You can argue against Cuban's point that anyone who doesn't know machine learning will be "dinosaur in 3 years", while also being enthusiastic about deep learni

You can argue against Cuban's point that anyone who doesn't know machine learning will be "dinosaur in 3 years", while also being enthusiastic about deep learning. > "traditional" machine learning methods ... may be good enough for business tasks, they are not the cutting edge of AI research That's true - but so? Under discussion is whether every developer should drop what they are doing and learn ML/DL, not what the cutting edge of AI research is? In that context a lot of the low-hanging fruit for the next while is going to involve actually deploying simpler ML, with all the non-ML work that entails - and maybe using off-the-shelf DL to extract information from unstructured data. > older ML PhDs are angry that deep learning is so easy That's very ungenerous. People tend to do PhDs in things like ML (often making sacrifices to do so) because they want to advance the field - not to make it harder! People are excited. They are wary about hype causing another AI winter, or a lot of important projects failing for silly reasons.