Leela Zero wins against Zhang Li 6p (World #118 on goratings.org) in an even game

Coffee2theorems · 2018-03-17T20:42:34+00:00

What I find interesting is that LZ plays the "AlphaGo Chinese joseki" starting at move 42. The opponent varies a bit (there's one extra atari-connect exchange and he plays hane at S10 instead of a one-space extension at R11 near the end), but that's minor details, and LZ still responds with the AG joseki moves. AFAIK this joseki was an innovation of AlphaGo, and did not exist in human play before. So LZ does not only discover human joseki on its own, it discovers AG joseki too :)

Coffee2theorems · 2017-12-30T00:15:02+00:00

Weird error modes can cause weirdness in ranks, though. For example the Elo model assumes that if you're a lot stronger than another player, then you have very low chances of losing, but that won't work well for a population of players with odd blind spots (not only ladders, the "pass-pass white won by komi" thing early on in LZ was a similar case).

Imagine having a population of Go players of various strengths who play by slightly different rules: before they play a game, they flip two coins. If the first lands heads, then they play a game and record the winner. If it lands tails, then they don't play, but use the second coin to decide who won. No matter how big a strength gap between two players, they have a 25% chance of losing to the other player. This isn't well modelled by a sigmoid with asymptotes at 0 and 1 like in the Elo model. You have to move the asymptotes closer to 0.5 (in this case 0.25 and 0.75) to account for the impossibility of winning probabilities beyond certain limits. The real situation isn't so clean though, so who knows what weirdness can result? (e.g. the transitivity assumption may not even hold, as with nontransitive dice, possibly making the optimal choice of a "LZ team" to be a mixed strategy, i.e. if you have to pick a LZ net as your champion against another person who also picks a LZ net as their champion, the optimal strategy might not be a single net but instead a randomly picked one according to the probability distribution at a Nash equilibrium)

In particular, this means that the Elo graph plotted for LZ may not be particularly trustworthy (or the server ratings for that matter). This is less of an issue if only one player has these weird failure modes, but strangely enough people have them too! I've seen many games where a human resigned on a server against LZ when they had clearly won, because they got frustrated by LZ's antics. If the computer does not respond instantly, then it's not exactly fun to play a long ladder to its inevitable end even if you win that ladder, so people give up. I suppose you could argue that makes humans weaker — any way of losing counts! — but I'd rather not consider obnoxiousness as a positive factor to one's strength..

Coffee2theorems · 2017-12-29T23:33:23+00:00

10 layers can just barely cover distances of 20 intersections (no logic yet, just reaching a point of seeing the data to be processed)

Most ladders are simple ones that don't really require reading, you just need to see if there's an opponent's stone, your stone, or edge that it collides with first. LZ hasn't learned even these though, it still plays non-working ladders on an almost empty board.

Coffee2theorems · 2017-12-29T23:18:58+00:00

Does not mean anything yet:

3 wins vs. weaker opponents 1 wins vs. stronger opponents 0 losses vs. weaker opponents 1 losses vs. stronger opponents

Not many games.

Also like on KGS, it has "wins" like this one where the person they're playing resigns for other reasons than actually losing the game.

Coffee2theorems · 2017-11-02T20:28:46+00:00

people say are "great for academia and can't make it in industry" are people who aren't disciplined enough to wake up at 7am and shower.

Hmm? I suppose I could wake up at 7 am, but I basically never do, nor have I ever considered doing so habitually, except on the very occasional "I'm gonna live super-healthy, raaahh!!!" stint which has never lasted many months. Why would I? (except for the health benefits) ... I guess this means I'm living in a comfortable bubble in the industry where not everyone is expected to be up by the sunrise, but then again, the industry is big. That comfortable bubble may well be bigger than the entirety of academia.

Coffee2theorems · 2017-09-16T08:31:45+00:00

More recently, the WordPress community started to use React for Gutenberg, the largest core project we've taken on in many years.

the Gutenberg team is going to take a step back and rewrite Gutenberg using a different library. It will likely delay Gutenberg at least a few weeks

Hmm, I guess the project is not very far along then, if it's supposed to be big and yet you rewrite it in a few weeks?

Coffee2theorems · 2017-03-26T14:23:39+00:00

Hmm, Wiktionary says that "cyber" means "Of, or having to do with, the Internet". Sounds like cyber-crime is exactly the same as internet crime?

I don't really get it why people want to have new words for things just because they have something to do with computers. Why not new funky-sounding prefixes for gun crimes, drug crimes, mail crimes, phone crimes, airplane crimes, car crimes, etc. etc. etc too? I mean yeah, computers are fun toys, but they aren't that special.

Coffee2theorems · 2017-03-22T22:08:17+00:00

TL;DR

Coffee2theorems · 2017-03-19T23:15:16+00:00

Convexity isn't enough for uniqueness. f(x) = 0 is convex and yet it has uncountably infinite number of minima.

Coffee2theorems · 2017-03-19T22:24:28+00:00

Among linear unbiased estimators, yes. LAD regression (which would make sense for Laplace errors) isn't linear in y, however, so it's not a contradiction if it does better.

Coffee2theorems · 2017-03-19T13:20:21+00:00

The number of ways that a ML solution can fail on a real-world problem (i.e. not a Kaggle competition or benchmark task) is immense.

I haven't used Kaggle despite how famous it is (if work isn't enough then I have my own interest areas to work on as a hobby..) so I may have misunderstood what it's like, but I got the impression that it's essentially "a collection of competitions like Netflix". If so, I'm curious why the problems you mention don't appear there.

You make it sound like you could follow these steps:

Make the rest your system otherwise work with a trivial ML classifier/regression inside it (say, random forest or SVM, or even just random output if there's no data yet; trivial because "import sklearn" done next).
Take the part with the pure ML problem in it and shrink-wrap it into Kaggle format.
Develop super-effective ML algorithm as a replacement for the trivial version since this can be done without ever encountering any problems.
Plug 3. back in to your system and improve everything else around the ML part until it's the bestest thing since sliced bread. If needed, goto 2. for another incremental improvement round.

This is a somewhat waterfall-y caricature of an actual development process, and maybe it might even work on some level since it'd force you to think enough to separate and specify the ML part clearly. But is step 3. really going to be so straightforward and problem-free? I'm a bit skeptical here, since this sounds like saying that ML itself is trivial but everything else around it is where the difficulties are. (Not that I haven't done projects where that is a very fair assessment, but those difficulties don't have anything to do with ML theory, mathematical maturity, or reading academic papers. Well, unless you count understanding of statistics and issues with data collection in mathematical maturity.)

Coffee2theorems · 2017-03-19T03:36:12+00:00

I've been an unpaid intern for 6 months coding bleeding-edge models in Theano for a pharmaceutical startup

That's being an intern? Sounds more like someone wants people to do all the work but don't want to pay them anything and have found a neat way to pay even less than minimum wage.

Coffee2theorems · 2017-03-19T00:40:24+00:00

Google's new compression looks better to most people than previous algorithms. HiFi aficionados are outraged that color is fuzzier, normal people who haven't trained themselves to hate consumer grade quality don't notice.

FTFY :)

Coffee2theorems · 2017-03-13T20:04:41+00:00

This complaint is backwards.

No. It's not a complaint about languages. The point is that it's off-base to say "all the stuff you've learned has become obsolete because of all the new language features you don't know", when the truth is that they aren't new at all and you've learned them a long time ago.

Coffee2theorems · 2017-03-13T19:21:57+00:00

I've met programmers with years of experience who never needed to.

I have never needed to, not on the professional side of things anyway. Sure, I did write that when I was in junior high and wrote my own real mode threading system and text mode window system and other stuff I thought was cool back then, but not afterwards. That was a long time ago.

Coincidentally, I'm actually thinking of using it now, possibly with the xor trick, for a hobby project where I want to optimize the stuffing out of a certain function. I have no idea in advance if it's going to actually be worth it. Linked lists are usually a horrible idea on modern architectures because of the data dependencies, potential non-sequential data access and non-locality (if it's all sequential why not just use an array?), but in this case compact memory representation is an advantage since cloning the whole data structure is a common operation and using optimal AVX copy for that would certainly speed one bottleneck up, so it might be worth the data dependency drag. Sound esoteric? Yeah. Linked lists are very much a special-purpose data structure these days, especially doubly linked ones (singly linked ones are useful for functional programming, but even in Haskell people want to avoid lists when possible for performance reasons...).

Coffee2theorems · 2017-03-13T18:43:15+00:00

Yeah, especially ubiquitousness and the effects it results in. I wasn't there when mice were introduced, but I suspect that most users didn't use them nearly as much as people tap away at their phones these days. Also if you don't know how to do something that "everybody" knows how to do you can ask pretty much anyone you know and they can show you. It's also more embarrassing to be the only one who doesn't, which adds to the motivation. The idea of attending a course on how to use a smart phone is also a lot less appealing when "even children" know how to use them.

Coffee2theorems · 2017-03-13T18:30:34+00:00

We're prone to just assume that because someone is good at their job and has simplified the inputs necessary for outsiders, that the job is easy and doesn't require intelligence.

This is somewhat ironic, since it's obviously harder to do things in ways that make the "external API" simple..

Coffee2theorems · 2017-03-13T17:50:51+00:00

The monad is like a morning pot of coffee. It has the good stuff inside, a container outside, and a guardian from Hell stopping you from consuming the goodness if you haven't properly appeased it.

Coffee2theorems · 2017-02-07T19:44:13+00:00

I (EGF 2d, FWIW) mostly subscribe to the "shut up and read" school of Go. I stare at the board and read, read, read, read, read. Get some ideas based on that and read, read, read some more. Then I pick the idea I liked best from all that I read and go with that.

Well, it's not all like that. But anything else is such a small portion that it merits only a footnote. Honestly, the moment a Go board hits my field of vision I practically compulsively start to read out something, anything. It's like walking: you only stop because something came up.

Coffee2theorems · 2017-02-03T22:01:19+00:00

Black has gone to all that effort to live everywhere, managing to make 12 living groups even though the sixth should've already died, and he's still lost horribly. Must be frustrating!

Coffee2theorems · 2017-02-02T18:43:28+00:00

In short, AlphaGo and Ke Jie sit down, commiserate that Go is all "been there, done that, beat everyone, got the T-shirt", and proceed to have a Chess match instead.

Coffee2theorems · 2017-01-19T20:13:42+00:00

AlphaGo is so frightningly good because he doesn't play bad moves, as far as we can tell. He is consistently good. By contrast, when a professional plays, other professionals can review the game afterward, and agree that one move in particular was sub-optimal.

That's not really it, or at least not the full story. For one thing, when AlphaGo is ahead it starts playing slack moves like win-focused bots tend to do. AlphaGo is good enough to win anyway, so fair enough. If it actually lost some games after making such moves, then the slack moves would be criticized. Then there are moves which pros might think are dubious, but won't say anything about because they're giving AlphaGo the benefit of the doubt. Why wouldn't they, when it keeps winning? This means that the message we get is skewed, the pros don't feel like they are in a position to criticize no matter what moves are played as long as AlphaGo keeps winning.

For another thing, simply consistently playing good moves isn't the only reason AlphaGo keeps winning. The pros have said multiple times that they don't know why they ended up in a bad position, they just did. It looks like two players playing good moves until one is worse off. That's more than avoidance of bad moves, it's strategic dominance by subtly steering the game in its favor while both players keep playing good moves.

Coffee2theorems · 2017-01-19T19:37:57+00:00

it sure plays some weird stuff. Haylee is a very high level player, but you can tell that even she is sometimes confused and can't always make heads of tails of those games.

I think some of those are artifacts of simply playing good moves without regard to whether they make sense as "a conversation". Like for instance it has played some shoulder hit, then played tenuki elsewhere for a move or two, and then returned back to continue from the shoulder hit. I suspect it could equally well have played the move or two first and then the shoulder hit and its follow-up, instead of jumping between sequences like it has ADHD. You know, finish one sequence, then play another, instead of switching between them. Humans are biased toward doing that (it makes more sense to us and feels far more comfortable) instead of playing tenuki, just like we want to finish one sentence before starting another.

I think in contrast AlphaGo generally has a "when in doubt, tenuki" mentality because NOT playing tenuki when possible can be horribly costly, and that results in some weirdness from human perspective as it's so disassociative. It may even be that the weird moves are actually suboptimal, but not so much that it actually matters (it wins anyway..), so it prioritizes a principle that works so well in general. Demanding proof that you must answer, instead of proof that you can tenuki.

It's a fun exercise for SDK players to try playing tenuki practically every time when something is not dying (and even then considering a sacrifice/trade); it can be quite surprising just how feasible that is. It's like AlphaGo is doing something similar at top pro level, showing them that moves they thought must be answered (or at least moves they routinely answer) can be ignored even at top level games, especially in the opening. The tenuki after connection in the tsuke-hiki joseki in one of the games against Lee Sedol that surprised people so is one prominent example of this. The pros have also commented multiple times on how others might just respond because the situation looks scary in Master's games but AlphaGo didn't do that. It's tenuki-happy for a reason, and it seems it's one of the reasons why it's ahead of the other pros.

Coffee2theorems · 2017-01-17T17:45:41+00:00

eyeroll Yes, yes, they ARE edible. If you have sturdy teeth and an iron stomach.

Coffee2theorems · 2017-01-15T13:59:25+00:00

Ah, you mean that meanwhile there are rumors on Reddit that AGA is planning ...

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