Exploring the Capabilities of Gemini: A Surprising Email Analysis Experience
Recently, I embarked on an intriguing journey with Gemini, an AI tool that caught my attention for its potential in processing information. On one occasion, I tasked Gemini with a rather specific search: I wanted to find out how many emails I had received over the past two years that could be categorized as stalking messages with attachments. Although the reasons behind my inquiry are extensive and would fill another post, the outcome was nothing short of remarkable.
During the search, Gemini not only identified instances of these emails but also provided analysis on their content. To my astonishment, the insights it generated perfectly aligned with what I had already discussed with a psychiatrist regarding the situation. The accuracy and relevance of Gemini’s observations left me thoroughly impressed, as it demonstrated a nuanced understanding of my circumstances.
However, the next day, I decided to revisit this capability and requested Gemini to perform similar tasks. To my surprise, it denied any ability to engage in such analysis or to have ever done so in the past. This sharp contrast left me puzzled and curious about the inconsistency in its performance. Had I merely imagined the previous encounter, or was there an underlying reason for this shift?
I have documented screenshots from my initial conversation with Gemini as a way to validate my experience, reinforcing the saying that seeing is believing. Still, I am left wondering: what could have caused this regression in functionality?
If anyone has insights into AI behavior changes like this or has experienced similar discrepancies, I would love to hear your thoughts. The evolving nature of AI is undoubtedly fascinating, and experiences like this only deepen my curiosity.