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Linch asked for more people to write basic AI safety explainers[1]. Here's mine. My main method is to ignore all the fictional evidence[2] and think about how achievable and how dangerous building AI seems just extrapolating from contemporary tech and how that has changed as the tech has advanced. My main thesis is that achievability has gone up with time, but the danger has varied and the present danger is serious but different from the classic fear.
Serious attempts at building machine intelligence started seventy-five years ago[3]. No one had any idea how to do it though. We had computers and we had dissected a lot of human brains, but no one really understood how to think or how you would make a machine do that. Respected scientists still thought that human reasoning might be literal magic, but the cutting edge knew it was actually chemistry[4].
About 50 years ago we had expert systems: massive piles of if-then rules that recorded what human experts thought and which programming languages like Lisp and Prolog made practical to write. If you had a million people writing down common sense and a million people programming how to move an android's motors, you might be able to make an artificial man, but the field struggled to get even a hundred people to do such tedious work. When this failed to achieve rapid success, no one could justify the funding, the AI winter set in, and the field mostly just gave up. The most serious holdout, Cyc, spent forty years on it without getting anywhere. Teaching a machine to be a good person was handwaved the same way as getting a machine to do anything clever at all.
Reinforcement learning was an attempt to make the computer do the learning, so you wouldn't need an army of experts to do the teaching. Just like how you train a dog with rewards and punishments, you could train a computer by defining a goal and letting it find its own way. TD-Gammon was the first major success. It learned backgammon by playing millions of games again