skysight-inc/HackerNews huggingface dataset:
Thank you :)

I will probably need it in real life in the next couple of months for a computergraphics project. So I thought, why not make it look nice and put it up there.

Happy, if it helps someone!
Telling people that living as they wish "sucks" doesn't make you right about them, it makes you a bigot.
Lee Sedol winning, and keeping his cool and not make any mistakes. AlphaGo, on the other hand, went bonker especially towards the end but it got into bad territory not because of silly mistakes but brilliant play by Lee Sedol.

Could it possibly be that both of the mistakes were bugs? Perhaps it suggested a non-sensical position such as (25.23, 13.15), and it was snapped to (19, 13) :D
> it seems AlphaGo sort of bugged out and started making wrong moves

Didn't they say that it's not considered a "bug" but rather how AlphaGo "thinks"? "when it's winning it doesn't care about how much it's winning, and when it's losing it doesn't care how bad it's losing"
"same impact as turning around a struggling multi billion dollar business."

You make it sound like solving a complex problem in concurrency or memory management or compiler optimization or power management (that may directly and layer-indirectly affect millions of users, if not billions of devices) is "less impact" then a human-goal management problem of a company like Yahoo.

Perhaps you believe Mayer's difficult task of specifying the right color of purple for the Yahoo Mail was somehow high impact?

""one day before Yahoo Mail was set to release, she convened a meeting at Phish Food, a conference room in the executive building of Yahoo’s campus, to talk about the product’s color. For months, the team had settled on blue and gray. If users were going to read emails on their phones all day long, the thinking went, it was best to choose the most subtly contrasting hues. But now, Mayer explained, she wanted to change the colors to various shades of purple, which she believed better suited Yahoo’s brand.

According to one senior executive, Sharma’s body language changed the moment Mayer issued her request. He looked deflated. Altering the color of such an intricate product would require that members of his team spend all night adjusting colors in thousands of places. He slumped off and prepared to tell his staff the bad news.

""

http://www.nytimes.com/2014/12/21/magazine/what-happened-whe...
The thing is, Lee Sedol didn't "find the right moves"; the wedge at L11 shouldn't have worked. If black's 79th move had been at L10 instead of K10, Sedol would likely have resigned on the spot.
> the idea that somehow the average person picking stocks can help make market efficient

The average person is supposed to pay someone to pick the stocks for them. I wonder who could promote this idea and rally against the alternatives...
Why can't they just leave the time alone, and change their working hours during summer and winter?
Google Deep Mind – What Is It, How Does It Work?
Rubbing drugs on gums is a popular way of administration, as the substances get easily absorbed into blood vessels here.
I would expect them to be running on Google's distributed infrastructure which has error corrections through end to end checksums
As far as I can tell from watching Redmond's commentary at the time, there were other options for white in the area.
The commentary from Redmond was illuminating for me, in his estimation having played against monte carlo simulations before, at that point the computer just plays very low probability moves with extremely high outcomes if the other player makes a grievous mistake. So the moves looked terrible since the 'retort' is obvious, but if for some reason LSD didn't play the obvious counter, it would catapult Alphago back into the lead. Of course since LSD is an expert, he never missed the retort, but very interesting behavior nonetheless.
it does seem like it is suboptimal behavior to keep making obviously dumb plays instead of attempting a comeback like a professional human would.
The title bar looks like it's GoGui, so it's just GoGui's reaction to a Go Text Protocol resignation?
Id bet E(X) >> E(Y) though.
I like this solution, except for a couple of things. One, what happens if I lose my Yubikey (or it gets stolen)? Another, more realistic, problem is what do I do if I don't have my Yubikey (at my parents house)?
Sometimes its just not possible to stop the snowball rolling down hill. You cant always turn a retreat into an advance or flanking move, sometimes the first step backwards just turns into a full on rout.

I got the impression (possibly incorrectly) that AlphaGo was trying to throw curve-balls and be 'unexpected' in a way that might have 'forced' a mistake it could exploit.
AlphaGo resigns

The result "W:Resign" was added to the game information.

Edit: Tinyyy is right.
I see by the top comments that in the end the responsibility still falls on human discipline. At least two people have already confessed to having no passphrase on their SSH keys and promising that they lock their laptop screen when leaving it unsupervised. This is very insecure regardless of disk encryption.
The crucial play here seems to have been Lee Seedol's "tesuji" at White 78. From what I understand this phrase in Go means something like "clever play" but is something like sneaking up on your opponent with something that they did not see coming. Deepmind CEO confirmed that the machine actually missed the implications of this move as the calculated win percentage did not shift until later.
https://twitter.com/demishassabis/status/708928006400581632

Another interesting thing I noticed while catching endgame is that AlphaGo actually used up almost all of its time. In professional Go, once each player uses their original (2 hour?) time block, they have 1 minute left for each move. Lee Sedol had gone into "overtime" in some of the earlier games, and here as well, but previously AlphaGo still had time left from its original 2 hours. In this game, it came down quite close to using overtime before resigning, which is does when the calculated win percentage falls below a certain percentage.
The mothers who setup a radiation lab
okay human race, let's sit back and enjoy our last moments of glory!
Not to defend your particular dentist, but...

> I asked if we could just do an extraction but they only responded with "Oh you don't want to do that!" (playfully) but legitimately not saying it was an option.

I think dentists are culturally and by training pre-disposed to keeping your real teeth in your mouth. I've asked different dentists why and they get a hand-wavy, but they all say it's better if your real teeth are in there. This even happened in a scenario where I wanted a tooth removed and an implant put in - they would have made more money saying yes to me, but they pushed me towards other options.
They've been in overtime before. In game 2 I think. AlphaGo spent about 30 seconds on each move
Food production takes up almost half of the planet's land surface and threatens to consume the fertile land that still remains, scientists warn.

The global impact of farming on the environment is revealed in new maps, which show that 40 percent of the Earth's land is now given over to agriculture.

http://news.nationalgeographic.com/news/2005/12/1209_051209_...
Care to explain why this was downvoted?

I honestly don't understand how did the authors manage to make a skeleton (!) page not passing validation. Even <title> is empty when it must not be so.

Is it because of Angular? Well, if a framework forces you to write invalid HTML (tags, attributes, whatever), it sucks, no matter what.
I think it reads W:Resign
the offset from UTC is arbitrary anyways. it is no more arbitrary to keep it at UTC-7 instead of UTC-8.
It was in overtime in game 2, I believe. It did fine.
I think it's more that the value network includes moves which look plausible but won't concentrate around the answer to a forcing move as having >99% probability. A human has a heuristic: "I must play here else I lose" but AG assumes its opponent might play anywhere that the ANN calls reasonable.
It's called scorboarding. You start coming up with solutions and ranking them and putting the best one up on the scoreboard. When you run out of time, you just go with what you got. Pretty much what humans have.

I'm sure there are many levels of watchdogs in this program.
The First Image Ever of a Hydrogen Atom's Orbital Structure
Which means the employer needs, in cash, enough money set aside to cover the liability of ~35% of fmv(not strike) of all rsu issued. This really is unlikely to happen at an early company.

What you really want, if you join at a point when you can afford to buy it, is to get options and buy and file 83b so you have zero tax owed.
I couldn't find out who is behind this organization but I did find this:

http://www.patentprogress.org/2015/02/02/real-inventor-adven...
great article i have seen linked here before: http://qntm.org/abolish
I was on the other stream; Myungwan Kim and Hajin Lee went way deeper than Redmond typically goes (since they have access to an SGF editor instead of a clumsy demo board, and they aren't performing for the camera as much). They seemed pretty confident in their conclusion that L10 killed white.
After AlphaGo won the first three games, I wondered not if the computer had reached and surpassed human mastery, but instead how many orders of magnitude better it was. Given today's result, it may be only one order, or even less. Perhaps the best human players are relatively close to the maximum skill level for go, and that the pros of the future will not be categorically better than Lee Sedol is today.
>A 2014 study showed that the hour of sleep lost when switching to DST can increase your risk of a heart attack by 25%

I highly doubt that one less hour of sleep on one day of the year can affect that risk by so much. If that were the case, I assume the risk of heart attack would be much higher than it is now.
According to the article our behavior and therefore Freedom of Speech is being manipulated by these companies that in turn brings us full circle to the point that surveillance companies control our behavior and therefore our voting decisions and thus weapons.

Check out Eric Schmidt's ties with government:

http://www.naturalnews.com/051687_Google_Eric_Schmidt_Hillar...
> the offset from UTC is arbitrary anyways.

It manifestly is not. Noon on the west coast of the continental US happens about 8 hours after noon in the UK. That's why it's nominally UTC-8, not UTC-5 or UTC+12.

And regardless of how you define time relative to UTC, a permanent DST is a permanent 1-hour difference between solar noon and clock noon.
IntelliJ IDEA Plugin for proofreading markup texts (AsciiDoc, Markdown etc)
There's a term for this called "revealed preferences." If you keep saying "I wish I could work sane hours" but actually keep making decisions to work insane hours, chances are you actually really just want to work insane hours.

I spent a lot of time being confused by this, listening to perfectly healthy young people with good paying jobs and no attachments telling me "I wish I could just drop everything and go traveling for months on end like you do" and then just sort of trailing off. "But You Can!!!" I always wanted to shout back. You just don't actually want to do that. Or you would. What that person really wanted was what they had: a stable comfortable job and a nice car and apartment, practicing for the day when they had a wife, kids, lease & mortgage to solidify that reality of "can't go traveling". Sounds pretty comfy, actually. If only they could come to grips with what they wanted and be happy with it.

It might be worth stepping back and looking at what you actually want. You just might find it's what you already have.
Fastest Java JSON library
Whats frustrating is ISO fixes this exact situation, but AMT rules haven't really caught up with the current situation(they've barely changed since introduced iirc) - all they have to do is raise AMT to something more reasonable, or exclude ISO from AMT
Interesting article. Waterpik is a related product (as in, for teeth and gums) that a dentist recommended. Anyone have experience using it - pros, cons?
If you only count individual human beings you are severely undercounting

In addition to agriculture & food production, transportation (road+rail), strip/open pit mines, and you have just take a look at

https://karolinanwk.files.wordpress.com/2012/04/garbagepatch...
Middle East Cyber Security Summit 2016, 6-7 April 2016, Riyadh, Saudi Arabia
Awesomesauce!
Am I right by asumming, that if they would play another game (AlphaGo black and Lee Sedol white), that Lee Sedol could pressure AlphaGo into makeing the same mistake again?
ييلاق طرقبه در مشهد
If it's true that AlphaGo started making a series of bad moves after its mistake on move 79, this might tie into a classic problem with agents trained using reinforcement learning, which is that after making an initial mistake (whether by accident or due to noise, etc.), the agent gets taken into a state it's not familiar with, so it makes another mistake, digging an even deeper hole for itself - the mistakes then continue to compound. This is one of the biggest challenges with RL agents in the real, physical world, where you have noise and imperfect information to confront.

Of course, a plausible alternate explanation is that AlphaGo felt like it needed to make risky moves to catch up.
I dont think they were bugs in a traditional sense. I think AlphaGo picked moves to try and maximize the probability of winning, and at some point that was only by the opponent making a suboptimal response. I remember reading somewhere most of it's training data is from amateur games. The model doesn't have a prior that AlphaGo is playing a professional who won't make a bad response. It probably would have resigned a lot earlier with that prior :)

Another thing to keep in mind is that AlphaGo has no "memory", so every turn it looks at the board fresh. This means if the probabilities are very close you could have it jump around a bit either due to numerical noise from floating point calculations, model errors, or just tiny differences in probability making the behavior appear erratic and quick to change "strategy".
“He was too good for us today and pressured AlphaGo into a mistake”
Id not give them a discount. Im sure they can afford to invest 12k a.k.a. peanuts if they are valued so highly.
I think esturk meant that AlphaGo is barely beatable now, so by the time anyone else gets a chance, it will have improved in the meantime and even a stronger human player won't be able to beat it.
No, it's "The result “W+Resign” was added to the game information"

https://www.dropbox.com/s/c5730ibejbv4wle/AlphaGo.jpg?dl=0
Usually the nomenclature is "W+Resign"
So AlphaGo is just a bot after all...

Toward the end AlphaGo was making moves that even I (as a double-digit kyu player) could recognize as really bad. However, one of the commentators made the observation that each time it did, the moves forced a highly-predictable move by Lee Sedol in response. From the point of view of a Go player, they were non-sensical because they only removed points from the board and didn't advance AlphaGo's position at all. From the point of view of a programmer, on the other hand, considering that predicting how your opponent will move has got to be one of the most challenging aspects of a Go algorithm, making a move that easily narrows and deepens the search tree makes complete sense.
According to this picture[1], it is more likely "W+Resign". I'm curious why a plus sign is used instead of a colon!

[1] http://gall.dcinside.com/board/view/?id=baduk&no=109200&page...
If you're far north enough (which you would be, if you are "further north than most americans"), DST won't help. Sunrise in winter morning is several hours off. The entire day is distorted. Shifting things one hour is useless.
You're missing the role of the IRS and tax law here.

In order to grant ISOs (Incentive Stock Options), which are taxed less unfavorably than NSOs (Non-qualified Stock Options), the strike price of the options must be equal to or above the "Fair Market Value" of the underlying stock.

The "Fair Market Value" is usually determined by an independent valuation firm through a process called a "409A Valuation." Firms like SVB, Alvarez & Marsal, Capshare and others do this.

The later-stage the company is, and the closer to IPO, the higher the 409A valuation will be.

If a company were to grant below the fair market value, there would be several bad consequences:

1 - You would owe taxes on vesting -- this means you could owe taxes possibly as soon as the first day of work, and likely on your 1 year anniversary of work. You owe these taxes whether you exercise or not;

2 - The company would have to withhold income taxes on your exercise date, including "employer-side" payroll taxes.

Normally, with an ISO exercise, you may face "AMT" (Alternative Minimum Tax) but you might not. By contrast, you certainly owe taxes with cheap NSOs.

Blame the IRS.
What good will an encrypted home folder do if your computer gets compromised? It protects against offline, physical attacks, sure, but I fail to see how it is safe against the myriad of remote attacks there exists.
Do you think Lee could use this as a way to crack AlphaGo?
What is the connection between OO and infix vs. FP and prefix? Most FP languages use infix notation for operators, and Haskell even supports infix notation for all functions. Lisp does both OOP and FP in prefix notation.
Companies have to set the strike price based on the value of the company, which they must file with the IRS, and be able to defend(called a 409a). If youre pulling revenue you can't claim the company is worth no money...
John Resig described a way to store secret files in source control here [1]. But there is still a problem transferring encryption password securely.

[1] http://ejohn.org/blog/keeping-passwords-in-source-control/
Could the AlphaGo developers make modifications before the next game to prevent this happening again?
This is an interesting question - if Lee Sedol simply replays the game exactly, does he repeat a win?

I think the answer would be most likely not - the monte carlo tree search is randomized so AlphaGo's responses to Sedol may not be exactly the same, requiring Sedol to not be able to repeat the exact same play.
I would avoid thinking of this like a traditional computer program that just "bugged out" due to a glitch or a problem in the software. More accurately, it failed to account for the implications of a move on the board and therefore focused its attention in the wrong place. This happens often in games, I can imagine that a chess master playing an amateur might move his knight or bishop into a position that does immediately seem threatening to an amateur, so the amateur responds by moving a pawn somewhere else on the board, while the correct move would have been to attempt to counter the threat. As should be well known by now, in Go you cannot examine the implications of all possible moves, so some things will be missed. In this case, it seems something important was missed at the time, and the implications were not realized by the program until 10 or 15 turns later.
It was not a good point.

AlphaGo can’t decide that it’s bored and go skydiving. Humans aren’t merely capable of playing Go. And when they do it, they can also pace around the table, and drink something, all at the same time, on a ridiculously low energy budget. Or they can decide never to learn Go in the first place but to master an equally difficult other discipline. They continuously decide what out of all of this to do at any given moment.

AlphaGo was built by humans, for a purpose selected by humans, out of algorithms designed by humans. It is not a more advanced species. It’s not even a general intelligence.

Your own original point was much better than the one made in response.
Actually they were running on a single PC!

https://twitter.com/demishassabis/status/708489093676568576

ed: oops I misread!
Honestly, I think that is a meaningless distinction unless AlphaGo actually broke down in the middle of the match
For anyone wondering, that's Ubuntu Linux (version most likely 14.04; Unity interface).

off-topic: DeepMind should switch to a tiling window manager like i3 for increased keyboard-only productivity :)
Apparently AlphaGo made two rather stupid moves on the sidelines, judging from the commentary. Which incidentally is the kind of edgecase one would expect machine learning against itself is bad at learning, since there is a possibility that AlphaGo just tries to avoid such situations. It will be interesting to see if top players are able to exploit such weaknesses once AlphaGo is better understood by high level Go players.
Fan Hui won 2 / 5 of the informal games of their earlier match
Except we've seen a few tools come up on HN that claim to reliably tie anonymous profiles together via stylistic analysis.

Is google doing the same? Most likely.

Is it reliable? Less clear, given the Google ads page that show what Google thinks it knows about you is often mistaken on age and gender. Perhaps it's more reliable singling out an individual than an age.
Timezones are for humans, not to align scientifically with actual sun progress. Many would much rather live with the DST offset permanently.
It's regressive because of the marginal utility of money. The difference between earning 0 and 10K (getting to pay rent and groceries) is more important than the difference between earning 100K and 110K (better luxuries), yet a flat tax assigns it the same value.
The compiler may do vectorization, but not splitting up things into multiple threads.
What you are talking about here is called "label bias". [2] It is present only if training is done badly.

When you have a game of Go, or Super Mario level. You don't want to make your decisions by just checking the local features and doing them, because it can be the case that by compounding errors you end up in a state you never saw, and all of the future decisions won't be good.

One can avoid these situations by training jointly over the whole game.

For example, maximum entropy models can work for decision making problems but their training leaves them in a "label bias" state because the training is trying to minimize loss of local decisions, instead of trying to minimize the future regret of current local decision.

The solution to these label bias problems are Conditional Random Fields, or Hidden Markov Models. You could accomplish the same with Recursive Neural Networks if you trained them properly. For example, there is no search part (monte carlo tree search, or dynamic programming [viterbi] like it is in CRFs or HMMs) in RNNs but they are completely adequate for decision based problems (sequence labeling etc.). Why is that the case? Because search results are present in the data, there's no need to search if you can just learn to search from the data.

If DeepMind open-sourced the hundreds of millions of games that AlphaGo played, it is quite possible to train a model that wouldn't need a Monte Carlo search and would work quite well, because you would learn the model to make local decisions to minimize future regret, not to minimize its local loss. [1]

The only reason why reinforcement learning is used is because there are too few human games of Go available for the model to generalize well. Reinforcement learning can be used in the setting of joint learning because you play out the whole game before you do the learning. This means that you can try to learn a classifier that will minimize the regret by making a proper local decision. Although, as far as I know, and can see from the paper, they didn't train AlphaGo jointly over the game sequence.

But! Now they have a lot of data and they can repeat the process.

[1]: http://arxiv.org/abs/1502.02206

[2]: http://repository.upenn.edu/cgi/viewcontent.cgi?article=1162...