Before starting my master’s in Business Information Management my exposure to Generative AI tools was fairly small. Especially after reading through a couple of the blog posts and talking to my fellow BIM students I realised that I am definitely a late adopter compared to my peers. Prior to my studies, my use of genAI was limited to simple text generations, like drafting an email with ChatGPT, or the AI Chatbot at my working student job that helped the company’s employees with all sorts of internal and administrative questions. I found genAI in both cases highly useful but failed to apply its potential to more areas of my life. The main point that stopped me back then were all the mistakes genAI tools were still making. I constantly heard stories about ChatGPT making up facts, citing sources that didn’t even exist or not being able to apply basic mathematics. Hearing about these incidents led me to believe that genAI had not reached a reliable stage yet. Consequently, I never made the effort of integrating genAI further into my daily life.
As BIM focuses on emerging technologies, particularly AI applications, I have approached Generative AI tools more open-mindedly. So far, primarily genAI tools related to academia. My most important discovery being Consensus AI. This AI application has become my go-to tool for conducting research. It is not only highly accurate in finding papers related to my research interests, but with the addition of their copilot it is very easy to retrieve relevant information from these papers for my work. Consensus AI also managed to erase my concerns about the accuracy of its information as every hard fact or number is directly linked to a paper, making it easy to validate the AI’s output. An issue that had stopped me before with ChatGPT. Alongside this AI tool, I have also grown to integrate ChatGPT more into my every-day life. It has completely replaced Google’s search engine as my primary search tool and I now ask any question directly to ChatGPT. Even though sometimes validation is required, the validation process is a lot quicker than using Google from the start.
Browsing this blog has further brought my attention to the Mindtrip AI application, an AI tool designed for planning trips. To test its capabilities, I asked it to recommend a trip to Munich, my hometown. I mentioned to the AI that I already know the city quite well and asked about more niche activities and places in the city. Even though I was not a hundred satisfied with the AI’s suggestion of niche activities, I was still surprised by the level of detail in its tips about Munich. It showed that it is definitely sufficient for cities or countries one hasn’t been to before.
My major takeaway after experimenting with a couple of AI tools is that whatever need or problem one encounters on a personal level it is always a valid first step to look for a genAI tool that could address it.
