Working too hard is not that efficient... in the long term
At a time where people are worried about losing their jobs and working all hours god sends to stand out from the pack in a positive manner it seems that they may not be providing their company with the best of themselves. Obviously if your company is short staffed and still has as much work they may not be so interested in the article over at FastCompany. But may be worth reading so at least you are aware 😉
Examples from Flickr and Facebook are provided to illustrate the misconception that getting people to work their socks off may not be providing you with the best results in the end!
Make sure you check out this great video from TED, Stefan Sagmeister is a world renowned designer who explains how every 7 years he takes a year off to pursue personal areas. He also indicates that structuring his time off was probably one of the most important parts in a successful sabbatical year. Furthermore this time often allows him to be a better designer and provide his clients with a better quality service once the sabbatical is over! Better still take the time to view the video see for yourself.
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 […]