Je viens de terminer la traduction de l'article 'Web 3.0' de Jeffrey Zeldman publié en janvier sur 'A List Apart'. Il n'existait pas encore de traduction de cet article fort intéressant pour les francophones, voilà chose faite. Je voudrai remercier Jean Tournier pour sa relecture et ses remarques précieuses. Je souhaite aussi remercier Erin Kissane, éditrice du magazine ‘A List Apart’ pour son aide pendant la traduction.
J'ai connu les premières périodes de l'Internet et les regards perplexes à la fin des années 90 lorsqu'on expliquait "un travail dans le Web". Le paragraphe "Bulle, bulle" de cet article a fait resurgir ces souvenirs. Je dois avouer qu’étant un passionné d'Internet depuis le début, j’ai été motivé par l’idée de traduire ces souvenirs...
I question the reliability and accuracy of Generative AI (GenAI) in enterprise scenarios, particularly when faced with adversarial questions, highlighting that current Large Language Models (LLMs) may be data-rich but lack in reasoning and causality. I would call for a more balanced approach to AI adoption in cases of assisting users, requiring supervision, and the need for better LLM models that can be trusted, learn, and reason.
I discuss my experience with chatbots, contrasting older rules-based systems with newer GenAI (General Artificial Intelligence) chatbots. We cannot dismiss the creative capabilities of GenAI-based chatbots, but these systems lack reliability, especially in customer-facing applications, and improvements in the way AI is structured could lead to a "software renaissance," potentially reducing society's technical debt.
The article discusses the contrasting debate on how AI safety is and should be managed, its impact on technical debt, and its societal implications.
It notes the Center for AI Safety's call for a worldwide focus on the risks of AI, and Meredith Whittaker's criticism that such warnings preserve the status quo, strengthening tech giants' dominance. The piece also highlights AI's potential to decrease societal and technical debt by making software production cheaper, simpler, and resulting in far more innovation. It provides examples of cost-effective open-source models that perform well and emphasizes the rapid pace of AI innovation. Last, the article emphasises the need for adaptive legislation to match the pace of AI innovation, empowering suitable government entities for oversight, defining appropriate scopes for legislation and regulation, addressing ethical issues and biases in AI, and promoting public engagement in AI regulatory decisions.
Japan has made its ruling on the situation between Content creators and Businesses. Japanese companies that use AI have the freedom to use content for training purposes without the burden of copyright laws. This news about the copyright laws in Japan reported over at Technomancers is seen as Businesses: 1 / Content Creators: 0 The […]