Funny that up until 2016 go was regarded as one of the most difficult games that computers could master, and now that it is solved it becomes a PR stunt? Would you claim the same in 2015?
It was a hard problem, and that's what makes it an effective PR stunt.
Google was working on machine learning for some practical application like image classification, better targeted ads or whatever thing Google does. A bunch of people then came up with the idea: "hey, we have all that AI stuff, we may be able to use it for computer go". And Google replied with "OK, sounds like good publicity, here is a budget, we also have a bunch of servers and if you need help, feel free to ask our machine learning department".
It is like making an industrial robot that can crush concrete blocks or whatever difficult but not that useful task. Maybe it is a huge deal because all previous attempts failed, but the point here is not that years of research in concrete crushing robots have payed out, but rather that recent advancement in practical engineering made it possible, and maybe even easy.
That's not true though. Google bought a company whose main product was a Go machine, on the idea that potentially those smart people could do useful work also. Or just as PR cover for their unrelated AI work.
The problem with the AI Effect is that people keep expecting solving one toy problem or another to give us some insight into how to break Moravec's paradox for the general case. Statistical learning, especially deep learning, have been massive advances precisely because they at least allow us to break the paradox for specific problems, where we happen to have large datasets.
Yes, I think von Neumann made this claim somewhere already that AI is a moving target, because once some task is automated it doesn't seem intelligent anymore.
Google was working on machine learning for some practical application like image classification, better targeted ads or whatever thing Google does. A bunch of people then came up with the idea: "hey, we have all that AI stuff, we may be able to use it for computer go". And Google replied with "OK, sounds like good publicity, here is a budget, we also have a bunch of servers and if you need help, feel free to ask our machine learning department".
It is like making an industrial robot that can crush concrete blocks or whatever difficult but not that useful task. Maybe it is a huge deal because all previous attempts failed, but the point here is not that years of research in concrete crushing robots have payed out, but rather that recent advancement in practical engineering made it possible, and maybe even easy.