This and elements where the intro to ML for me as well.
I understand your sentiment but we also have to accept that for a lot of Ml usecases just calling ChatGPT api is 100x better approach than creating your own Ml model, and thus there is really no need to understand any math.
As an example I am building an Ai nutrition counting app. And I use ChatGPT function calling. I can just add a field that has say an emoji of the good and it automatically classifies any food to the right emoji. There is absolutely no need to know gradient descent or any fundamental property to be able to do that.
As an ML engineer, I look forward to the day OpenAI ups their prices 5x and companies hire people like me as a consultant to replace their expensive API calls with an SVM or random forest that can be run off a smartphone.
That intern who figured out how to make a POST request and accidentally committed the API keys to public GitHub? Long gone. The rise and grind manager who discovered ChatGPT in April? Retired. But we will be there, ready to cut your costs by 95% because people couldn’t be bothered to understand the basics of what they’re using, in exchange for a sizable consulting fee of course.
I understand your sentiment but we also have to accept that for a lot of Ml usecases just calling ChatGPT api is 100x better approach than creating your own Ml model, and thus there is really no need to understand any math.
As an example I am building an Ai nutrition counting app. And I use ChatGPT function calling. I can just add a field that has say an emoji of the good and it automatically classifies any food to the right emoji. There is absolutely no need to know gradient descent or any fundamental property to be able to do that.