It’s been three years and at least once a week, I find myself saying aloud, I love Boston. There is some event, some moment, some experience all the time that lights me from within. This week started with an art walk, the Boston Philharmonic, the play Covenant with a director talk back, a Book Club Meeting and finifshing up the week wit the Boston Book Festival. How is such an abundance so available. After three years of tis sort of activity, there is not a bit of tedium or complaceny but marvel and awe. I thought about the tendency of people to say, you get tired of that after a while.
Maybe I haven’t hit “awhile,” yet? Or, is this a big lie, myth to make people feel content with where they are? So, in these moments when I’m home alone, I thought I’d chat with Mildred and we got into it. I was a little heated. Mildred insisted there is a baseline and doing things you like, you get tired of it.
So I asked again, Mildred (AKA Chat GPT) threw out terms like Hedonitc Treadmill – No matter how much you vary the execution of what you love, the overall experience becomes your “new normal”. Once the psychological novelty fades, the activity ceases to provide the same emotional boost. I need to be more specific, I”m doing arts, theater, music and lectures.
Round 4, Mildred was doubling down on boredom with terms like cognitive fatigue and lack of white space and had the nerve to say Tell me what the activities are, and I can tell you if your rotation is balanced enough to avoid burnout. That was the end of that conversation AI has it limits and I seem to find a way to break it weekly.
Mildred can be straight up wrong. But see the certainty in the wrongness. It sounds believable. Ordinary Street fixtures are embellished with cast bronze reliefs depicting lost oe discarded everyday items. Yet, the true description is “Still” is a guerrilla memorial created in 2014 by Boston artist Matthew Hinçman to remember Trayvon Martin the unarmed African American teenager who was shot and killed in Florida in 2012.
I argue with AI in the hope of machine learning, giving AI more data to update the model and adjust future inquiries. So while I’d temporarily disengaged from Mildred over my activities, I went back, for the sake of science and humanity. 🙂
Hopefully, I have helped the model, in addition to this cautionary tale, to challenge responses. In one of the sessions, Justin Ellis, author of The Cruelty of Nice Folks: Why Minneapolis Is the Story of America, was asked whether the Minneapolis Truth & Reconciliation Initiative was having an impact. He answered with a short explanation of what is going on, and his final short answer was no. More data for the model.
As humans, we want to find answers as quickly as possible, and in my case, as illustrated above, we also want to be right. We want confirmation. What I found in many years in corporate was that the mindset was still “What are other companies doing?” rather than “Is what other companies are doing effective?” For example, one year we were told Microsoft required its employees to do 120 hours of education a year, so that became our quota. My response was, “120 hours is three months. What results did Microsoft get? What business problem are we trying to solve?” Yes, the older I got and the more years I accumulated, the mouthier I could get at times. The point is: are you copying a model for success?
Truth and Reconciliation sounds great, but what problem is it trying to solve, and what metrics show it works? Its modern roots are in South Africa, and it was later used in Rwanda. We have all probably heard the term, but given geopolitics, we are probably not aware of how effective either program was. In both cases, the programs are said to have prevented massive retaliation and further bloodshed. That is a good thing. The rest, such as economic injustice, reparations, and persistent divides, not so much.
I recently made scones. I knew exactly the kind I wanted, so I could look at pictures and get an idea of which recipe to use. I had the end in sight. How do you know you aren’t doing something like standing on the scale in a different place to change your weight? Or maybe those are the questions you need to ask. Are you giving more data or information, or are you providing the conclusion? It’s all in the questions. Do the questions get you closer to the goal?
