A recent study discussed at the Detroit Free Press web site indicates that using a phone in the car when you are driving with or without a 'hands free' set leads to unsafe driving. The direct comparison is between people phoning while driving drive and people driving when drunk (ref. 0.08% alcohol level).
Astonishing results :
- Participants drove more aggressively when intoxicated, and more slowly while talking on a cell phone.
- Cell phone users were 9% slower to brake, had 24% more variation in following distance and were 19% slower to return to their initial speed after braking.
- Three cell phone users had accidents.
- Intoxicated drivers followed the pace car more closely, were twice as likely to brake 4 seconds before an accident would have occurred and hit the brakes with 23% more force, according to the study. There were no accidents.
What it will be like for policemen, trying to figure out whether the person is talking on his hands free set to a person miles away, or someone in the back seat ?
Imagine the scene :
Driver : "Well what's the problem ?"
Policeman : "You are alone in the car and talking..."
Driver : "Well I like talking to myself, keeps my stress levels down"
Policeman : "You're next topic of conversation with yourself will be about the fine I'm giving you !"
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 […]