In this video from the New Yorker, chess granmaster Garry Kasparov talks through his four most memorable chess games: two against Anatoly Karpov, one against Viswanathan Anand, and the final game in his rematch against Deep Blue, in which he gets wrong-footed by a move that the computer didn’t know how to make. Even if you’re not a huge fan of chess, it’s instructive to hear Kasparov talk about the importance of what’s happening not on the board — things like body language and confidence.
kottke.org posts about games
Chess grandmaster Garry Kasparov’s most memorable games
Google’s AI beats the world’s top chess engine w/ only 4 hours of practice
With just four hours of practice playing against itself and no study of outside material, AlphaZero (an upgraded version of Alpha Go, the AI program that Google built for playing Go) beat the silicon pants off of the world’s strongest chess program yesterday. This is massively and scarily impressive.
AlphaZero won the closed-door, 100-game match with 28 wins, 72 draws, and zero losses.
Oh, and it took AlphaZero only four hours to “learn” chess. Sorry humans, you had a good run.
That’s right — the programmers of AlphaZero, housed within the DeepMind division of Google, had it use a type of “machine learning,” specifically reinforcement learning. Put more plainly, AlphaZero was not “taught” the game in the traditional sense. That means no opening book, no endgame tables, and apparently no complicated algorithms dissecting minute differences between center pawns and side pawns.
This would be akin to a robot being given access to thousands of metal bits and parts, but no knowledge of a combustion engine, then it experiments numerous times with every combination possible until it builds a Ferrari. That’s all in less time that it takes to watch the “Lord of the Rings” trilogy. The program had four hours to play itself many, many times, thereby becoming its own teacher.
Grandmaster Peter Heine Nelson likened the experience of watching AlphaZero play to aliens:
After reading the paper but especially seeing the games I thought, well, I always wondered how it would be if a superior species landed on earth and showed us how they play chess. I feel now I know.
As I said about AlphaGo last year, our machines becoming unpredictable is unnerving:
Unpredictable machines. Machines that act more like the weather than Newtonian gravity. That’s going to take some getting used to.
Albert Silver has a good overview of AlphaZero’s history and what Google has accomplished. To many chess experts, it seemed as though AlphaZero was playing more like a human than a machine:
If Karpov had been a chess engine, he might have been called AlphaZero. There is a relentless positional boa constrictor approach that is simply unheard of. Modern chess engines are focused on activity, and have special safeguards to avoid blocked positions as they have no understanding of them and often find themselves in a dead end before they realize it. AlphaZero has no such prejudices or issues, and seems to thrive on snuffing out the opponent’s play. It is singularly impressive, and what is astonishing is how it is able to also find tactics that the engines seem blind to.
So, where does Google take AlphaZero from here? In a post which includes the phrase “Skynet Goes Live”, Tyler Cowen ventures a guess:
I’ve long said that Google’s final fate will be to evolve into a hedge fund.
Why goof around with search & display advertising when directly gaming the world’s financial market could be so much more lucrative?
Scrabble pros recount their best and worst plays
The New Yorker interviewed a bunch of top Scrabble players about favorite moves they’ve played…their best, worst, and most humbling. I dislike playing Scrabble1 but love watching expert practitioners talk about about their areas of expertise.
When I’m playing and an opponent lays down “qi” or some shit, I want to take the board and throw it across the room. I love Boggle though. It’s basically pattern matching at speed, something my brain seems to be particularly good at.↩
Google’s impractical voice experiments
Google has launched a series of voice experiments that work with Google Home and also in the browser. For example, Mystery Animal is a 20 questions style game in which you attempt to guess the identity of a particular animal. Here’s how it works:
Another of the experiments, MixLab, helps you make music with simple voice commands (“add a club beat”, etc.). The experiments use AI to understand what people are asking them.
Nicole He, who worked on Mystery Animal and another experiment called Story Speaker, explains why it’s an interesting time to be goofing around with voice technology.
Talking out loud to computers has always felt more science fiction than real life. But speech recognition technology has come a long way, and developers are now making lots of useful things with voice devices. These days, you can speak out loud and have your lights turn on, or your favorite music played, or the news read to you.
That’s all nice and good, but there’s something clearly missing: the weird stuff. We should make things for voice technology that aren’t just practical. We should make things that are way more creative and bizarre. Things that are more provocative and expressive, or whimsical and delightful.
We’re in what I’m going to call The 1996 Web Design Era of voice technology. The web was created for something practical (sharing information between scientists), but it didn’t take very long for people to come up with strange and creative things to do with it.
I am terrible at 20 questions, so of course Mystery Animal stumped me. My last guess was “are you a zebra?” when the animal was actually a panda bear.
A new AI beats top human poker players
Poker is famously hard for machines to model because you have limited information, you have to iterate your strategies over time, and react to shifts in your interactions with multiple other agents. In short, poker’s too real. Sounds like fun! A couple of researchers at Carnegie Mellon found a way to win big:
Carnegie Mellon professor Tuomas Sandholm and grad student Noam Brown designed the AI, which they call Libratus, Latin for “balance.” Almost two years ago, the pair challenged some top human players with a similar AI and lost. But this time, they won handily: Across 20 days of play, Libratus topped its four human competitors by more than $1.7 million, and all four humans finished with a negative number of chips…
According to the human players that lost out to the machine, Libratus is aptly named. It does a little bit of everything well: knowing when to bluff and when to bet low with very good cards, as well as when to change its bets just to thrown off the competition. “It splits its bets into three, four, five different sizes,” says Daniel McAulay, 26, one of the players bested by the machine. “No human has the ability to do that.”
This makes me suspect that, as Garry Kasparov discovered with chess, and Clive Thompson’s documented in many other fields, a human player working with an AI like Libratus would perform even better than the best machines or best players on their own.
Update: Sam Pratt points out that while Libratus played against four human players simultaneously, each match was one-on-one. Libratus “was only created to play Heads-Up, No-Limit Texas Hold’em poker.” So managing that particular multidimensional aspect of the game (playing against players who are also playing against each other, with infinite possible bets) hasn’t been solved by the machines just yet.
The greatest chess game ever played
Garry Kasparov, who is one of the top chess players ever, said that his 1999 match against Veselin Topalov was the greatest game of chess he ever played. In this video, MatoJelic goes through the game, move by move. Even if you only have a passing interest in chess, I’d recommend watching…it gets really interesting after the first 10-12 moves (which are presented without explanation) and listening to someone who is passionate about a topic is often worth it.
Also entertaining and informative was his explanation of The Game of the Century, which pitted a 13-year-old Bobby Fischer against Donald Byrne, a top-ranked American player. (via farnam street)
Why chess has failed to catch on as a spectator sport
Magnus Carlsen and Sergey Karjakin are competing in the FIDE World Chess Championship Match in NYC and are currently tied going into the final match. By all accounts, it’s been a tense competition. But watching chess being played in real time is perhaps only for die-hard fans. Here’s video of Karjakin thinking about a move for 25 minutes:
And here’s Carlsen thinking about Karjakin thinking about the same move for 25 minutes:
(via @juririm)
Elliptical pool table
In a perfect world, if you place a cue ball at the focal point of an elliptical pool table, you can hit it in any direction you want and it will go into a pocket located at the other focal point. Geometry! Of course, in the real world, you need to worry about things like hitting it too hard, variations in the table, spin on the ball, etc., but it still works pretty well.
How would you play an actual game on an elliptical table though? Like this. (Hint: to sink the intended ball on the table, hit it as though it came from the opposite focal point.)
Really Bad Chess
Really Bad Chess is an iOS game by Zach Gage that randomizes the distribution of pieces when the board is set up, so that you might start a game with 4 queens, 3 knights, and only 2 pawns in the back row. The result is that you get a completely new strategic game each time, but you still play with the familiar tactical rules of chess. What a great idea…I can’t tell if people who really love chess will love or hate this.
Update: See also Knightmare Chess:
Knightmare Chess is played with cards that change the default rules of chess. The cards might change how a piece moves, move opponent’s pieces, create special squares on the board or otherwise alter the game.
and Chess960 invented by Bobby Fischer:
It employs the same board and pieces as standard chess; however, the starting position of the pieces on the players’ home ranks is randomized. The random setup renders the prospect of obtaining an advantage through the memorization of opening lines impracticable, compelling players to rely on their talent and creativity.
The four types of board games
In The Oxford History of Board Games published in 1999, scholar David Parlett wrote that there are four types of classical board game: race, chase, space, and displace. The book is out of print (but is available direct from the author as a PDF), so I found this description of Parlett’s categorization in a book by Stewart Woods called Eurogames.
In categorizing these public domain or “folk” games, Parlett (1999) draws on the work of H.J.R. Murray (1952) and R.C. Bell (1979) in describing four types of game, as identified by the game goals: race games, in which players traverse a track in an attempt to be the first to finish (e.g. Nyout, Pachisi); space games, in which players manipulate the position of pieces to achieve prescribed alignments, make connections, or traverse the board (e.g. Noughts and Crosses, Twixt, and Halma, respectively); chase games, in which asymmetrical starting positions and goals cast players in the role of pursuer and pursued (e.g. Hnefatafl, Fox & Geese); and games of displacement, where symmetrically equipped players attempt to capture and eliminate each other’s pieces (e.g. Chess, Draughts).
You’re probably unfamiliar with some of these games (as I was). For race games, Parcheesi is a modern version of pachisi…other examples would be Sorry, Candyland, or Snakes and Ladders. Noughts and crosses is tic-tac-toe; other space games include Go and Connect 4. A modern example of a chase game might be Clue. And as written above, chess and draughts (checkers) are classic displace games. (via @genmon)
Trump paid the stereotype tax in the first debate
Jeet Heer writing for The New Republic: Clinton Proved Trump Is a Man You Can Bait in a Debate.
In her acceptance speech at the Democratic convention, Hillary Clinton called out Donald Trump memorably, saying, “A man you can bait with a tweet is not a man we can trust with nuclear weapons.” The insight that Trump is easy to provoke formed the core of Clinton’s successful strategy in the first debate on Monday, as she repeatedly incited the Republican nominee to both adopt an off-putting aggressive tone and to make a series of damaging self-admissions.
This reminded me of poker player Annie Duke’s explanation of how she used the gender stereotypes of her male opponents against them.
I figured it was part of the game that if somebody was at the table who was so emotionally invested in the fact that I was a woman, that they could treat me that way, that probably, that person wasn’t going to make good decisions at the table against me. So I really tried to sort of separate that out and think about it from a strategic place of, how can I come up with the best strategy to take their money because I guess, in the end, isn’t that the best revenge?
Trump sounds like he’s a combination of the angry and disrespecting chauvinists:
VEDANTAM: She says she divided the men who had stereotypes about her into three categories.
DUKE: One was the flirting chauvinists, and that person was really viewing me in a way that was sexual.
VEDANTAM: With the guys who were like that, Annie could make nice.
DUKE: I never did go out on a date with any of them, but you know, it was kind of flirtatious at the table. And I could use that to my advantage.
VEDANTAM: And then there was the disrespecting chauvinist. Annie says these players thought women weren’t creative.
DUKE: There are strategies that you can use against them. Mainly, you can bluff those people a lot.
VEDANTAM: And then there’s a third kind of guy, perhaps the most reckless.
DUKE: The angry chauvinist.
VEDANTAM: This is a guy who would do anything to avoid being beaten by a woman. Annie says you can’t bluff an angry chauvinist. You just have to wait.
DUKE: What I say is, until they would impale themselves on your chips.
Although I suspect his chauvinism is only part of his poor debate showing…his insecurity is off the charts as well.
Limits are fun? Limits are fun!
Play Anything is a forthcoming book by game designer and philosopher Ian Bogost. The subtitle — The Pleasure of Limits, the Uses of Boredom, and the Secret of Games — provides a clue as to what it’s about. Here’s more from the book’s description:
Play is what happens when we accept these limitations, narrow our focus, and, consequently, have fun. Which is also how to live a good life. Manipulating a soccer ball into a goal is no different than treating ordinary circumstances — like grocery shopping, lawn mowing, and making PowerPoints — as sources for meaning and joy. We can “play anything” by filling our days with attention and discipline, devotion and love for the world as it really is, beyond our desires and fears.
Reading this little blurb, I immediately thought of two things:
1. One thing you hear from pediatricians and early childhood educators is: set limits. Children thrive on boundaries. There’s a certain sort of person for whom this appeals to their authoritarian nature, which is not the intended message. Then there are those who can’t abide by the thought of limiting their children in any way. But perhaps, per Bogost, the boundaries parents set for their children can be thought of as a series of games designed to keep their lives interesting and meaningful.1
2. This recent post about turning anxiety into excitement. Shifting from finding life’s limitations annoying to thinking of them as playable moments seems similar. Problems become opportunities, etc.
3. Ok, three things. I once wrote a post about bagging groceries and mowing the lawn as games.
Two chores I find extremely satisfying are bagging groceries and (especially) mowing the lawn. Getting all those different types of products — with their various shapes, sizes, weights, levels of fragility, temperatures — quickly into the least possible number of bags…quite pleasurable. Reminds me a little of Tetris. And mowing the lawn…making all the grass the same height, surrounding the remaining uncut lawn with concentric rectangles of freshly mowed grass.
What I’m saying is, I’m looking forward to reading this book. See also Steven Johnson’s forthcoming book, Wonderland: How Play Made the Modern World.
I don’t know about other parents, but 75% of my parental energy is taken up by thinking about what limits are appropriate for my kids. (The other 25% is meal-planning.) What do they need right now? What do they want? What can I give them? How do I balance all of those concerns? What makes it particularly difficult for me sometimes is that my instincts and my intellect are not always in agreement with what is appropriate. What is easiest for me is not always best for them. This shit keeps me up at night. :| ↩
In a game of big words, brevity triumphs
If you love reading about unusual Moneyball-esque strategies in sports, check out how a player on the Nigerian team won the Scrabble World Championship last year.
So while Scrabblers still fancy bingos, they increasingly hold off on other high-scoring moves, such as six-letter words, or seven-letter terms that only use six tiles from the rack. Instead, by spelling four- or five-letter words, a player can keep their most useful tiles — like E-D or I-N-G — for the next round, a strategy called rack management. The Nigerians rehearse it during dayslong scrimmage sessions.
Also, thanks to a design quirk, the board is oddly generous to short words. Most of the bonus squares are just four or five letters apart.
“The geometry of the Scrabble board rewards five-letter words,” said Mr. Mackay, who lost to Mr. Jighere in the world championship final, during which the Nigerian nabbed a triple word score with the antiquated adjective KATTI, meaning “spiteful.” “It’s a smart tactic.”
Documentary on chess champion Magnus Carlsen
Using behind-the-scenes footage shot over the past decade, Magnus is a feature-length documentary about reigning world chess champion Magnus Carlsen.
From a young age Magnus Carlsen had aspirations of becoming a champion chess player. While many players seek out an intensely rigid environment to hone their skills, Magnus’ brilliance shines brightest when surrounded by his loving and supportive family. Through an extensive amount of archival footage and home movies, director Benjamin Ree reveals this young man’s unusual and rapid trajectory to the pinnacle of the chess world. This film allows the audience to not only peek inside this isolated community but also witness the maturation of a modern genius.
Our creative, beautiful, unpredictable machines
I have been following with fascination the match between Google’s Go-playing AI AlphaGo and top-tier player Lee Sedol and with even more fascination the human reaction to AlphaGo’s success. Many humans seem unnerved not only by AlphaGo’s early lead in the best-of-five match but especially by how the machine is playing in those games.
Then, with its 19th move, AlphaGo made an even more surprising and forceful play, dropping a black piece into some empty space on the right-hand side of the board. Lee Sedol seemed just as surprised as anyone else. He promptly left the match table, taking an (allowed) break as his game clock continued to run. “It’s a creative move,” Redmond said of AlphaGo’s sudden change in tack. “It’s something that I don’t think I’ve seen in a top player’s game.”
When Lee Sedol returned to the match table, he took an usually long time to respond, his game clock running down to an hour and 19 minutes, a full twenty minutes less than the time left on AlphaGo’s clock. “He’s having trouble dealing with a move he has never seen before,” Redmond said. But he also suspected that the Korean grandmaster was feeling a certain “pleasure” after the machine’s big move. “It’s something new and unique he has to think about,” Redmond explained. “This is a reason people become pros.”
“A creative move.” Let’s think about that…a machine that is thinking creatively. Whaaaaaa… In fact, AlphaGo’s first strong human opponent, Fan Hui, has credited the machine for making him a better player, a more beautiful player:
As he played match after match with AlphaGo over the past five months, he watched the machine improve. But he also watched himself improve. The experience has, quite literally, changed the way he views the game. When he first played the Google machine, he was ranked 633rd in the world. Now, he is up into the 300s. In the months since October, AlphaGo has taught him, a human, to be a better player. He sees things he didn’t see before. And that makes him happy. “So beautiful,” he says. “So beautiful.”
Creative. Beautiful. Machine? What is going on here? Not even the creators of the machine know:
“Although we have programmed this machine to play, we have no idea what moves it will come up with,” Graepel said. “Its moves are an emergent phenomenon from the training. We just create the data sets and the training algorithms. But the moves it then comes up with are out of our hands — and much better than we, as Go players, could come up with.”
Generally speaking,1 until recently machines were predictable and more or less easily understood. That’s central to the definition of a machine, you might say. You build them to do X, Y, & Z and that’s what they do. A car built to do 0-60 in 4.2 seconds isn’t suddenly going to do it in 3.6 seconds under the same conditions.
Now machines are starting to be built to think for themselves, creatively and unpredictably. Some emergent, non-linear shit is going on. And humans are having a hard time figuring out not only what the machine is up to but how it’s even thinking about it, which strikes me as a relatively new development in our relationship. It is not all that hard to imagine, in time, an even smarter AlphaGo that can do more things — paint a picture, write a poem, prove a difficult mathematical conjecture, negotiate peace — and do those things creatively and better than people.
Unpredictable machines. Machines that act more like the weather than Newtonian gravity. That’s going to take some getting used to. For one thing, we might have to stop shoving them around with hockey sticks. (thx, twitter folks)
Update: AlphaGo beat Lee in the third game of the match, in perhaps the most dominant fashion yet. The human disquiet persists…this time, it’s David Ormerod:
Move after move was exchanged and it became apparent that Lee wasn’t gaining enough profit from his attack.
By move 32, it was unclear who was attacking whom, and by 48 Lee was desperately fending off White’s powerful counter-attack.
I can only speak for myself here, but as I watched the game unfold and the realization of what was happening dawned on me, I felt physically unwell.
Generally I avoid this sort of personal commentary, but this game was just so disquieting. I say this as someone who is quite interested in AI and who has been looking forward to the match since it was announced.
One of the game’s greatest virtuosos of the middle game had just been upstaged in black and white clarity.
AlphaGo’s strength was simply remarkable and it was hard not to feel Lee’s pain.
Let’s get the caveats out of the way here. Machines and their outputs aren’t completely deterministic. Also, with AlphaGo, we are talking about a machine with a very limited capacity. It just plays one game. It can’t make a better omelette than Jacques Pepin or flow like Nicki. But not only beating a top human player while showing creativity in a game like Go, which was considered to be uncrackable not that long ago, seems rather remarkable.↩
Plagiarism of crossword puzzles
Not to be outdone by the recent New Yorker piece on cheating in bridge, FiveThirtyEight has written about allegations of plagiarism by a prominent crossword puzzle editor. Much like in the pro bridge case, an online repository of games has led to people uncovering inconsistencies in dozens of Timothy Parker’s crossword puzzles that would not have been otherwise noticed.
There are two types of Parker’s puzzle duplications that the database has laid bare: what I’m calling the “shady” and the “shoddy.” The shady are puzzles that appeared in Universal or USA Today with themes and theme answers identical to puzzles published earlier and in separate, unrelated publications, most often The New York Times and occasionally the Los Angeles Times and Chicago Tribune. In every such case I saw - roughly 100 cases - the theme answers were in identical locations within the grid, and in many cases, the later puzzle also replicated the earlier puzzle’s grid and some of its clues.
(via waxy)
Professional bridge has a cheating problem
Players in the top ranks of the world’s professional bridge organizations have been caught cheating and the evidence is on YouTube.
On deals in which Fisher and Schwartz ended up as declarer and dummy, they cleared away the tray and the board in the usual manner. But when they were defending-meaning that one of them would make the opening lead-they were wildly inconsistent. Sometimes Fisher would remove the tray, and sometimes Schwartz would, and sometimes they would leave it on the table. Furthermore, they placed the duplicate board in a number of different positions — each of which, it turns out, conveyed a particular meaning. “If Lotan wanted a spade lead, he put the board in the middle and pushed it all the way to the other side,” Weinstein said. If he wanted a heart, he put it to the right. Diamond, over here. Club, here. No preference, here.”
Here’s a video showing what Fisher and Schwartz were doing:
Once you see it, it’s obvious they’re cheating.
What an odd seeming game when played at the professional level, BTW. Players seated so they can’t see their teammates. Information is passed through bidding, but only through signals that everyone is aware of. And some available information you can use and some you can’t:
Expert poker players often take advantage of a skill they call table feel: an ability to read the facial expressions and other unconscious “tells” exhibited by their opponents. Bridge players rely on table feel, too, but in bridge not all tells can be exploited legally by all players. If one of my opponents hesitates during the bidding or the play, I’m allowed to draw conclusions from the hesitation — but if my partner hesitates I’m not. What’s more, if I seem to have taken advantage of information that I wasn’t authorized to know, my opponents can summon the tournament director and seek an adjusted result for the hand we just played. Principled players do their best to ignore their partner and play at a consistent tempo, in order to avoid exchanging unauthorized information — and, if they do end up noticing something they shouldn’t have noticed, they go out of their way not to exploit it.
As the story goes on to say, there are technological fixes that would curtail the cheating, but would get rid of the actual cards in a card game. Why not get rid of the humans as well and just run games as computer simulations? Again, odd game. (via @pomeranian99)
How to Win at Monopoly and Piss Off Your Friends
If you’re forced into playing Monopoly by friends, you can employ this simple strategy to ensure they will never ever ask you to play again.
With a second monopoly completed, your next task is to improve those properties to three houses each, then all of your properties to four houses each. Six properties with three houses will give you more than half of the houses in the game, and four houses each will give you 75% of the total supply. This will make it nearly impossible for your opponents to improve their own property in a meaningful way. Keep the rulebook nearby once the supply gets low, as you will undoubtedly be questioned on it. At this point, you will be asked repeatedly to build some friggin’ hotels already so that other people can build houses. Don’t.
At this point, you more or less have the game sewn up. If losing a normal game of monopoly is frustrating, losing to this strategy is excruciating, as a losing opponent essentially has no path to victory, even with lucky rolls. Your goal is to play conservatively, lock up more resources, and let the other players lose by attrition. If you want to see these people again, I recommend not gloating, but simply state that you’re playing to win, and that it wasn’t your idea to play Monopoly in the first place.
It is difficult to read this without thinking about income inequality in the real world.
Quantum Chess: Stephen Hawking vs Paul Rudd
So, this is a time travel movie with Keanu Reeves (narrator) and Alex Winter (director), but it’s not Bill & Ted’s Excellent Adventure, Part 3? No, of course not. It’s actually a video about quantum chess featuring Paul Rudd, Stephen Hawking, and music from The Matrix. Like, WHAT?! If The Chickening hadn’t dropped earlier, this would be the oddest thing you’ll watch this week. (And it’s not quite clear, but the video appears to be an advertisement for a quantum chess game that’s launching on Kickstarter next week. Nothing about this makes any sense…) (via @gavinpurcell)
Paying the stereotype tax
I love this piece from NPR about how poker player Annie Duke uses her male opponents’ stereotypical views of women against them.
I figured it was part of the game that if somebody was at the table who was so emotionally invested in the fact that I was a woman, that they could treat me that way, that probably, that person wasn’t going to make good decisions at the table against me. So I really tried to sort of separate that out and think about it from a strategic place of, how can I come up with the best strategy to take their money because I guess, in the end, isn’t that the best revenge?
She noticed there were three types of chauvinist players and approached each with a different strategy.
VEDANTAM: She says she divided the men who had stereotypes about her into three categories.
DUKE: One was the flirting chauvinists, and that person was really viewing me in a way that was sexual.
VEDANTAM: With the guys who were like that, Annie could make nice.
DUKE: I never did go out on a date with any of them, but you know, it was kind of flirtatious at the table. And I could use that to my advantage.
VEDANTAM: And then there was the disrespecting chauvinist. Annie says these players thought women weren’t creative.
DUKE: There are strategies that you can use against them. Mainly, you can bluff those people a lot.
VEDANTAM: And then there’s a third kind of guy, perhaps the most reckless.
DUKE: The angry chauvinist.
VEDANTAM: This is a guy who would do anything to avoid being beaten by a woman. Annie says you can’t bluff an angry chauvinist. You just have to wait.
DUKE: What I say is, until they would impale themselves on your chips.
I got this link from Andy Baio, who also linked to the video of the specific match referenced in the NPR piece and noted “Phil Hellmuth attributes all of Annie’s wins to luck, all of his own to skill”.
Real-life first person shooter on Chatroulette
These folks created a real-life first person shooter game and invited strangers on Chatroulette to control the action.
Here’s a behind-the-scenes look at how they did it. (thx, oren)
Human/computer partnerships are potent
Tim Wu writes for the New Yorker about how Netflix uses a ~70/30 combination of data and human judgment to determine their recommendations and what shows/movies to make.
Over the years, however, I’ve started to wonder whether Netflix’s big decisions are truly as data driven as they are purported to be. The company does have more audience data than nearly anyone else (with the possible exception of YouTube), so it has a reason to emphasize its comparative advantage. But, when I was reporting a story, a couple of years ago, about Netflix’s embrace of fandom over mass culture, I began to sense that their biggest bets always seemed ultimately driven by faith in a particular cult creator, like David Fincher (“House of Cards”), Jenji Leslie Kohan (“Orange is the New Black”), Ricky Gervais (“Derek”), John Fusco (“Marco Polo”), or Mitchell Hurwitz (“Arrested Development”). And, while Netflix does not release its viewership numbers, some of the company’s programming, like “Marco Polo,” hasn’t seemed to generate the same audience excitement as, say, “House of Cards.” In short, I do think that there is a sophisticated algorithm at work here — but I think his name is Ted Sarandos.
I presented Sarandos with this theory at a Sundance panel called “How I Learned to Stop Worrying and Trust the Algorithm,” moderated by Jason Hirschhorn, formerly of MySpace. Sarandos, very agreeably, wobbled a bit. “It is important to know which data to ignore,” he conceded, before saying, at the end, “In practice, its probably a seventy-thirty mix.” But which is the seventy and which is the thirty? “Seventy is the data, and thirty is judgment,” he told me later. Then he paused, and said, “But the thirty needs to be on top, if that makes sense.”
This reminds me of the situation in chess, where cyborg human/computer teams can beat computer- or human-only players in chess, although perhaps for not much longer.
Some of you will know that Average is Over contains an extensive discussion of “freestyle chess,” where humans can use any and all tools available — most of all computers and computer programs — to play the best chess game possible. The book also notes that “man plus computer” is a stronger player than “computer alone,” at least provided the human knows what he is doing. You will find a similar claim from Brynjolfsson and McAfee.
Computer chess expert Kenneth W. Regan has compiled extensive data on this question, and you will see that a striking percentage of the best or most accurate chess games of all time have been played by man-machine pairs. Ken’s explanations are a bit dense for those who don’t already know chess, computer chess, Freestyle and its lingo, but yes that is what he finds, click on the links in his link for confirmation. In this list for instance the Freestyle teams do very very well.
I wonder what the human/cyborg split is at Buzzfeed or Facebook? Or at food companies like McDonald’s or Kraft? Or at Goldman Sachs?
Grandmaster Fabiano Caruana is on a roll
Unless you’re a close follower of chess, you’re probably missing out on one of the most impressive feats the game has ever seen. Fabiano Caruana, an Italian born in the US and currently ranked #3 in the world, has won seven straight games in the “strongest ever chess tournament”, the Sinquefield Cup in St. Louis, MO. No losses, no draws, just 7 straight wins.
In terms of comparison, Magnus Carlsen, the world’s current #1 and owner of the highest ranking ever, is 2-1-4 at the same tournament. Which is pretty typical; the best players draw a lot. Over his career, Carlsen has drawn almost 50% of the time and Caruana about 40%.
The modern times of chess have a new king, king Fabiano Caruana. One has to look back to 1968 where in Wijk Aan Zee the legendary Korchnoi started with 8,0/8. The times now are so different and the competition so fierce that already Fabiano’s success can be proclaimed as the most memorable streak in the history of chess.
Along the way, Caruana has beaten Carlsen (#1), Levon Aronian (#2), Maxime Vachier-Lagrave (#9) twice, Hikaru Nakamura (#7), and Veselin Topalov (#6) twice. If you look at the unofficial live chess ratings, you’ll see he has moved into the #2 position in the world, jumping a whopping 34.1 points in rating. He also owns the fourth highest rating in history, behind Carlsen, Kasparov, and Aronian. Caruana plays Carlsen again today, starting from the more advantageous white position. (via @tylercowen)
Update: In his eighth match, Caruana drew against Carlsen but clinched first place overall with two matches remaining.
Update: Seth Stevenson has an article in Slate about what went down at the Sinquefield Cup, saying “one of the most amazing feats in chess history just happened, and no one noticed”.
The secret rites of Minecraft
“Minecraft is a game about creation,” writes Robin Sloan. “But it is just as much a game about secret knowledge.”
There’s no official manual, so the game’s teeming network of devotees, young and old, proper publishers and web-based wildcats, have worked to create them by the score. Not just guides, but wikis, videos, hints, tricks. The rules can only be discovered by observation, reasoning, and experiment. Like science — or magic:
Imagine yourself a child. Imagine yourself given one of these books: not merely a story of exploration and adventure, but a manual to such.
Imagine yourself acquiring the keys to a mutable world in which you can explore caves, fight spiders, build castles, ride pigs, blow up mountains, construct aqueducts to carry water to your summer palace… anything.
Imagine yourself a child, in possession of the secret knowledge.
Maybe the most interesting thing about this, Robin writes, is how the game “calls forth” the books — another kind of magic. Is this a function of how much Minecraft players love the game? Or is that arcane, indirect, networked, bottomless well of knowledge, asking to be impossibly filled, what they love about it?
Changing chess openings
The moves that expert chess players use to open a game have changed significantly since the 1850s.
It’s a well-known fact that White has a small advantage at the beginning of the game. To maintain this advantage, White should press their advantage to take over the middle of the board as quickly as possible. The most popular first White moves from 1850-2014 are shown below. Note that all of these are fairly aggressive openings that build toward control of the middle of the board.
In 1850, White openings were fairly homogeneous: Most chess experts played King’s Pawn. Chess players didn’t begin to explore variants of the King’s Pawn in earnest until the 1890s, when Queen’s Pawn (moving a Pawn to d4) started to replace King’s Pawn in some player’s repertoires. The 1920s saw another burst of innovation with the rising popularity of the Zukertort Opening (moving the Knight to f3) and the English Opening (moving a Pawn to c4), which completed the set of staple first-turn openings that are really ever used nowadays.
Reimagining Monopoly
Mike Merrill reimagines the game of Monopoly to better represent the modern financial system by adding the banker as a player, convertible notes, and Series A financing.
Each player starts with only $500. That’s a nice bit of cash, but it’s going to be expensive to build your capitalist empire. Baltic Avenue will cost you $80, States Avenue is $140, Atlantic is $260, and that leaves you just $20. Even if you’re the first to land on Boardwalk you won’t be able to afford the $400 price tag. Another $200 from “passing Go” is not going to last that long. You need more money.
At the start of the game the banker will offer each player a convertible note of $1000 at a 20% discount and 5% interest*. Armed with $1500 the player is now ready to set out on their titan of the universe adventure! (Of course players are not required to take the convertible note.)
That sounds fun? (via waxy)
Game of Phones
Ooh, I really like the idea of this smartphone card game on Kickstarter: Game of Phones.
One player picks a card and gets to judge that round. They read the prompt to everyone else. Something like ‘Find the best #selfie’ or ‘Show the last photo you took’. Everyone finds something on their phones and shows the judge, who gets to choose a winner for that round. First to win 10 rounds is the overall winner.
This is pretty much what people do when they get together anyway, why not make it a game?
Grit, chess, and how to think
Shane Parrish’s excerpt and exploration of Paul Tough’s How Children Succeed: Grit, Curiosity, and the Hidden Power of Character made me want to stop everything and read the book all the way through.
“Tell me about your game,” Spiegel said. Sebastian flopped into the chair and handed her his notepad, where he’d recorded all the moves for both players in the game.
Sebastian explained that the other guy was simply better. “He had good skills,” he said. “Good strategies.”
And this is the point where many of us would simply say something along the lines of “did you do your best?,” in which case the likely response is “Yes.” Everyone is at least let off the hook. The teacher for ensuring students try their best, the student for having lost to someone better. Spiegel did not take this approach.
You may remember Tough’s 2011 piece on grit in the NY Times Magazine.
The most critical missing piece, Randolph explained as we sat in his office last fall, is character — those essential traits of mind and habit that were drilled into him at boarding school in England and that also have deep roots in American history. “Whether it’s the pioneer in the Conestoga wagon or someone coming here in the 1920s from southern Italy, there was this idea in America that if you worked hard and you showed real grit, that you could be successful,” he said. “Strangely, we’ve now forgotten that. People who have an easy time of things, who get 800s on their SAT’s, I worry that those people get feedback that everything they’re doing is great. And I think as a result, we are actually setting them up for long-term failure. When that person suddenly has to face up to a difficult moment, then I think they’re screwed, to be honest. I don’t think they’ve grown the capacities to be able to handle that.”
Magnus Carlsen chess app
The best chess player in history, 23-year-old Norwegian Magnus Carlsen, has released an iOS app where you can play simulated games against Carlsen at various stages of his career, from age 5 up to the present. The Telegraph has the details.
Anyone who wants to find out more about his playing style can do so with Mr Carlsen’s new app, which allows users to play him at the different levels he has achieved since the age of five.
The app is built on hundreds of thousands of different positions from Mr Carlsen’s games, be they classical, rapid or blitz, to determine what moves he would make at those ages.
The aim is to promote chess among as many people as possible to make the sport more popular and accessible.
“The good thing is that you can play me at any age. At age five, anyone has a chance to beat me,” Mr Carlsen said.
So what is it like for Mr Carlsen to play against his younger self?
“He is really tricky,” the champion said. “Even Magnus at 11 years old was a very gifted tactician. A while ago I played as a test Magnus [aged] 14. I outplayed him at some point positionally. And just boom, boom, he tricked me tactically.
“But he makes mistakes as well, so I just have to be patient.”
(via mr)
Regular expression crossword puzzle
No one can solve this. Not Ken Jennings. Not Marilyn vos Savant. Not Alan Turing. Not Ada Lovelace. Not Watson. Not even Richard Feynman. (Ok, maybe Feynman.)
(via @lhl)
Update: Here’s the answer to the puzzle, presumably by some time traveling super-being from the future. (via @grimmelm)
