Yeah I broadly agree that there is a lot of confusion about capabilities.
Some years ago I was working somewhere and the management had caught the AI/ML bug and were obsessed with the idea of using ML to generate business "insights". They'd get some vague & unspecified data about a client's business operations, we'd input it into the ML and voila: "insights" about how to improve their business (and make us money)
They didn't know what these "insights" would be, they expected machine learning to magically generate them on its own.
I tried explaining that at a high level, ML can only really give you answers you already know are possibilities. It won't offer up some totally novel answer that you've not trained it for - i.e. you've got to know what the answers could be before you even start.
We got shut down by the parent company not long after that.
Some years ago I was working somewhere and the management had caught the AI/ML bug and were obsessed with the idea of using ML to generate business "insights". They'd get some vague & unspecified data about a client's business operations, we'd input it into the ML and voila: "insights" about how to improve their business (and make us money)
They didn't know what these "insights" would be, they expected machine learning to magically generate them on its own.
I tried explaining that at a high level, ML can only really give you answers you already know are possibilities. It won't offer up some totally novel answer that you've not trained it for - i.e. you've got to know what the answers could be before you even start.
We got shut down by the parent company not long after that.