Interpretable Machine Learning Through Teaching
When I teach others, I usually rely on a lot of metaphors/analogies.
For example if I had to describe gradient descent to someone, I wouldn't start with a definition from calculus. I would instead describe it as climbing down a hill.
But that assumes prior knowledge of what a hill is (it has gradients) and what climbing is (you wanna go to the lowest point) and what the physics is like (go towards steepest direction with some momentum). This isn't something that I expect a blank-slate AI to be able to produce.
In short, explaining things intuitively to another human requires knowing about common human experiences and mode of thought. It's the "transfer learning" for humans. This seems like the realm of AGI and not something I expect to see solved well anytime soon. I hope I am wrong!
(Note: even in this example I assumed you understand the experience of trying to explain gradient descent in order to explain my point)
Moving the knowledge from one machine to another via learning rather than trying to copy in some form or another seems like a good approach. You don't limit the architecture of the learning or teaching implementations. It even works if the learner is a human, which is a bit different architecture :)
I also like the idea of having teaching output parsable by humans. Then you could ask your teacher AI, teach me (show me) what you know.
This is great stuff. I actually think the obvious application here is foreign language instruction. Particulary chinese character recognition for english speaking elementary students. Most teachers at that level will probably not have mastery of the language themselves. In addition, the "teacher agent" may be able to find patterns that would dramatically boost recall.
Additional meta-learning research from openai posted recently on arxiv:
Evolved Policy Gradients
https://arxiv.org/pdf/1802.04821.pdf
As well as the Ray distributed AI system: