The De Gruyter Handbook Of Artificial Intelligence Identity And Technology Studies
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The De Gruyter Handbook of Artificial Intelligence, Identity and Technology Studies
Author: Anthony Elliott
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2024-07-22
The De Gruyter Handbook of Artificial Intelligence, Identity and Technology Studies examines the relationship of the social sciences to artificial intelligence, surveying the various convergences and divergences between science and technology studies on the one hand and identity transformations on the other. It provides representative coverage of all aspects of the AI revolution, from employment to education to military warfare, impacts on public policy and governance and the future of ethics. How is AI currently transforming social, economic, cultural and psychological processes? This handbook answers these questions by looking at recent developments in supercomputing, deep learning and neural networks, including such topics as AI mobile technology, social robotics, big data and digital research. It focuses especially on mechanisms of identity by defining AI as a new context for self-exploration and social relations and analyzing phenomena such as race, ethnicity and gender politics in human-machine interfaces.
The De Gruyter Handbook on Law and Digital Technologies
Author: Massimo Durante
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2025-09-22
The De Gruyter Handbook on Law and Digital Technologies provides a comprehensive, accessible and thought-provoking guide to the current and future regulation of digital technologies. It addresses key legal challenges such as reconceptualizing crucial, deep-rooted notions, including those of person, autonomy, democracy, the rule of law, sovereignty, constitutionalism and governance. The handbook proposes critical explorations of the potential impact of digital technologies on new and traditional forms of governance and regulation across different and competitive normative perspectives such as law, economy, social norms and legal design. In this framework, it addresses the societal transformations brought about by digital technologies, the legal means for regulating the field, and the impact of governance in areas such as fintech, sustainability, outer space, or healthcare.
Generative AI and LLMs
Author: S. Balasubramaniam
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2024-09-23
Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.