Look, I'm happy that machine learning is hot and it's an interesting research area with some good applications. That said, it's ridiculous to assert that all of the sudden that all of these mature fields are dead. Control algorithms will always have a place because these dynamical systems are based on classical mechanics, which are really accurate for an extremely broad section of the world that we care about. Machine learning algorithms build models using generic functions that don't have information about the underlying physics. Now, could there be hybrid approaches? Absolutely. However, till that point, having an algorithm that works directly on the kinematics of the model is a huge advantage and that's not going anywhere anytime soon.
That said, there are many problems that troubled the earliest philosophers that still trouble the latest philosophers.. ¯\_(ツ)_/¯ .. but computers do pose themselves as a fundamentally-new tool.
I couldn't disagree more. It's incredibly amazing that control theorists spent so much time working on analytic solutions to dynamical systems, so much so that they're ubiquitous and modern technologies depend on them.
Huh? MuJoCo was primarily developed by Emo Todorov and his lab at University of Washington, and now his company.
The recent successes of Alpha Go, Alpha Go Zero, as well work at OpenAI and Berkeley (especially on the incredibly physically-accurate simulator MuJuCo) show that the era of classical controls is dead. Indeed, it may be the case that seeking analytic solutions to problems like this is an artifact of a time when computation was expensive and human time was cheap.
In fact, recent work on robots learning to play with themselves from OpenAI [https://blog.openai.com/competitive-self-play/] suggests that in the near future, humans won't be needed for designing these algorithms at all!