Experimenting With AI
For the last few months I have being using Google Gemini. I subscribed so I could access a wider spectrum of services and evaluate it effectively. I also wanted to dispel some of the hysteria that accompanies any conversation about Large Language Models and Artificial Intelligence. The results were initially interesting and Google Gemini proved useful when researching material for blog posts. It is good at providing summaries about well documented subjects. I wrote a post about food banks recently and it distilled a lot of data into a manageable amount, which was ideal for my article. Google Gemini has also produced some visual basic scripting and simple batch files that have allowed me to automate processes on my PC. Such as changing the default monitor, or screen resolution at the click of an icon. However, I found this AI struggled with in-depth software troubleshooting and that its ability to create images varies greatly depending on the subject.
Learning via AI
In 1990 I was working at the DWP (Department for Work and Pensions). I had just taken the role of Computer Support Officer where I administered user access to the mainframe and provided first line support to staff using the terminals. That summer the office moved to a PC based network and the project was undertaken by the newly formed Siemens Nixdorf company. To cut a long story short I befriended one of the contract engineers and over the next 9 months he became an unofficial mentor to me. Technically he was only supposed to train me in the most basic of fashions but he liked the fact that I was always asking questions and effectively ended up schooling me in PC and server architecture, network administration and the associated protocols. For me this was the best way to learn. It was not a case of coming to grips with theory in a classroom environment. It was learning by doing and having direct and exclusive access to a source of knowledge and experience.