I am not a fan of having articles written by AI alone. In my experience, it still works much better to think of these services as tools and assistants. In that moment, they don’t do the work for you. Instead, they help you do certain tasks faster and more efficiently – including writing. Here’s an … Read more
A new analysis highlights the risks of attributing human characteristics to artificial intelligence systems. In an article published on VentureBeat, Roanie Levy from CCC explains how anthropomorphizing AI can lead to serious misconceptions and problems in business and legal contexts. The practice of describing AI systems as “learning” or “thinking” masks their true nature as … Read more
According to Ben Hylak’s detailed analysis, published as a guest post, OpenAI’s o1 model requires a fundamentally different approach compared to traditional chat models. Hylak, who initially criticized the model but later became a regular user, explains that o1 functions best as a “report generator” rather than a conversational AI. The key to successful o1 … Read more
A comprehensive analysis published by Andy Masley demonstrates that individual use of ChatGPT and other large language models (LLMs) has minimal environmental impact. The study shows that a single ChatGPT query consumes approximately 3 watt-hours of energy, equivalent to watching 10 seconds of streaming video or running a space heater for 2.5 seconds. The research … Read more
Major enterprises are revealing their approaches to scaling generative AI as organizations prepare for widespread adoption in 2025. In an article published by VentureBeat, author Bryson Masse explores how companies like Wayfair and Expedia are combining custom solutions with external platforms to optimize their AI operations. Wayfair’s CTO Fiona Tan reports that the company uses … Read more
OpenAI’s o1 reasoning model and similar AI systems require a different prompting strategy to achieve optimal results. According to an article by Carl Franzen in VentureBeat, users should provide detailed context through “briefs” rather than traditional prompting methods. Former Apple interface designer Ben Hylak demonstrated that letting o1 plan its own analytical steps leads to … Read more
Overfitting is a common problem in AI training where the model learns the training data too precisely, rather than understanding general patterns. It can be compared to a student who memorizes example problems from a textbook instead of understanding the underlying mathematical principles. When faced with slightly different problems in an actual test, they fail. … Read more
Religious leaders across the United States are incorporating AI into their religious practices, from sermon writing to theological research. According to an article by Eli Tan in The New York Times, clergy members are testing various AI applications while grappling with ethical considerations. Rabbi Josh Fixler of Congregation Emanu El in Houston created “Rabbi Bot,” … Read more
A Reddit post titled “After Working on LLM Apps, I’m Wondering: Are they really providing value” reflects the author’s skepticism about the advantages of LLM-based applications compared to traditional automation tools. They note that LLM apps primarily process text inputs to determine user intent and call appropriate functions, which doesn’t seem significantly different from previous … Read more
A recent experiment demonstrates that Large Language Models (LLMs) can significantly improve code quality through iterative prompting. Max Woolf tested whether repeatedly asking an LLM to optimize code would yield better results. Using Claude 3.5 Sonnet, the experiment showed performance improvements of up to 100 times compared to initial implementations. The test focused on a … Read more