When dealing with a high-complexity, high-uncertainty problem, if you think you know what you're doing, you probably don't know what you're doing.
Be aware of your own biases & weaknesses, and hedge your potential mistakes
Not committing your entire career to one specific tool enables you to be more objective about its strengths, its limitations, and its future potential.
If we remove all design constraints, the optimizer came up with a really tall bipedal walker robot that “solves” the task by simply falling over and landing near the exit.
We haven't yet solved even 10% of the problems we could solve with existing AI/ML techniques. Even if new research were to deliver nothing from now on, there still wouldn't be another AI winter. AI/ML will keep on delivering for years to come.
Also, I don't like the view that a lack of motivation in the context of education is a personal failing. That's like saying that not having fun while playing a video game is your fault, rather than the fault of the game designers. Motivation should be part of curriculum design.
“Instead of trying to solve every problem with a single language, in my case: C++, and become an entrenched ‘C++ expert’, it is much more enjoyable and productive to learn a few different languages and pick a language that naturally fits a problem.” https://t.co/yoHST4RVRa
Playing hard exploration games by watching YouTube (DeepMind). One-shot imitation allows an agent to exceed human-level performance on the infamously hard exploration game Montezuma's Revenge, even if the agent isn't presented with any environment rewards. https://t.co/kpsFtfJH2W
Rocket Lander OpenAI Gym environment inspired by SpaceX's Falcon 9 vertical landing, using Box2D physics engine. They experiment using PID, MPC control benchmarks, and also tried DQN, DDPG ... https://t.co/egb9iyCtPS