: From writing instructions on how to make a homemade bomb to refusing to provide a list of « bad » gifts to give, ChatGPT, which is being introduced as the representative of the new generation of chatbots, has made its choice. In this series of articles devoted to the use of generative AI through various experiments, we will first review some general information about these algorithms, then focus on the limitations encountered when using
: The development of general-purpose AI models requires the creation of massive datasets, often built from publicly available data collected online. Website owners whose content is subject to such collection have a range of tools at their disposal to help them control the data being collected and the ways in which it may be used. The articles in this dossier first introduce the concepts related to online data scraping, before presenting the methods that website publishers can implement to manage and control the data collected by data extraction bots.
: The emergence of agentic AI first raises the question of what this term actually means. Although the concept itself is not new, it has taken on a new significance in the era of generative AI. To date, there is no universally accepted definition of agentic AI or AI agents. This is illustrated by the OECD ’s February 2026 report, which identifies around ten different proposed definitions - each reflecting a distinct perspective. The terminology remains ambiguous, with varying scopes and overlapping concepts. Between marketing-driven labels, confusion with established terms such as conversational agent, and inconsistent definitions, it is far from straightforward to clearly define agentic AI and the systems it encompasses. In this article, we therefore provide an overview of both historical and contemporary terminology to help clarify the emerging—or perhaps not so new - landscape of agentic AI.