—could strengthen the book’s positioning for graduate-level coursework or research-oriented audiences.
These are areas where future editions or supplemental resources could build an already excellent foundation.
In summary, The Theory and Practice of Artificial Intelligence could only have come from an outstanding set of authors. This book is a fantastic, guided tour of the topic of AI with a combination of clarity, depth, breadth, and relevance that is rare to see, whether it is used in an educational course, professional development experience, or through self-directed exploration. The authors have successfully created an AI book that explores purposeful theory and simultaneously builds practical skills for any modern AI practitioner.
If you are a professional technical practitioner, the book makes a strong case for using it for well thought out rigorous instruction, real world purpose, and even future readiness. Combined with the content on Generative AI and Agentic systems, alongside a grounding in classical movement of AI, the book serves as a leading-edge resource that uses forward-thinking pedagogy to meet the future demands of academia and practices that learning with the purpose of building for industry.
If you’re a student, academic, or a practitioner who wants to build and embed emerging responsible AI models, or regardless of your role if you want a structured curriculum material or simply want to read and stay aware of clear and accessible scaffolding for your journey through a large field, the book argues convincingly that it can support you in your journey.
Artificial Intelligence for Strategic Communication
Karen E. Sutherland. Singapore: Springer Nature Singapore Pte Ltd. 2025. 484 pages, including index.
Index Terms: AI applications, content generation, ethical concerns, human judgment, strategic communication
Reviewed by Ziyong Wu , School of Journalism and Communication, Guangzhou University, China, 2112420050@e.gzhu.edu.cn
Artificial Intelligence for Strategic Communication is suitable for scholars researching the intersection of artificial intelligence (AI) and communication studies, professionals in the field of strategic communication, as well as students majoring in related disciplines.
There are currently some books and research findings exploring AI and strategic communication, but Karen Sutherland also recognizes that these materials lack diverse perspectives from those who are “directly affecting and being affected by AI in strategic communication”(p. vi). Consequently, this book integrates “the perspectives of strategic communication and AI scholars with strategic communication practitioners and representatives from generative AI tools”(p. vi) to address this deficiency. Most crucially, it provides an actionable framework for readers to guide their use of AI and navigate the constantly evolving AI landscape.
With its unique value and purpose established, Sutherland organized this book into three parts, each addressing distinct dimensions of AI’s intersection with strategic communication. Part 1, “Foundations of AI in Strategic Communication” establishes the conceptual and ethical groundwork for understanding AI’s role in the field. Part 2, “Integrating AI within Strategic Communication Practices”, the core section transitions from theory to implementation, detailing AI’s operationalization across communication workflows. Part 3, “Future Perspectives and Concluding Insights”, the concluding section projects AI’s trajectory while consolidating actionable guidance.
Chapter 1 defines AI as “computational systems” (p. 5) mimicking human intelligence and adapting to achieve goals through data learning. The chapter traces AI’s historical development, examines its current applications in strategic communication, and identifies three core research questions: attitudes toward AI in strategic communication, current AI uses, and future perceptions of AI’s impact. These questions will guide subsequent discussions and inform the development of a practical model.
Chapter 2 delves into the application, impact, and adoption of AI in strategic communication by integrating the Innovation Diffusion Theory and Technology Acceptance Model 3. The findings reveal that AI adoption in strategic communication is growing but not yet widespread. Professionals acknowledge AI’s benefits in enhancing the speed, personalization, and accessibility of content creation, but express concerns about ethical risks, over-reliance, and potential job displacement.
Chapter 3 further analyzes the ethical issues and implications of AI in strategic communication. It identifies ten key ethical concerns within this domain, extending beyond technical limitations to reflect profound impacts on social, legal, and moral dimensions. Drawing on the European Commission’s Ethics Guidelines for Trustworthy AI, the chapter proposes an AI ethics policy template to guide practitioners in regulating AI use in strategic communication.
Chapter 4 examines AI’s role in strategic communication strategies. Through empirical research, it indicates that AI is widely used in audience research, personalized content generation, and data analysis. This significantly enhances efficiency and precision. However, challenges like over-reliance on AI, hallucination and data privacy concerns were identified. The chapter proposes strategies like transparent disclosure of AI usage and regular auditing of AI outputs to mitigate these risks. It also emphasizes the irreplaceability of human judgment in strategic communication.
Chapter 5 highlights prompt engineering as critical for improving AI-generated content quality and outlines a guide for AI integration: policy development, training, and data research in preparation; prompt input and reasoning during generation; and fact-checking, editing, and synthesis in post-production.
Chapter 6 then emphasizes the importance of clear, specific prompts with sufficient context in strategic writing tasks, introducing techniques like chain prompting, prompt shortcuts, and custom GPTs to refine AI-generated content. Interviews highlight the potential of multimodal AI tools while cautioning against risks such as data insecurity and copyright issues. The chapter concludes by providing practical guidance on selecting AI writing tools and crafting effective prompts to improve efficiency and output quality.
Chapter 7 explores AI’s application in image generation tools within strategic communication. The chapter reveals that practitioners using AI tools for image generation “indicate polarisation across the satisfaction levels” (p. 211), primarily attributed to inconsistent output quality, technical complexity, and concerns regarding copyright and data security. These findings highlight deficiencies in the user experience and technical maturity of current AI image tools, while also providing insights for future technological improvements.
Chapter 8 overviews AI applications in audio/video production and editing for strategic communication. It examines technologies like text-to-audio/video, virtual avatars, and personalized content generation. The chapter highlights AI’s potential while addressing challenges such as deepfakes and ethical concerns.
Chapter 9 focuses on the importance and methods of fact-checking AI-generated content. Traditional fact-checking methods, including human-led verification, AI-assisted verification, and hybrid models, each have their advantages and limitations. The chapter proposes a practical guide for fact-checking AI-generated content, which is divided into three stages: preparation, content generation, and traditional fact-checking. These stages are designed to mitigate risks by integrating human expertise with AI capabilities.
Chapter 10 underscores the significance of editing and synthesizing AI-generated content, presenting eight core reasons: enhancing accuracy, improving clarity, infusing emotional tone, maintaining brand consistency, detecting biases, optimizing SEO, avoiding copyright issues, and adapting to specific contexts and audiences. The chapter concludes with a practical guide, including recommendations to avoid “AI Banality” (p. 321) and two checklists for editing and synthesizing AI content. This guide helps practitioners ensure professionalism and communication effectiveness in AI content post-production.
Chapter 11 investigates the evaluation and continuous improvement of AI in strategic communication across three dimensions: AI’s application in evaluating strategic communication practices, assessing AI’s impact on communication workflows, and fostering continuous improvement in AI-enhanced communication practices. It highlights AI’s effective use in communication evaluation, particularly in sentiment analysis, customer insights, crisis communication, media monitoring, and automation.
Chapter 12 outlines the six developmental stages of AI, from the current Artificial Narrow Intelligence (ANI) to prospective Artificial General Intelligence, Technological Singularity, and Artificial Superintelligence. Each stage is defined and contextualized within strategic communication applications, such as ANI’s application in social media data analysis and content generation. This chapter delivers a forecast of AI’s future role in strategic communication and provides actionable guidance for practitioners.
Chapter 13 reveals that while AI enhances efficiency and facilitates content creation, it also raises concerns about skill degradation, job displacement, and ethical issues such as bias and misinformation. The chapter proposes a cyclical practice model comprising five stages: Preparation, Strategy and Content Generation, Post-Production, Evaluation, and Continuous Improvement, emphasizing AI’s dynamic nature and the need for ongoing refinement.
Chapter 14 compiles a list of AI-related resources, encompassing international organizations like the AI Marketers Guild and the OpenAI, notable experts such as Alicia Lyttle and Anna Adi, and practical publications and tool guidelines, including Adweek and the AI Ethics Lab. This compilation assists strategic communication professionals in understanding and integrating AI technologies into their daily work.
Artificial Intelligence for Strategic Communication holds profound significance for practitioners, scholars, and students. For practitioners, it helps them craft prompt instructions and optimize AI-generated content, systematically master human-machine collaboration skills, and apply AI rationally in tasks like audience analysis and crisis management. For scholars and students, the extended application of the Technology Acceptance Theory, mixed research methods, and communication forms in the AI era offer direct theoretical frameworks and empirical references.
Although the scope of the research sample is limited and the critical analysis of AI-related flaws, particularly ethical concerns, is somewhat insufficient, the book nonetheless serves as a valuable reference, especially in research areas related to the integration of AI and communication technologies, communication ethics, policy, as well as in courses on digital communication and research methodology.
AI Governance: Applying AI Policy and Ethics through Principles and Assessments
Darryl J. Carlton. Bradley Beach, NJ: Technics Publication. 2024. 82 pages, including index.
Index Terms—AI governance, ethical AI practices, European Union AI Act.
Reviewed by Jackie Damrau, IEEE PCS Member.
Darryl J. Carlton’s book, AI Governance: Applying AI Policy and Ethics through Principles and Assessments, is a short, no-nonsense look into how Australia’s AI Ethics Principles and the European Union’s (EU) AI Act are looking at Artificial Intelligence (AI).
This book has four chapters: “A Brief History of AI,” “The Eight Guiding Principles of AI,” “Ethics and AI,” and “Conformance Assessment Checklist.” These are succinct, short chapters that provide contextual background that gives the reader enough information to understand why they would want to continue researching these areas on their own. Chapter 2, “The Eight Guiding Principles of AI,” and Chapter 3, “Ethics and AI,” each include two subsections: an Examples section that provide three instances related to how AI is being responsibly used and where it was not, and a References and Sources sections that shows the statements Carlton used and the cited source of that statement.
In the Preface, Carlton states that organizations should begin outlining their “obligations, rights, and responsibilities” based on two things: 1) “every country will eventually put legislation in place to establish guardrails for the use and operation of artificial intelligence, and 2) if you offer your product or service in global markets, then you will be governed by the legislation of those markets, not just your home location” (p. iv). Dr. Carlton also covers:
- AI Ethical Framework components of 1) Human, Social, and Environmental Well-Being, 2) Human-Centered Values, 3) Fairness, 4) Privacy Protection and Security, 5) Reliability and Safety, 6) Transparency and Explainability, 7) Contestability, and 8) Accountability, and
- The eight AI principles “from a comprehensive analysis of global approaches to AI governance” (p. 1).
Chapter 4 provides a comprehensive, self-directed Conformance Assessment Checklist that the EU has stipulated to use leading to “CE Certification for the deployment of AI systems into any member country of the EU” (p. 3). This four-part Assessment includes “AI Systems Overview;” “Governance and Oversight;” “Data and Documentation;” and “Operations, Risk & Retirement.” The third part, “Data and Documentation,” is of importance to technical communicators working with AI initiatives. It recommends identifying the transparency and -provisioning of information to the users by asking the following (p. 72):
- Describe how the AI system is designed and developed to ensure…enabling users to interpret and use [the software’s] output appropriately.
- Do the AI systems’ instructions for use…include concise, complete, correct, and clear information that is relevant, accessible, and understandable to users?
AI Governance, while small, is a book that you will want to really spend time reading and thinking how to incorporate the information Carlton brings to light. Even though this book is from the perspective of the Australian and EU governance entities driving protection for their citizens and member countries, you will find valuable information to increase your knowledge on AI governance principles.
Transcending Imagination: Artificial Intelligence and the Future of Creativity
Alexander Manu. New York, NY: CRC Press/Taylor and Francis Group. 2024. 264 pages, including index.
Index Terms—AI collaboration, human-machine creativity, artistic design.
Reviewed by Donald R. Riccomini, Emeritus Senior Lecturer in English, Santa Clara University.
Alexander Manu, in Transcending Imagination: Artificial Intelligence and the Future of Creativity, argues that artificial intelligence (AI) functions “as a collaborator” (p. 37) in artistic design and invention, and that “art is not limited to the intentions of the creator” (pp. 16–17). Art is no longer a purely human invention, but a collaboration that results in an ambiguous relationship “between human and machine creativity” (p. 101). As the “role of humans evolves from direct creators to facilitators” (p. 75), the “demarcation between creator and tool becomes increasingly blurred” (p. 60), until finally “the form is created without artistic intervention, culminating in outputs exceeding human imagination and expectations” (p. 74). This process completes AI’s “evolution from a simple, rule-following agent to a sentient-like entity capable of learning, adapting, and creating” (p. 114). Operating without human direction, AI eventually displaces humans as the primal creative force.
Or does it? Manu also argues that AI “does not possess intention” and “cannot instigate creation independently” (p. 15), because “our vision is a directive force shaping the system’s ensuing manifestations,” so that “we exercise influence over the final results, steering them toward our envisioned outcome” (p. 78). If the artist remains necessary to initiate and influence the creative process, can AI take final, independent control of the creative process?
The answer may lie in situating Manu’s argument within an historical context. Artists have always borrowed themes and techniques from previous artists. Shakespeare’s plays rework and reimagine earlier versions of the same basic story or archetype, as did his predecessors before him. The earlier texts act like AI, as an inventory of antecedent artistic choices, techniques, and inputs that prompt different ways to realize the artist’s vision. Yet the new play also transcends all previous work in its originality. Older texts are absorbed and refashioned in unpredictable ways reflecting the artist’s intention, and not as a purely mechanical or accidental recombination of parts based on a fixed set of rules.
These elements both affirm the underlying archetypal structure and introduce unique elements that “detach from conventional concepts and delve into alternate forms” (p. 178). Those “forms” do not invalidate the archetype, they reinvigorate it, adapting the archetype to a specific time and place conceived by the artist’s vision, and introducing new variations while absorbing and recontextualizing older ones, all under the final control of the artist.
AI, therefore, can serve as an online library or database of previous archetypal variations that supplement the artist’s unique choices by suggesting alternatives the artist—the “directive force”—may not have considered or known about, and can decide to include or not. AI acts as an automated version of the traditional assistant artist, who carries out background or focused work under the supervision of the main artist. In that sense, Manu may be right that AI is transforming imagination, but only insofar as the artist allows.
Artificial Intelligence—Intelligent Art? Human-Machine Interaction and Creative Practice
Robin Markus Auer, Dietmar Elflein, Sebastian Kunas, Jan Röhnert, and Eckart Voigts, eds. Bielefeld, Germany: transcript publishing. 2024. 294 pages.
Index Terms—artificial intelligence, human-machine interaction, ethical AI practices.
Reviewed by Pradhika Yudha Dharma, Student, Universitas Gadjah Mada, supported by Lembaga Pengelola Dana Pendidikan (LPDP), Indonesia.
Artificial Intelligence—Intelligent Art? Human-Machine Interaction and Creative Practice explores artificial intelligence’s (AI) potential as a collaborator in creative writing and music. While acknowledging technical hurdles and cultural biases in AI-generated works, the research highlights the value of human-machine interaction in artistic endeavors. It emphasizes small-scale art projects as testing grounds for this collaboration, even allowing for self-reflection within AI systems. While AI can inspire new art forms and transmedia storytelling, human intelligence still plays a crucial role in bridging cultural gaps. The study explores this shift towards machine-based creativity and its impact on the future of artistic expression.
Editors Eckart Voigts, Robin Markus Auer, Dietmar Elflein, Sebastian Kunas, Jan Röhnert, and Christoph Seelinger use various methods to explore human and machine creativity interactions. These methods include collaborative annotation, which reflects on the development of AI technology in artistic practice, and GPT-2 Simple, which generates texts based on 21 pandemic-related fiction texts. This approach involves refining the AI model and using voice techniques from post-Grotowski actor training to test the physical presence of vocals in space. An interdisciplinary approach combining insights from literature, music, cultural studies, and AI technology underpins the research’s objectivity. The study’s goal is to investigate human practitioners’ perceptions of AI systems and develop ethical approaches to AI practice. Significant contributions include highlighting AI’s potential as a collaborative tool in artistic practice, developing tools that facilitate self-reflection within AI systems, and providing a historical analysis of the relationship between technology and art. The study also identifies cultural and semantic weaknesses in AI-generated creative works, emphasizing the need for human understanding to bridge these gaps.
This study’s strength lies in its interdisciplinary approach, drawing on literature, music, cultural studies, and AI for a holistic understanding of AI in art. It uses innovative methods like collaborative annotations and refined AI models to showcase AI’s potential as a collaborator, not a replacement, and emphasizes self-reflection in both humans and machines. However, AI-generated creative work tends to have cultural and semantic flaws that require human intervention to bridge these gaps. Reliance on a specific dataset limits the generalizability of findings, and the focus on small-scale artistic projects may not fully capture AI’s implications in large-scale commercial artistic production. The complexity of formulating story automation programs, as well as the nuanced differences between speech and images at the technical level, present challenges.
Artificial Intelligence – Intelligent Art? examines AI as a creative partner in literature and music focusing on researchers in AI, the digital humanities, and interdisciplinary fields. It challenges traditional authorship and artistic boundaries advocating for ethical human-machine collaboration. Employing diverse methods and small-scale art projects, the book analyzes AI’s potential and limitations in artistic creation offering valuable insights for those interested in the intersection of AI and art.
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AI-Driven Project Management: Harnessing the Power of Artificial Intelligence and ChatGPT to Achieve Peak Productivity and Success
Jonathan Passmore, Sandra J. Diller, Sam Isaacson, and Maximilian Brantl, eds. New York, NY: Routledge. 392 pages, including index.
Index Terms—AI integration, digital coaching, online coaching, case studies.
Reviewed by—Marcia Shannon.
Why did I choose to review a handbook for digital coaches for a technical communications journal? I was curious. Whether you are dealing in information or coaching, our delivery methods changed radically with the Great Pandemic shutdowns. I wondered how other professions managed the situation.
I expected this book would provide useful parallels between instructional design and coaching methods and goals. Both disciplines provide information or instruction to change the recipient’s knowledge or skills. Coaching centers more on an individual’s needs and goals. Instructional design is all about training a workforce or user base. Current delivery methods are similar enough to achieve cross-pollination of ideas. Both share familiar goals, such as defining best practices, adapting technology to meet audience/client needs, career building.
With the primary author and editor, Jonathan Passmore, the other three editors, and their 47 contributors, The Digital and AI Coaches’ Handbook: The Complete Guide to the Use of Online, AI, and Technology in Coaching becomes an invaluable handbook. This book addresses the impact of global changes rooted in the recent critical technical developments. There is advice on how to build a successful coaching business, whether as a single practitioner or a larger enterprise.
The Digital and AI Coaches’ Handbook’s seven sections cover topics like Coaching practice, Technologies, Critical factors, Digitalisation and diversity, The coaching industry, Coaching practice, and Case studies. Within the 36 chapters, each author or team of authors tackles one aspect of digital coaching by defining its purpose and process. They compare key points addressing where AI tools are useful or a hindrance in a successful coaching session. The last eleven chapters are case studies. Each chapter documents one AI coaching platform used by the author or team. They describe how they shifted their delivery methods from traditional person-to-person to digital delivery using a range of technologies and the results of the project.
Although it was not comfortable to shift from my usual technical communicator thought processes, I found this to be an interesting and challenging book. It is a book to dip into not to read straight through. With 51 authors, the information is consistent, but the voices and viewpoints vary. I found scanning the index for a familiar topic was my best approach to learn something new. Discovering how other professions are managing the AI revolution and which of those actions could apply to a technical communication situation was interesting and useful.
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs
James Phoenix and Mike Taylor. Sebastopol,CA: O’Reilly Media, Inc. 2024. 424 pages, including index.
Index Terms—prompt engineering, generative AI, AI language models, AI applications.
Reviewed by—Josh Anderson, Information Architect, Paligo.
Prompt engineering is a difficult subject about which to author a book, not only because the subject itself is so nascent but also because information goes out-of-date far quicker than any publishing cycle. Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs attempts to remedy this by focusing not on any one particular artificial intelligence (AI) language model, but by first starting from general principles and then illustrating applications across a large variety of AI language models. The result is a book that is oftentimes dense and overwhelming, but that might just be what some audiences are looking for.
In 2022, I picked up a then-cutting-edge book, GPT-3: Building Innovative NLP Products Using Large Language Models, from this same publisher. Back then, the best authors could do was explain each parameter of the OpenAI application programming interface (API) and offer some ideas for startups that could be built around generative AI. Two years have made quite a difference; that book is now all but obsolete, not only because further iterations of the GPT model have been released since then, but also because the general approach to building AI applications has changed because of all the new services and companies that have sprung up. Prompt Engineering for Generative AI, on the other hand, gives readers much more to chew on. If key generative AI concepts such as “vector database,” “diffusion model,” or “text splitter” still sound like a foreign language to you, I believe you would be better off first browsing YouTube for short, concise explainer videos before tackling a tome such as this. Sure, this book defines these sorts of terms, but usually for only about a paragraph or two before diving into a long series of Python code samples for the reader to put to work immediately on their own projects. This book is primarily for developers. If you do not have a computer in front of you as you page through the book, there will be little that you can do until you return to your keyboard.
Two chapters of the book that I personally found valuable were the “Five Principles of Prompting,” the introductory chapter that approaches the subject from a model-agnostic perspective, as well as Chapter 8, which goes into thorough detail about how to use Midjourney, one of the most popular image generation models on the market today. This is a book for people who are ready to go beyond the surface level and want to dive deep into code examples of actual generative AI applications in action. For the readers who are ready for it, this book offers details that you are not likely to find elsewhere on the printed page.
AI-Driven Project Management: Harnessing the Power of Artificial Intelligence and ChatGPT to Achieve Peak Productivity and Success
Kristian Bainey. San Francisco, CA: Wiley. 2024. 384 pages, including index.
Index Terms—artificial intelligence, decision-making optimization, ethical AI practices.
Reviewed by— Novian Tiandini, Student, Universitas Gadjah Mada, supported by Lembaga Pengelola Dana Pendidikan (LPDP), Indonesia.
AI-Driven Project Management: Harnessing the Power of Artificial Intelligence and ChatGPT to Achieve Peak Productivity and Success explores integrating artificial intelligence (AI) and ChatGPT into project management practices. Practical implications include faster, more accurate decision-making, higher efficiency and productivity, and improved team collaboration. AI and ChatGPT can process large datasets, automate tasks, and provide real-time recommendations, enhancing project outcomes. However, the book also highlights the need to address data breaches, misuse of information, and ethical dilemmas in AI decision-making. Continuous monitoring, system updates, and adequate AI literacy are essential for effectively leveraging this technology in project management.
AI has revolutionized project management by optimizing efficiency, decision-making, and resource allocation. It anticipates needs and obstacles enhancing planning and execution. AI automates tasks boosting productivity and innovation. It extends to risk management, which involves assessing risks using historical data. AI streamlines documentation and scheduling reducing administrative burdens and optimizing task allocation. By analyzing data patterns, AI improves decision-making and provides insights for complex projects. Its continuous learning capabilities enable a dynamic, responsive approach that evolves over time.
Kristian Bainey uses a multifaceted methodological approach to investigate AI integration in project management. He uses quantitative methods, such as statistical analysis of project data, to pinpoint the underlying causes of project delays. Qualitative methods, such as expert interviews and case study analysis, provide insights into effective solutions. Bainey uses an exploratory research design, including the deployment of ChatGPT in controlled scenarios, to understand the potential applications and limitations of AI in project management.
This research reveals that material delays are the primary cause of 70% project delays, followed by 20% personnel shortages and 10% weather issues. Proposed solutions include strengthening supply chains, enhancing personnel training, and adapting construction methods. ChatGPT plays a crucial role in predictive analysis, automated forecasting, and project planning optimization. The research emphasizes a balance between AI utilization and human judgment addressing AI hallucination challenges and security measures in AI technology development.
The book highlights the efficiency of AI-based tools in streamlining specific project management tasks, particularly in data processing and analysis. Bainey also emphasizes AI’s accuracy in handling complex data, which enhances decision-making. However, the book acknowledges the AI limitations, including a lack of flexibility in adapting to tasks beyond its programming, as well as the requirements for large datasets and high computational intensity, which can be challenging for smaller projects or organizations.
AI-Driven Project Management targets a wide audience, including project managers, business analysts, information technology (IT) architects, data scientists, developers, managers, executives, entrepreneurs, and business leaders. It caters to all expertise levels, from beginners to advanced practitioners, focusing on those with a basic understanding of project management. It is also valuable for IT specialists and those interested in the intersection of AI, machine learning, and project management. The book bridges the gap between AI technology and practical applications significantly enhancing project outcomes.