I don't think I underestimate the speed nor velocity of the advancement, just being realistic about the scope of the advancements. I follow conferences and read

I don't think I underestimate the speed nor velocity of the advancement, just being realistic about the scope of the advancements. I follow conferences and read ML papers (DL & DL/ML application in Robotics) regularly so I think I have a some grasp on the advancement in the field. GAN sure have been a big step, and have found a great usage. (deepfake etc) but I'd say it is still highly overblown. Like GPT3 it will see wide adaptation enough to make visible improvement (and problems) in certain industries, just as better classifiers changed a lot. I also don't like the blanket term of "automation" because it is like AI: too vague and often used by someone who don't fully grasp HOW hard automation tasks are (and how the complexity scale and differs from the task ). Just as optimism in Self-driving car is now more on low-fire. The over-promise of ML is something akin to people expecting "it to just get better" similar to Moore's law. If you take a look at what direction the algorithms are improving, what incremental advancements are being made the scope of the speed and velocity become much more clear; and just because cars get faster, doesn't mean it will one day fly to space like a rocket. If we one day in short term future see great improvement, it will probably not be the ML improvements, but due external factors (hardware, network speed, etc)