If we came out with a polynomial time solution for the graph coloring problem, you could use that same logic to say "it's just labeling colors, what's the big deal?"
Besides, people who make advances in abstract problems like that do not release PR statements and get 1/1000th of the hype and coverage AlphaGo received recently.
This achievement will be useful in a myriad of obvious applications. And, it is one of the greatest engineering feats that I have been alive to witness.
Have you read the research paper and seen how much of AlphaGo's architecture was designed around the specifics of Go gameplay? (Including a set of handcrafted Go-specific training features.)
[1] According to some comment in /r/MachineLearning the other day. I haven't looked into it myself.
Are you seriously failing to understand the implications.
That's super cool, but no I wouldn't say it's more impressive or groundbreaking than AlphaGo. They're both obviously incredibly impressive though.
I guess I don't get the point you're trying to make. Why does something have to be practical to be impressive? And why don't you think the technology used in AlphaGo is practical?
So in one sense, those machines are unupgradable; you can't upgrade them without violating a number of laws and regulations.
In another sense, they're trivially upgradable; if you don't care about violating laws and regulations, you've got a playground with a number of highly documented vulnerabilities and publicized exploits.
The sorts of people who would install malware on those computers are the sorts of people who aren't really fussed about "laws and regulations"; that's not a position that people running a hospital are really able to take, though.