@nixCraft@mastodon.social Two things. First, most of the time, discovering problems are easier than building complex systems, and the same goes for ML/AI models, of course. Second, ML/AI is "bound" to make mistakes, and ML/AI scientists have been saying for years that you need mistakes for ML/AI to be useful (because otherwise, it means overfitting). I understand that ML/AI tools can be helpful for productivity, but it's guaranteed that it'll make mistakes (if not intentional).