AlphaGo's premise is that instead of using human feedback for reinforcement learning, you instead have the model play games against itself, with a simple reward mechanism, so that it can learn from its own mistakes. This achieves scalability of the training data, allowing the model to discover new Go moves and eventually exceed the quality of its initial training data.
From an engineering point of view, how do you see this applied to other areas like software development, where there is no opponent player? Do you connect the model to a compiler, and have it learn by trial and error based on compiler output? Do you set desired software outcomes and have another AI evaluate how much closer or farther the output is with each iteration? How would this closed feedback loop work to get an AI to become a world expert in a specific programming language or framework?
The biggest difference I've seen between Academia and random wannapreneurs is the patents and deal structuring. If you've already spent 5 years doing research and coming up with knowledge that noone else has, that is a product and execution in itself, even before commercialization. Universities patent these and structure deals so that if it is commercialized, the University gets commission.
On the other hand, someone coming on reddit and asking how to protect a random ass idea like a "Dislike" button that you can install on any website (opposite to FB like button), that they came up over drinks at the pub, then that is what noone cares about. It doesn't have a 5 year long reasearch effort by a team of 11 behind it, and that's the reason why anyone could come up with such an idea.