Machine learning requires a large high-quality dataset, which a lot of companies simply don't have.
Building one takes a lot of time and money. The gains don't outweigh the costs in many cases.
Another problem is that machine learning models are never a 100% correct and not easily interpretable, so they cannot be used for some critical processes.
Good luck with explaining a customer why his account blocked due to a false positive made be the AI.
I think there is still a lot of potential for boring symbolic AI, in a lot of domains you can get results quickly, reliably and if the AI is wrong it's easy to debug.
They have been flooding many of the outlets with counterfeit emotional capital (social/MSM) and counterfeit intellectual capital (psuedo-science/education/politics) for quite some time now.
As long as the "I" or the "L" in those fancy acronyms know the difference, the quality of the data will not be in dispute.
Even without AI/ML, it`s been obvious how bad data integrity is in the most basic sense of the meaning.
Another problem is that machine learning models are never a 100% correct and not easily interpretable, so they cannot be used for some critical processes. Good luck with explaining a customer why his account blocked due to a false positive made be the AI.
I think there is still a lot of potential for boring symbolic AI, in a lot of domains you can get results quickly, reliably and if the AI is wrong it's easy to debug.