A wave of research improves reinforcement learning algorithms by pre-training them as if they were human.
Imagine that your neighbor calls to ask a favor: Could you please feed their pet rabbit some carrot slices? Easy enough, you’d think. You can imagine their kitchen, even if you’ve never been there — carrots in a fridge, a drawer holding various knives. It’s abstract knowledge: You don’t know what your neighbor’s carrots and knives look like exactly, but you won’t take a spoon to a cucumber.
Artificial intelligence programs can’t compete. What seems to you like an easy task is a huge undertaking for current algorithms.