Use Case 3 in Chapter 6 examines the regulation of MDTs in the context of political advertising under the General Data Protection Regulation (GDPR), the Regulation on Transparency and Targeting of Political Advertising (TTPA), the Digital Services Act (DSA), and the Artificial Intelligence Act (AIA). The prohibition on advertising based on profiling with special category data in both the DSA and the TTPA does not adequately reflect the capabilities of modern data analytics. Both the DSA and the TTPA fall short in addressing MDTs as stand-alone techniques or as complements to online behavioural advertising and political microtargeting. The AIA’s prohibition of subliminal, manipulative, and deceptive techniques requires a complex set of criteria to be met, with the outcome still uncertain.

For the past 30 years we have lived through the information revolution, powered by the explosive growth of semiconductor integration and the internet. The exponential performance improvement of semiconductor devices was predicted by Moore’s law as early as the 1960s. Moore’s law predicts that the computing power of microprocessors will double every 18-24 months at constant cost so that their cost-effectiveness (the ratio between performance and cost) will grow at an exponential rate. It has been observed that the computing power of entire systems also grows at the same pace. This law has endured the test of time and remains valid today. This law will be tested repeatedly, both now and in the future, as many people today see strong evidence that the "end of the ride" is near, mostly because the miniaturization of CMOS technology is rapidly reaching its limit. This chapter reviews technology trends underpinning the evolution of computer systems. It also introduces metrics for performance comparison of computer systems and fundamental laws that drive the field of computer systems such as Amdahl’s law.

Chapter 1 lays the groundwork for the subsequent legal analysis. Following the fundamentals, the chapter highlights ongoing global policy discussions and initial regulatory efforts, with particular emphasis on the latest developments within international organisations such as UNESCO, the OECD, the Council of Europe, and the EU. It also addresses relevant legal scholarship, ensuring a comprehensive understanding of the evolving regulatory debate surrounding these technologies.

Nowadays, artificial intelligence (AI) is becoming a powerful tool to process huge volumes of data generated in scientific research and extract enlightening insights to drive further explorations. The recent trend of human-in-loop AI has promoted the paradigm shift in scientific research by enabling the interactive collaboration between AI models and human experts. Inspired by these advancements, this chapter explores the transformative role of AI in accelerating scientific discovery across various disciplines such as mathematics, physics, chemistry, and life sciences. It provides a comprehensive overview of how AI is reshaping the scientific research – enabling more efficient data analysis, enhancing predictive modeling, and automating experimental processes. Through the examination of case studies and recent developments, this chapter underscores AI’s potential to revolutionize scientific discovery, providing insights into current applications and future directions. It also addresses the ethical challenges associated with AI in science. Through this comprehensive analysis, the chapter aims to provide a nuanced understanding of how AI is facilitating scientific discovery and its potential to accelerate innovations while maintaining rigorous ethical standards.

With the rapid development of artificial intelligence technology, human–AI interaction and collaboration have become important topics in the field of contemporary technology. The capabilities of AI have gradually expanded from basic task automation to complex decision support, content creation, and intelligent collaboration in high-risk scenarios. This technological evolution has provided unprecedented opportunities for industries in different fields, but also brought challenges, such as privacy protection, credibility issues, and the ethical and legal relationship between AI and humans. This book explores the role and potential of AI in human–AI interaction and collaboration from multiple dimensions and analyzes AI’s performance in privacy and credibility, knowledge sharing, search interaction, false information processing, and high-risk application scenarios in detail through different chapters.

Informal caregivers such as family members or friends provide much care to people with physical or cognitive impairment. To address challenges in care, caregivers often seek information online via social media platforms for their health information wants (HIWs), the types of care-related information that caregivers wish to have. Some efforts have been made to use Artificial Intelligence (AI) to understand caregivers’ information behaviors on social media. In this chapter, we present achievements of research with a human–AI collaboration approach in identifying caregivers’ HIWs, focusing on dementia caregivers as one example. Through this collaboration, AI techniques such as large language models (LLMs) can be used to extract health-related domain knowledge for building classification models, while human experts can benefit from the help of AI to further understand caregivers’ HIWs. Our approach has implications for the caregiving of various groups. The outcomes of human–AI collaboration can provide smart interventions to help caregivers and patients.

This chapter is dedicated to the correct and reliable communication of values in shared-memory multiprocessors. Correctness properties of the memory system of shared-memory multiprocessors include coherence, the memory consistency model, and the reliable execution of synchronization primitives. Since CMPs are designed as shared-memory multi-core systems, this chapter targets correctness issues not only in symmetric multiprocessors (SMPs) or large-scale cache coherent distributed shared-memory systems, but also in CMPs with core multi-threading. The chapter reviews the hardware components of a shared-memory architecture and why memory correctness properties are so hard to enforce in modern shared-memory multiprocessor systems. We then treat various levels of coherence and the difference between plain memory coherence and store atomicity. We introduce memory models and sequential consistency, the most fundamental memory model, enforcing sequential consistency by store synchronization. Finally, we review thread synchronization and ISA-level synchronization primitives and relaxed memory models based on hardware efficiency and relaxed memory models relying on synchronization.

The chapter also covers compiler-centric approaches to build computers known as VLIW computers. Apart from reviewing the design principles of VLIW pipelines, we also review compiler techniques to uncover instruction-level parallelism, including loop unrolling, software pipelining, and trace scheduling. Finally, this chapter covers vector machines.

The instruction set is the interface between the hardware and the software and must be followed meticulously when designing a computer. This chapter starts with introducing the instruction set of a computer. A basic instruction set is used throughout the book. This instruction set is broadly inspired by the MIPS instruction set, a rather simple instruction set which is representative of many instruction sets such as ARM and RISC V. We then review how one can support a representative instruction set with the concept of static pipelining. We start with reviewing a simple 5-stage pipeline and all issues involved in avoiding hazards. This simple pipeline is gradually augmented to allow for higher instruction execution rates including out-of-order instruction completion, superpipelining, and superscalar designs.

Misinformation on social media is a recognized threat to societies. Research has shown that social media users play an important role in the spread of misinformation. It is crucial to understand how misinformation affects user online interaction behavior and the factors that contribute to it. In this study, we employ an AI deep learning model to analyze emotions in user online social media conversations about misinformation during the COVID-19 pandemic. We further apply the Stimuli–Organism–Response framework to examine the relationship between the presence of misinformation, emotions, and social bonding behavior. Our findings highlight the usefulness of AI deep learning models to analyze emotions in social media posts and enhance the understanding of online social bonding behavior around health-related misinformation.

In Chapter 2, the classification of data processed by MDTs under the General Data Protection Regulation (GDPR) is examined. While the data processed by MDTs is typically linked to the category of biometric data, accurately classifying the data as special category biometric data is complex. As a result, substantial amounts of data lack the special protections afforded by the GDPR. Notably, data processed by text-based MDTs falls entirely outside the realm of special protection unless associated with another protected category. The book advocates for a shift away from focusing on the technological or biophysical parameters that render mental processes datafiable. Instead, it emphasises the need to prioritise the protection of the information itself. To address this, Chapter 2 proposes the inclusion of a new special category of ‘mind data’ within the GDPR. The analysis shows that classifying mind data as a sui generis special category aligns with the rationale and tradition of special category data in data protection law.

Given the widening gaps between processor speed, main memory (DRAM) speed, and secondary memory (disk) speed, it has become more and more difficult in recent years to feed data and instructions at the speed required by the processor while providing the ever-expanding memory space expected by modern applications.