Chess is as physically demanding as many other sports due to stress and because the human brain uses a ton of energy. Many of today’s top chess players train and eat like pro tennis or soccer players.
In 2004, winner Rustam Kasimdzhanov walked away from the six-game world championship having lost 17 pounds. In October 2018, Polar, a U.S.-based company that tracks heart rates, monitored chess players during a tournament and found that 21-year-old Russian grandmaster Mikhail Antipov had burned 560 calories in two hours of sitting and playing chess — or roughly what Roger Federer would burn in an hour of singles tennis.
Robert Sapolsky, who studies stress in primates at Stanford University, says a chess player can burn up to 6,000 calories a day while playing in a tournament, three times what an average person consumes in a day. Based on breathing rates (which triple during competition), blood pressure (which elevates) and muscle contractions before, during and after major tournaments, Sapolsky suggests that grandmasters’ stress responses to chess are on par with what elite athletes experience.
“Grandmasters sustain elevated blood pressure for hours in the range found in competitive marathon runners,” Sapolsky says.
From experimental game developer Pippin Barr, several variations on the game of chess that makes the game more interesting (or at least weirder). In “Clone” mode, every time you move a piece, a copy of that piece is made. In “Chance” mode, selecting a piece causes the piece to change randomly to another type of piece (e.g. from a pawn to a rook) that you can then move. In “Gravity” mode, pieces fall to the bottom of the board unless they’re blocked by other pieces. In “Quantum” mode, a new piece is spawned in each possible new position of a selected piece.
From directors Molly Brass and Stephen Tyler, this is a really lovely & poignant short film about Chess Forum and its owner, Imad Khachan, a Palestinian refugee who came to America to get a PhD in American literature and ended up as the owner/operator of a classic NYC establishment.
Anybody who doesn’t speak any language or different languages, they can sit here and play chess. You can still hold a meaningful conversation without saying a word.
After an ownership agreement between Khachan and a former business mentor fell to pieces, Khachan opened the Chess Forum directly across the street from his former partner’s shop, The Chess Shop. His move triggered what, in New York chess circles, is still known as the Civil War on Thompson Street.
“Sometimes attack is the best defense,” Khachan said of his decision.
His move tore New York’s tight-knit chess community in two. A ceasefire eventually settled in, with each shop courting its own customers and suppliers. His business rival closed in 2012, but the feud taught Khachan a lesson as strong as any he learned on the board.
“Like any chess game it’s the thinking ahead that keeps you one step ahead of the guy who’s shooting after you and not hitting you,” he said. “You have to keep moving.”
Our analysis provides new evidence that even simple turn-based games contain a great deal of interaction richness and subtlety, and that the different levels of communication should be considered by designers as a real and legitimate vehicle for social interaction.
The New Yorker asked three professional poker players to give blow-by-blow accounts of their most memorable hands. Aside from the rules, I know almost nothing about poker and have played Texas Hold’em for about one total hour in my life — guess how I did! — so I actually learned a lot from this video. And the third player’s explanation of his hand and his thought process was fascinating.
Maria Konnikova is a writer for the New Yorker. Or she was until she went on sabbatical to play poker professionally. After immersing herself in the game while working on her third book, The Biggest Bluff, Konnikova discovered she was quite good at it, winning over $230,000 and a major tournament in a year.
When you see someone looking a certain way, you assume they play a certain way. So once I figure out how they view women, I can figure out how to play against them. They’re not seeing me as a poker player, they’re seeing me as a female poker player.
There are people who’d rather die than be bluffed by a woman. They’ll never fold to me because that’s an affront to their masculinity.
I never bluff them. I know that no matter how strong my hand, they are still going to call me because they just can’t fold to a girl.
Other people think women are incapable of bluffing. They think if I’m betting really aggressively, it means I have an incredibly strong hand. I bluff those people all the time.
There are people who think that women shouldn’t be at a poker table, and they try to bully me. So, what do I do? I let them. And I wait to be in a good position so that I can take their chips. Just like life, right?
In a 2015 NPR interview, pro player Annie Duke talked about getting her opponents to pay the stereotype tax.
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.
Update: In an episode of The Pay Check podcast, Duke and Konnikova “discuss power dynamics and sexism in the ultra male dominated field” of poker.
Hasbro has come out with an official “Cheaters Edition” of Monopoly (available at Amazon) where popular game cheats like stealing money from the bank, busting out of jail early, and taking a hotel from another player have been added to the gameplay. Fast Company has more on how the game came to be.
“We’ve had this data for years. 50% of all Monopoly players cheat,” says Randy Klimpert, Hasbro’s senior director of design and games development. This fact of life was always something of a running joke within the walls of Hasbro. It became the giggly fodder of proposed ad campaigns. Employees got a kick out of listening to the messages left on its holiday helpline, established in 2016, to help families settle disputes in their games and address accusations of creative cheating. “We were literally sitting around thinking, ‘what would really corrupt Monopoly?’ And someone said, ‘what if we cheated?’”
“Our senior marketer… you could see him mulling it,” Klimpert continues. “Monopoly… cheaters… Cheater Edition!” Hasbro instantly had the hook for a new game. But how do you make a game for cheaters that’s still sensical and fun?
Quick reviews of some things I’ve read, seen, heard, and experienced in the past month or so. I was out of town for a few days so there are more books on here than usual. I’m trying to keep it up…reading right now but too early to call: Broad Band, Am I There Yet?, Black Panther: A Nation Under Our Feet. Oh and I’m really glad The Americans is back on, even though it’s the final season. (As I’ve said before, don’t pay too much attention to the letter grades. They are subjective and frequently wrong.)
Star Trek Voyager. Not in the same league as Next Generation, but it hums along nicely after they get going. (B)
Mr. Robot. I watched the first episode of season three and then got distracted by other things. Anybody watch the whole season? Is it worth circling back? (TBD)
Annihilation. I enjoyed this more than many people I know, but not as much as Matt Zoller Seitz. Eager to watch it again since reading the book (see below). (B+)
Lincoln. I love this movie. One of Spielberg’s best. (A)
Ugly Delicious. I wanted to hate this, but it’s really interesting and David Chang wears you down with his, well, I wouldn’t call it charm exactly. The episode that really hooked me was the Thanksgiving one, when he’s wandering around a massive supermarket with his mom, who’s mockingly calling him “David Chang” (you can almost hear the appended ™ in her voice) and then refers to him as the “Baby King”. Also, for a chef, Chang is weirdly incurious about food but harangues people for not appreciating kimchi. I really should write a longer post about this… (A-)
Murder on the Orient Express. Better than I had heard, if you choose to embrace its slight campiness. I really enjoyed Branagh’s Poirot. (B+)
Geostorm. I love disaster movies like this, but I kept checking my phone during this one and a day or two later I couldn’t have told you a single plot point. That will not stop me from watching it again because (see first sentence). (C)
Sunsets. I recommend them, particularly on the beach. (A)
Annihilation by Jeff VanderMeer. This is likely an unpopular opinion, but I liked the movie more. Upon finishing, I was not inclined to read the sequels. (B)
An Incomplete History of Protest. Inspiring collection of objects related to the protests of everything from the AIDS crisis to Vietnam. Fascinating to see how the disenfranchised leveraged art and design to counter their neglect by the powerful. (A-)
Grant Wood: American Gothic and Other Fables. Fun to see American Gothic up close, but I was more impressed by some of Wood’s other work, particularly his illustration-like landscapes. I showed the kids a photo I had taken of one of the paintings and Ollie said, “that looks like a 3D rendering!” (B+)
Stephen Shore at MoMA. I’d label this a “must see” if you’re into photography at all. Shore’s shape-shifting career is inspiring. (A-)
Red Sparrow. I was texting with a friend about how cool it would be if J. Law’s character in Red Sparrow was Paige Jennings from The Americans all grown up, but the timelines don’t match up. (B-)
Harry Potter Hogwarts Battle. I don’t play a lot of board games so maybe this is a common thing now, but I really like how all the players have to work together against the game to win. But once you get past the first couple of decks, the games take *forever*. (B+)
The Royal Tenenbaums. Rushmore will always be my sentimental Wes Anderson fave, but Tenenbaums is right up there. (A)
Consider the Lobster by David Foster Wallace. I have been listening to the audiobook version while in the car, and Wallace’s reading of the first story, Big Red Son (about an adult video awards show), made me laugh so hard that I had to pull of the road at one point. (A)
Logan Lucky. Much better on the second watch. I don’t know why I didn’t appreciate it the first time around…I love Soderbergh and this is basically Ocean’s 7/11. (A-)
Moon. I saw this when it originally came out but didn’t like it as much the second time around. Great soundtrack though. (B+)
Simon and the Whale. Wonderful room and service. Really good cocktails. I know the kitchen crew and they still blew me away with the food. (A)
Girls Trip. I haven’t laughed so hard at a movie since I don’t know when. Bridesmaids maybe? Can’t wait to watch this again in a few months. (A-)
Ready Player One. I very much enjoyed watching this movie. Spielberg must have had fun going back through the 80s pop culture he had a large part in shaping. (A-)
Electricity. I’m writing this not from my usual home office but from the lobby of the local diner/movie theater. We had a wind storm last night, which knocked the power out at my house. That means no heat, no water, no wifi, and very poor cell reception. And a tree came down across the road I live on, so I was “stranded” for a few hours this morning until someone showed up with a chainsaw. I unreservedly recommend electricity (and civilization more generally). (A+)
A group of high school friends has been playing an elaborate game of tag since reconnecting at a reunion almost 30 years ago. A few years ago, one of the players wrote a piece for The Guardian about the game.
Since we had busy lives and lived hundreds of miles apart, we agreed on three rules. First, we would play it only in February each year; second, you were not allowed immediately to tag back the person who had tagged you; and finally, you had to declare to the group that you were “it”.
Now we are grown men, we don’t run like Usain Bolt, so subterfuge and collusion have become our weapons. Eleven months of the year are spent planning. Collaborating with a friend is where the fun is — we can spend hours discussing approaches.
I was tagged spectacularly a few years back when a friend popped round to show me his new car. As I approached it, Sean sprang out of the boot where he’d been hiding and tagged me. He’d flown 800 miles from Seattle to San Francisco just to stop being “it” — to shrug off the “mantle of shame”, as we call it. My wife was so startled she fell and injured her knee, but she wasn’t angry; she was pleased to see Sean.
Hollywood, who knows a winning idea when they see one,1 has now based a movie on the game. Tag stars Jon Hamm, Ed Helms, Jeremy Renner, and Rashida Jones; here’s the trailer:
And if you think some of the tagging scenarios in the movie are too good to be true (a funeral, really?)…yeah, no:
Some things we did early on we wouldn’t do now — like when Mike sneaked into Brian’s house at night, crept into the bedroom and woke him up to tag him, surprising the life out of him and his girlfriend.
Perhaps one of the most unexpected tags was during Mike’s father’s funeral. During the service, he felt a hand on his shoulder and turned to find Joe mouthing, “You’re it.” Afterwards, he said his father would have approved, because he found our game hilarious.
A decades-long game of adult tag is exactly the type of thing I love reading about but would never participate in. I am a huge stick-in-the-mud, but I’ve made my peace with it.
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.
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.
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?
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 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.
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.
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.”
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.
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.
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:
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 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.
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.
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.
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)
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.
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.
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
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.
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. :| ↩
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.”
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.
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.↩
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.
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)
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.
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)
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”.
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.”
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?
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