What's interesting about this particular paper, is that it can learn from a single demonstration. Instead of taking thousands of examples before it can do anyth

What's interesting about this particular paper, is that it can learn from a single demonstration. Instead of taking thousands of examples before it can do anything. Even cooler, it learns how to learn. They didn't hand code how to do this, it figured it out through training. On top of that it uses machine vision to learn to see all on it's own, without having to be hand coded with information about where things are. More generally, it uses a deep neural network. Which is very different than the older approaches you mention, and can learn much more sophisticated functions. And has enabled a lot of results that would have been unimaginable previously. As for the early AI researchers? They were insanely overly optimistic about how difficult AI would be. It didn't seem like a hard problem. They thought they could just assign a bunch of graduate students to solve machine vision over the summer. It seems pretty simple. We can see great without even thinking about it, how hard can it be? But after biting their teeth into it a bit, I'm sure they would appreciate our modern methods. They might not be as elegant as they hoped. They wanted to find "top down" solutions to AI. Simple algorithms based on symbolic manipulation and logic and reasoning. Such an algorithm probably doesn't exist, and an enormous amount of the history of AI was wasted searching for it. And even if they did discover our modern methods much earlier, they wouldn't have been able to use them. It's only recently that computers have gotten fast enough to do anything interesting. It's like they were trying to go to the moon with nothing but hand tools. Sometimes you just need to wait for the tech tree to unlock the prerequisite branches first.