Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

48 results

Page 1 of 3

The Cambridge Handbook of Generative AI and the Law
  • Edited by Mimi Zou, Cristina Poncibò, Martin Ebers, Ryan Calo
  • Coming soon
  • Expected online publication date:
    July 2025
    Print publication:
    31 July 2025
  • This handbook offers an important exploration of generative AI and its legal and regulatory implications from interdisciplinary perspectives. The volume is divided into four parts. Part I provides the necessary context and background to understand the topic, including its technical underpinnings and societal impacts. Part II probes the emerging regulatory and policy frameworks related to generative AI and AI more broadly across different jurisdictions. Part III analyses generative AI's impact on specific areas of law, from non-discrimination and data protection to intellectual property, corporate governance, criminal law and more. Part IV examines the various practical applications of generative AI in the legal sector and public administration. Overall, this volume provides a comprehensive resource for those seeking to understand and navigate the substantial and growing implications of generative AI for the law.

  • The architecture of language: Understanding the mechanics behind LLMs

  • Part of
  • Andrea Filippo Ferraris, Davide Audrito, Luigi Di Caro, Cristina Poncibò
  • Journal:
    Cambridge Forum on AI: Law and Governance / Volume 1 / 2025
    Published online by Cambridge University Press:
    06 January 2025, e11
    • Article
      • You have access
      • Open access
    • PDF
    • HTML
    • Export citation

  • Large language models (LLMs) have significantly advanced artificial intelligence (AI) and natural language processing (NLP) by excelling in tasks like text generation, machine translation, question answering and sentiment analysis, often rivaling human performance. This paper reviews LLMs’ foundations, advancements and applications, beginning with the transformative transformer architecture, which improved on earlier models like recurrent neural networks and convolutional neural networks through self-attention mechanisms that capture long-range dependencies and contextual relationships. Key innovations such as masked language modeling and causal language modeling underpin leading models like Bidirectional encoder representations from transformers (BERT) and the Generative Pre-trained Transformer (GPT) series. The paper highlights scaling laws, model size increases and advanced training techniques that have driven LLMs’ growth. It also explores methodologies to enhance their precision and adaptability, including parameter-efficient fine-tuning and prompt engineering. Challenges like high computational demands, biases and hallucinations are addressed, with solutions such as retrieval-augmented generation to improve factual accuracy. By discussing LLMs’ strengths, limitations and transformative potential, this paper provides researchers, practitioners and students with a comprehensive understanding. It underscores the importance of ongoing research to improve efficiency, manage ethical concerns and shape the future of AI and language technologies.

  • 16 - AI Platforms

  • from Part V - Application and Regulation of AI
  • Edited by Larry A. DiMatteo, University of Florida, Cristina Poncibó, Université de Turin, Geraint Howells, National University of Ireland, Galway
  • Book:
    The Cambridge Handbook of AI and Consumer Law
    Published online:
    21 October 2024
    Print publication:
    31 October 2024, pp 236-251

  • Summary

    Despite the benefits of the convergence of AI in ecommerce, it is necessary to address some concerns. The presence of AI-powered platforms raises significant challenges to consumer autonomy. This chapter discusses the overlap and interplay among three main legal regimes – EU AI Act Proposal, Digital Services Act (DSA), and EU Consumer Law.These laws will need to be amended with new articles to adequately address AI-specific concerns

The Cambridge Handbook of AI and Consumer Law
  • Comparative Perspectives
  • Edited by Larry A. DiMatteo, Cristina Poncibó, Geraint Howells
  • Published online:
    21 October 2024
    Print publication:
    31 October 2024
  • This comprehensive handbook delves into the intricate relationship between artificial intelligence, law, and government regulations in society and business. With a particular focus on consumer-centric issues, chapters analyze the benefits and challenges of the expanding influence of AI systems on consumers, while shedding light on the psychological impact and potential harm posed by AI. Readers will navigate the complexities of tort law and its application to harm caused by AI, explore the legal conundrums arising from consumers utilizing digital delegates as agents, and uncover the innovative ways AI can be harnessed to enforce consumer law. This work is essential reading for anyone seeking to understand the implications of AI on the legal landscape, the future of the consumer marketplace, and the role of consumer law.

Page 1 of 3