The fighter jet AI technique is hard-coded to a very specific problem domain, and could only be reproduced in a different problem domain by doing it from scratch.
The technique used by AlphaGo is at least closer to the idea that we can eventually build generically trainable machines that can learn to do a variety of tasks, without having to code them from scratch every time.
So are machine learning models, ultimately. Although the algorithms that build the models have more general application, once you train a model on a certain set of data, that model can only ever be used in the domain circumscribed by the data. They're one-trick ponies, yes?
>> we can eventually build generically trainable machines that can learn to do a variety of tasks, without having to code them from scratch every time.
We would however have to train them from scratch every time, for each separate task, and each time we'd need terabytes of data and megawatts of power.
AlphaGo has several handcoded training features, uses Monte-Carlo tree search, and was primed by a huge human-made dataset. It's not like it can be throws at other problems without heave re-engineering.