Re: AI
The thing about your described AI use: when you chat with AI, it's engaging in a narrow and simple level of output to input. You prompt, it responds. The use cases where AI fails is when the model has to respond to multiple and contradictory prompts from a range of users and situations and address them concurrently in the same session instead of individually in separate sessions.
AI was able to handle monitoring and restocking inventory of a vending machine. What it couldn't handle was product selection, individual customer product requests, calculating pricing against competing vendors, weighing one customer request against other different requests. It could vend the drinks, but it was unable to make decisions across multiple variables and overlapping situations.
In terms of medications and solutions, I think AI is as it exists is good for generating hypothetical treatments and creating ideas for experimentation and refinement. However, the internal reality of an AI model rarely corresponds exactly or even at all to real world situations. It's only offering a theory based on a simulative model that can range from somewhat accurate to totally wrong.
For example, I used Gemini 2.0 Flash to try to come up with solutions to blend a patch of new paint into the existing paint of a damaged wall. A lot of its solutions were not effective because its internal model of how paint functions was not always complete or accurate.
It suggested paint matching apps that failed to identify blue as blue. It suggested a paint strategy that didn't account for the speed of paint drying. It suggested thinner coats of paint at the edges of the damage to blend into the older paint; this instead created a border of darker paint at the perimeter. It suggested paint matching techniques that didn't account for sheen. It suggested blending mismatched paint by diluting the paint with acrylic gloss to have it gradually lighten at the edges and this last one actually worked.
Where AI can be helpful: I struggle at coming up with ideas to test. AI can generate ideas faster so that I can focus on testing them and refining the ones that don't immediately fail. It speeds up the process of trial and error by offering ideas for trial. However, its hypotheticals are not solutions; they're only ever starting points for tests.
