Before 2012, you also couldn't run a system to classify among 1000 classes. Just because RL isn't there yet, doesn't mean it's not worth doing.
The reason we're discussing hardness of RL is fast.ai's narrative of "you don't need math for AI" and "AI is easy". Sure, implementing and applying AI is easy, and you just need to learn tensorflow, but doing even a modicum of novel research in RL requires a tremendous background in all kinds of math. I appreciate what fast.ai is doing to democratize as much of AI as possible, but that doesn't need to be at odds with other people prioritizing RL research.
fast.ai's narrative is "AI is easy for what you probably want to use it for". There are a ton of awesome applications that are enabled by the level of AI taught in the course. However, Rachel's article is about how AGI is actually really, really hard. So much so that we have no idea how to get there and can't predict when it will happen. So instead of fearmongering about AI, we should instead be encouraging everyone to do awesome new stuff with AI.
The reason we're discussing hardness of RL is fast.ai's narrative of "you don't need math for AI" and "AI is easy". Sure, implementing and applying AI is easy, and you just need to learn tensorflow, but doing even a modicum of novel research in RL requires a tremendous background in all kinds of math. I appreciate what fast.ai is doing to democratize as much of AI as possible, but that doesn't need to be at odds with other people prioritizing RL research.