Comment by colllectorof | Hacker News Reader

Comment by colllectorof | Hacker News Reader

This reads like a mix of a disingenuous PR statement and a typical agenda-selling article from some mainstream news org.

Let's look at just one sentence:

>We've also had the chance to see something that's never happened before: DeepMind's AlphaGo took on and defeated legendary Go player, Lee Sedol (9-dan professional with 18 world titles), marking a major milestone for artificial intelligence.

Hype-inducers: "never happened before" "legendary" "marking a major milestone"

Also, AlphaGo didn't "take on" anyone. It plays whatever games are fed into it. It might seem insignificant, but such small details is exactly how most of marketing works. That is how we get "ultimate" luxury cars, "curious" banks, and insurance providers that are "always there for you".

>And because the machine learning methods we’ve used in AlphaGo are general purpose, we hope to apply some of these techniques to other challenges in the future.

AlphaGo's design has a lot of stuff highly specific to Go.

This is legendary. Most people (including me) would have thought this would not be possible for decades.

9-dan is the highest rank in Go. It is not possible to play against anyone higher.

So I am not sure why you think it isn't a big deal.

Because it's a fucking board game. There are tons and tons of more practical and impressive computer science achievements that get absolutely no news coverage.

Like what?

Oh, gee, I don't know. The first recent thing that comes to my mind and involves ML is a package for fingerprinting software via changes in computer's power consumption. Not just that, but using it to detect malware on medical devices (which often run decades-old OSes and aren't upgradeable). Is that groundbreaking enough for you?

That's "just" figuring out what's running on a computer.

I assume you're talking about this company? http://www.pfpcyber.com/

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?

Sounds like these medical devices are upgradeable if you can install malware on them.

So there's a lot of regulation and compliance stuff that you run into if you start dealing with computers in a medical context. On the face of it, this makes sense; you don't want to install software that hasn't been rigorously tested and approved, because the cost of having a computer fail can be human lives. If you have something that works, it's hard to get the inertia to change it because by the time you've finished testing a new piece of software to make sure it's as stable as you need, it's already out of date.

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.

> more practical and impressive computer science achievements

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?"

Graph coloring problem is generic and is guaranteed to be useful in a myriad obvious applications. Plus, "polynomial time" is a specific criteria, whereas "somewhat better than a human champion" is an arbitrary milestone.

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.

I'm not trying to troll you here, but have you ever actually played Go?? Go is so complex that the word 'complex' doesn't even begin to cover it. It's a game that requires equal parts creativity and logic. The sheer number of options absolutely dwarf anything else out there.

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.

I have played Go and I understand that it is a complex and nuanced game. However, I also understand that it's a game very heavily based on patterns and pattern-matching.

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.)

NxN Go is PSPACE-complete.[1] That's an even bigger deal than NP-complete.

[1] According to some comment in /r/MachineLearning the other day. I haven't looked into it myself.

That is with some bounds applied to it - if we allow unbounded games (ko, superko, etc) then it is EXPTIME-complete.

Yes. Source here: http://senseis.xmp.net/?ComplexityOfGo

To be fair, AlphaGo can only play 19x19 Go. It's unrelated to solving NxN Go.

And written language is just a string of squiggly lines...

Are you seriously failing to understand the implications.

Spot on. Question is if the entire 'AI' discussion is fair or also hyped by marketing departments. Is AlphaGo really AI, or is it an algorithm focussed on finding the best move to win at the game of GO. When in game 4, people were talking about 'it making a mistake', was it really a mistake. Did it choose the wrong move or did it simply make the move that the algorithm had put forth.

I think they're hinting that the techniques used in AlphaGO are a promising path towards general AI. AlphaGo has NNs trained on Go boards, but champion go players will also have parts of their brain dedicated to their Go intuition that don't transfer to investment banking or playing golf. However the techniques of reinforcement learning can be used to tackle other problems... so we hope.

Also this reminded me of a comment I read last week: "Don't anthropomorphize computers. They hate that"

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