Temporal quality degradation in AI models
One, I think overlooked issue with AI as ML, is how radically it affords the done-and-finished past over a creative future.<p>When faced with a blank page some percentage of people will not copy a known solution, they&#x27;ll draw on their experiences and eg., write a book about them.<p>For example, I could write a book about software development without research only inventorying my opinions, arguing for them, illustrating them, and so on.<p>If I were to start with ChatGPT I would, <i>necessarily</i>, only say what has been said.<p>There are so many dangers with the Eliza-effect, and this is one of them. I think the narrative on how these systems work needs more strongly wrestled from their salesmen -- and the real downsides exposed.<p>A client of mine seemed quite insistent that ChatGPT was &quot;creative&quot;. I hope in a few years that opinion will be rare, as everyone ends up writing the same article over-and-over.<p>And likewise, in development, using earlier versions of libraries with the most &quot;stackoverflow training data&quot;; using dying languages with the most github repos; and so on.<p>In other words: what we <i>have</i> written the most about is not what we now need to write the most about. And the former is the training data for modern AI.<p>It is not in the world with us, with our concerns: it is a statistical average over &quot;done and dead&quot; cases.