Issue link: https://iconnect007.uberflip.com/i/1503998
is prone to a lot of mistakes and a thing that they call "hallucinations," where it begins to imagine answers to things that are factually wrong. It's a dangerous tool if you're unable to recognize when it's lying to you. I had a dialog with ChatGPT about the Red Bead Experiment; you can get into "argu- ments" with it. It will apologize profusely, and then keep making the same mistake. In terms of how it can interpret the data and draw out meaningful patterns to which lead- ership can respond, I'm concerned about the innate interpretations which will be built into the models inside of the AI itself. You can train AI on large amounts of data, but you may not be able to train it on large amounts of context. Elon Musk had his visions of a 24-hour dark factory (he called it the "Alien Dreadnought") but came to the realization that humans were being underrated and backtracked. at's the caution I would have about relying too much on what AI can translate data into. It's not necessarily going to give you knowledge; it's still up to the individual to decide. Could I say to ChatGPT, based on current produc- tion statistics, "Show me a process behav- ior chart about how well we are meeting our overall objectives for delivery units per unit of time"? Now, if it could actually do that, that would be remarkable. But I would rec- ommend the same caution as when I teach management about making their own pro- cess behavior charts: Don't rely on soware, and do it yourself, because then you know the veracity and validity of the calculations being done, whether the picture being pre- sented makes sense in context. Your point about generative AI, like Chat- GPT, is spot on. There's this domain in which the AI engines are really good at ana- lyzing large chunks of data and finding pat- terns in the data that may not otherwise be obvious. This is very sophisticated pattern matching. Research and development are