I've been reading a lot recently about the 'Web 2.0', and how getting people to participate and create, is the future. Wikipedia is often cited as an example. The recent study that has flared up in the magazine Nature's face is interesting in this matter. It is for me an indication that saying everything should be open as if trying to find some parallel with the words Open Source is just not feasible as a model.
Letting anybody post anything, anywhere and expecting people to respect one another and be accurate in all they say is pretty utopian. The will to get rid of all structure and process that is inherent to professional structures in order to get things done faster sounds nice but is just far too simplistic in the real (and virtual) world.
Some may think that this study illustrates how well Wikipedia have done in proving the case for opening things up. However, most articles discussing this matter, seem on the contrary to indicate that Wikipedia has changed their model. They have brought in new processes to 'tighten their system up further' in order to provide accurate information.
Over and above the issue of quantity and quality; are you going to want to use a reference system that is often inaccurate or one that is nearly always accurate ? The Encyclopaedia Britannica has since been fiercely defending its accuracy status that was questioned in the article Nature magazine published.
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 […]