Later On

A blog written for those whose interests more or less match mine.

Archive for the ‘Chess’ Category

Coarse-brush week, and today is Leviathan and the Baili 171

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Yesterday I used my Omega 20102 and today you see the Omega Mighty Midget, a mix of boar and badger. I do wet the knot and let the brush sit while I shower to soften the boar bristles. A reader commented on his fondness for horsehair, and those brushes too have a pleasantly coarse feel on the face — not rough, but with a perceptible grain. So I thought II’d go through some of the coarser brushes in my collection for a pleasant change of pace.

The Mighty Midget, though, really doesn’t feel all that coarse. The badger smooths it out quite a bit. It did make a mighty fine lather from one of my favorite soaps, this one from Barrister & Mann.

The Baili 171 is a remarkable razor: $6 at the link (and I have no affiliation with the company — I’m just a customer), and it shaves like a dream. It’s so comfortable it doesn’t feel as though it’s doing much, but the result today is as smooth as one could want. I also like the looks and feel in the hand. It’s somewhat unusual in that it secures blade alignment through corner brackets instead of the usual studs from the cap (or baseplate). Works like a charm.

A good splash of Leviathan aftershave — I love the fragrance — and I’m set for the day, which will include some afternoon chess. I downloaded a free (and quite nice) chess-clock app for my iPhone, one provided by Chess.com. I recommend it if you play any two-person strategy games (chess, Go, checkers, or the like) since it ensures that the games move along, plus it’s easy to give (or receive) a time handicap — e.g., the stronger player gets 10 minutes and the weaker player gets 20. It’s not so cut and dried as that seems, since obviously the strong player will be thinking while the weaker player’s clock runs, but it can help — particularly if the division is 5 minutes vs. 25 minutes.

Written by LeisureGuy

30 June 2020 at 10:33 am

Posted in Chess, Games, Shaving, Software

Algorithm-governed interactions are often convenient, sometimes enraging, and occasionally dangerous

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Here’s an example of the enraging sort. The comments on YouTube for this video are interesting:

Written by LeisureGuy

29 June 2020 at 9:33 am

From homeless refugee to chess prodigy, 9-year-old dreams of becoming youngest grandmaster

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Aishwarya Kuma reports at ESPN:

IT’S 9 P.M., and 8-year-old Tani Adewumi is wired, like he’d just swallowed a bag of sugar. He had played chess all day, but he wanted to play more, at least until midnight. The first day of the 2019 New York State Scholastic Chess Championship had just ended, and he finished with three wins in as many matches, surprising a former champion and two other seeded players. He was heading into Day 2 — the final day of the tournament — in the lead, and he wanted to keep up the momentum when he returned to the huge Airbnb he was sharing with his family, his coach and a few other coaches in Saratoga Springs.

“If you want to win tomorrow, you better get your butt to sleep like the rest of the champions are right now,” his coach, Shawn Martinez, told him. And so, reluctantly, Tani went to bed, and as soon as he closed his eyes, he fell asleep. Already in his young life, Tani had spent nights in fear — fear for his own life, fear for the lives of his parents. Nerves over a chess match weren’t about to cause a single lost z.

The next day, Tani won his fourth match, no sweat. In the semifinal, Tani did something unorthodox: He purposely sacrificed his bishop for a pawn.

Why did you do that? Martinez wondered. I wouldn’t have made such a risky move.

It appeared to be a blunder, but Tani knew exactly what he was doing. He remembered studying a 19th-century chess game played by the legendary Paul Morphy, and he knew if he could bait his opponent into taking his bishop, he could win the game.

His opponent gave him a wry smile as he realized — too late — why Tani had made that move, the one that would send him to the championship match with a perfect record.

Incredulous, Martinez plugged all of the moves up until the sacrifice of the bishop into an automated chess program on his laptop. After the match, he showed the results to Tani: The strongest move Tani could have made at that point was to sacrifice his bishop. It was aggressive, bold and brave. It was a move most chess players wouldn’t even consider.

But Tani is no ordinary chess player. And his journey isn’t ordinary, either. Fifteen months earlier, his family had settled into a New York City homeless shelter after fleeing Nigeria. Thirteen months earlier, he couldn’t tell a rook from a pawn. That March day, after drawing in the final, he was crowned a state champion. They didn’t know it then, but Tani’s 8-year-old brain and its ability to think 20 moves ahead on an 8-by-8 chessboard were about to change the Adewumis’ lives forever.

“That moment was everything,” Martinez says. “I knew then he was meant for greatness.”

ON A DREARY December 2016 afternoon, Tani’s father, Kayode Adewumi, sat in his dining room chair in Abuja, the capital of Nigeria, with his palms on his head, staring at his computer. A poster with the words “No to Western education” and “Kill all Christians” screamed at him from the screen. But what was more terrifying was the logo that accompanied the words — a logo he could recognize in his sleep. It was Boko Haram.

Four men had come into his printing shop earlier that afternoon and, after handing him a thumb drive, asked him to print 25,000 copies of the poster saved on the drive. Kayode didn’t think much about it until this moment, back in his house, with his wife, Oluwatoyin, looking at him, her eyes narrowed and worry smeared across her forehead.

Accepting the business meant he had to work for Boko Haram, a terrorist organization, and that, as a Christian, and a human being, he couldn’t bring himself to do. But refusing essentially meant a death sentence for him and his family, especially now that he’s seen what the poster says and can identify the four men.

He could hear Tani, 6, and his older brother, Austin, playing with friends out in the front yard, arguing about who gets to kick the soccer ball, and a fresh wave of fear went through his body.

What are we going to do? Where are we going to go?

Even before that threat, the Adewumis noticed their country changing under the attack of Boko Haram. Ever since the 2014 abduction of 276 girls from a northern Nigerian high school, Boko Haram’s attacks on civilians had only increased. In 2015, a bomb blast occurred so close to Oluwatoyin’s office that she could feel the heat as security escorted her out of her office. The day before the Boko Haram men came into Kayode’s print shop, Tani and Austin had come home from school early — they were evacuated after Boko Haram sent a message threatening another attack on a school in Abuja. Tani had peppered his parents with questions. “Why were we let off early?” “Who is Boko Haram?” “What is religious extremism?” All the while, his parents were able to shield him. They didn’t know how much longer they could keep doing that.

Kayode came up with a plan. When the men come for their posters the next day, he’ll tell them he couldn’t do the job because his printing press had broken the previous evening. He’ll then hand them the flash drive and tell them he hadn’t looked at it because he hadn’t needed to. Clean lie. He prayed they’d bite and leave his family alone.

They didn’t believe him. A week later, when only Oluwatoyin was home and the children were asleep, they showed up at the Adewumis’ house looking for Kayode’s laptop. They assumed Kayode had seen the poster and saved it to use against them. Let’s use Oluwatoyin to send Kayode a message, Oluwatoyin heard them whisper to each other in Arabic.

What they didn’t know was this: Oluwatoyin was raised Muslim and spoke Arabic growing up. When she heard this, she knew they were going to kill her or rape her. So she did the one thing she could still do: She knelt and began to pray. Atuasal iilayk — I’m begging you. She said the Arabic phrase over and over. “Are you a Muslim?” they asked her. “Yes,” she whispered, as tears fell down her cheeks. Silence followed her response. They looked at each other, and without saying another word, they exited the house.

A few weeks later, Kayode asked Oluwatoyin to pack a small bag of necessities. Without informing anybody, the family moved to Akure in rural Nigeria, to a house with a tall fence. They hid there, using their savings to get by, hoping Boko Haram would lose track of them so they could eventually go back to living a normal life in that small town.

A few months into their life in Akure, when they were getting ready to go to bed, they heard a noise — like somebody was shaking their fence. Boko Haram, they realized, had found them. “You’ve been escaping us for far too long, but we know you are inside, and we know that today you will go to heaven,” they heard the group of men yelling from outside. Kayode asked Oluwatoyin to go to their kids’ bedroom and pray hard, because nothing short of a miracle could save them now.

Kayode knew it would take a while for them to knock down the fence, but a back door attached to the fence led directly to the kitchen. If they found the back door, they’d get inside within minutes. He came up with a plan: He would push open the kitchen door and announce himself. They’d follow him and leave his family alone. It worked — even if by accident. When they heard him, Kayode believes they mistook him for the police and yelled, “It’s the police, let’s go,” and jumped into a car and fled. Kayode stayed outside the kitchen door all night, waiting to see whether they’d come back.

As daylight broke, Kayode wearily walked back into the house to find Oluwatoyin calling him frantically. The kids, who were asleep before, were now awake, fear etched on their faces.

Their faces confirmed the one thing he’d been thinking over and over in his head. They had to leave the country for good — and they had to do it now. . .

Continue reading. There’s much more.

Written by LeisureGuy

23 May 2020 at 4:57 pm

Posted in Chess, Daily life, Games

The magic of chess

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Written by LeisureGuy

6 February 2020 at 4:53 pm

Posted in Chess, Games, Video

Chess: A life-lesson in concentration

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Jonathan Rowson,co-founder and director of Perspectiva, a research institute in London, and author of The Moves that Matter: A Chess Grandmaster on the Game of Life, writes in Aeon:

Arriving at the chess board is like entering an eagerly anticipated party. All my old friends are there: the royal couple, their associates, the reassuringly straight lines of noble infantry. I adjust them, ensuring that they are optimally located in the centre of their starting squares, an anxious fidgeting and tactile caress. I know these pieces, and care about them. They are my responsibility. And I’m grateful to my opponent for obliging me to treat them well on pain of death.

In many ways, I owe chess everything. Since the age of five, the game has been a source of friendship, refuge and growth, and I have been a grandmaster for 20 years. The lifelong title is the highest awarded to chess players, and it is based on achieving three qualifying norms in international events that are often peak performances, combined with an international rating reflecting a consistently high level of play – all validated by FIDE, the world chess federation. There are about 1,500 grandmasters in the world. At my peak, I was just outside the world top 100, and I feel some gentle regret at not climbing even higher, but I knew there were limits. Even in the absence of a plan A for my life, chess always felt like plan B, mostly because I couldn’t imagine surrendering myself to competitive ambition. I have not trained or played with serious professional intent for more than a decade, and while my mind remains charmed by the game, my soul feels free of it.

In recent years, I have worked in academic and public policy contexts, attempting to integrate our understanding of complex societal challenges with our inner lives, while also looking after my two sons. I miss many things about not being an active player. I miss the feeling of strength, power and dignity that comes with making good decisions under pressure. I miss the clarity of purpose experienced at each moment of each game, the lucky escapes from defeat, and the thrill of the chase towards victory. But, most of all, I miss the experience of concentration.

I can still concentrate, of course, but not with the same reliability and intensity that a life of professional chess affords. In fact, from a distance, chess looks to me suspiciously like a socially permissible pretext to concentrate for several hours at a time. In The Island from the Day Before (1994), Umberto Eco composes a love letter that includes the line: ‘[O]nly in your prison does [my heart] enjoy the most sublime of freedoms’ – that could be said of chess, too, and the experience of concentration is what makes it possible. I believe concentration is a defining feature of a fulfilling life, a necessary habit of mind for a viable civilisation, and that chess can teach us more about what concentration really means.

Any skilled endeavour entails concentration, but chess is unusual in requiring that we concentrate not for a few minutes at a time, but for several hours at a time, within tournaments, for days at a time, and within careers, for years at a time. Concentration is the sine qua non of the chess experience.

In chess, concentration usually unfolds in quick succession through perceiving, desiring and searching. But it’s recursive, so I often find something I didn’t expect in a way that leads me to see my position differently and want something else from it. My perception is pre-patterned through years of experience, so I don’t see one square or piece at a time. Instead, I see the whole position as a situation featuring relationships between pieces in familiar strategic contexts; a castled king, a fianchettoed bishop, a misplaced knight, an isolated pawn; it’s a kind of conceptual grammar. The meaning of the position is embedded in those patterns, partly revealed and partly concealed, and my search to do the right thing feels fundamentally aesthetic in nature.

I could describe the feeling as a kind of evaluative hunting  not so much for a particular target, but for trails of ideas that look right and feel right. I am drawn towards some transfigurations of the patterns that make me look deeper, and repelled by others. Good moves have the qualities of truth and beauty. They are discoveries of how things are, and should be.

However, chess invites me to deepen my concentration a few centimetres away from another being who is also trying to concentrate; someone I can smell, sense moving, and hear breathing. I often know, even like, these people, but they loom within my psyche in a relatively impersonal sense – a familiar energy, not friends as such. I sometimes think of chess opponents as psychopathic flatmates with whom I have to share a living space. They look harmless, but I know we signed the same contract that says they need to try to get inside my room, steal my possessions and hunt me down, before killing me; naturally, I am obliged to do the same to them. Together we create a story, and narrative themes such as attack and defence are both reduced and reified into particular moves with particular pieces on particular squares, which we record like stenographers, into our own arcana of algebraic notation. The climax of a game’s story might be ‘Brutal counter-attack!’ but the record merely reflects the logical power of a short sequence of moves, for instance: ‘…34. Bf3 Nh3+ 35.Kh1 Qg4!! Resigns.’

The forces on the board are always embroiled, but concentration is particularly important when the pieces stop eyeing each other from a strategic distance, and come into direct tactical contact. At such moments, spotting a hidden detail could guarantee victory, while missing it could lead to inexorable defeat. Such details were usually a few moves away from any given position in front of me, so I would have to seek them out; while much of chess thinking has a narrative quality, this seeking called for the merciless logic of calculation. It’s about keeping track of the balance of material forces as they attempt to eliminate each other in a battle for supremacy. The process is strenuous, even painful, but learning to appreciate the beauty of uncovering the truth was critical in my advancing up the grandmaster ranks.

One of the highlights of my chess career was beating the Russian-born grandmaster Alex Yermolinsky at the World Open in Philadelphia in 2002, because it was a palpable experience of self-overcoming. ‘Yermo’ is a two-time US Champion. On paper, he was the favourite, but I’d recently been training by solving chess exercises, setting up carefully vetted positions and deciding what I would play, then comparing my thoughts with the book answer. Yermolinsky offered a pawn as bait, and I very nearly didn’t take it because doing so would allow him to play a series of forcing moves, including an elegant counterattack that appeared decisive. Looking deeper, I discovered a surprising detail right at the end of the line, in which my knight could retreat back to its original square, solving all my defensive problems and leaving me with a decisive advantage. I checked the variation just once – the wise side of neurotic! – and we briskly played straight down the line. The clock clicked gently with each move. Yermo played the impressive-looking tactic that we’d both anticipated as if it was decisive. Then I executed the additional detail only I had seen, and he immediately resigned. I felt strong.

Concentration is not always so rewarding. It comes and goes, forms and collapses, builds and then crumbles, because there is an upper limit to what players can hold in their heads at any one time. I find that I move towards my upper limit and away from it repeatedly. Peering into the unfolding position, it is as if I am driving more or less automatically, until new possibilities flash before me like bikes emerging from side-streets, and bring me back to the challenge of steering consciousness. At such moments, the edifice of thought I have built is likely to collapse. If I’m not careful, I can spend far too many minutes in this state of perpetual irresolution, seeking but not finding an answer to what is happening, because there is just too much meaning in the position for my mind to process. This challenge of learning how to hold complexity in mind and still make good decisions is pertinent not just to chess but to life more generally.

As a chess grandmaster, I find the familiar injunction to ‘Concentrate!’ a little naive. Concentration is not like a bulb that we can turn on and off with a switch, because we are not just the bulb; we are also the switcher and the switch. Humans are more like  . . .

Continue reading. There’s much more.

Written by LeisureGuy

6 January 2020 at 8:50 pm

Posted in Chess

Alpha Zero plays the Evans gambit against Stockfish

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Fascinating game.

Written by LeisureGuy

2 October 2019 at 10:05 am

Deep Mind AI Alpha Zero’s Positional Masterpiece With the Black Pieces

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This game was interesting to me because I never know what to do in very closed positions, such as seen in this game. But AlphaZero knows.

Written by LeisureGuy

3 August 2019 at 8:33 pm

Posted in Chess, Games, Video

What a game! by the Indian Tal

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24 July 2019 at 7:58 pm

Posted in Chess, Games, Video

Chess game of the (21st) century

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Perhaps premature, but an amazing game from a very young player.

Written by LeisureGuy

2 April 2019 at 5:52 pm

Posted in Chess

Gary Kasparov has a nice article on chess and the new 8-year-old US champion—an immigrant who lives in a homeless shelter

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Gary Kasparov writes in the Washington Post:

Garry Kasparov is the chairman of the Renew Democracy Initiative and author of “Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins.

The victory of 8-year-old Tanitoluwa Adewumi in the New York State K-3 championship this month has received more attention than any chess story in a long time. His circumstances, a Nigerian refugee living in a family shelter, were the key ingredient, even more than his dazzling smile next to a trophy taller than he is.

According to reports, “Tani” had learned to play only a year earlier, while most of his rivals had been playing in tournaments for several years. It’s an irresistible underdog story, well-deserving of going viral and generating an outpouring of donations to aid him and his family.

This heart-warming tale is also a quintessentially American one. Despite his family’s conditions, Tani learned to play at a good chess program in an excellent Manhattan public school. His mother took the initiative of getting him into the school chess club, reminding any true chess fan of a similar letter written by the mother of future U.S. world champion Bobby Fischer. (All praise to assertive chess mothers like my own!)

The United States is where the world’s talent comes to flourish. Since its inception, one of America’s greatest strengths has been its ability to attract and channel the energy of wave after wave of striving immigrants. It’s a machine that turns that vigor and diversity into economic growth. It may mean opening a dry-cleaners or a start-up that becomes Google. It could mean studying medicine, law or physics, or — as Tani says he would like to do — becoming the world’s youngest chess champion.

Many of the questions I received as world champion centered on why the Soviet Union produced so many great chess players. After the dissolution of the U.S.S.R., these questions were asked again along new national borders. Why did Russia, or Armenia, or my native Azerbaijan have so many grandmasters? Was there something in the water, the genes or the schools? And why weren’t there more chess prodigies from the United States (or wherever the questioner was from)?

My answer was always the same: Talent is universal, but opportunity is not, and talent cannot thrive in a vacuum. Finding talent is a numbers game — the more players there are, the more excellent ones will be found. (This same math applies to the gender disparity in chess. There are so few elite female players because there are still far fewer girls in a traditionally male pastime. Addressing that imbalance is why my foundation sponsors the All-Girls Scholastic Championship.)

The Soviet leadership always looked at chess as an opportunity to tout the superiority of the communist system. The leadership invested heavily in the game and promoted it at every level, for kids and professionals. I benefited directly from this aggressive farm system, receiving good coaching at a very young age in Baku and quickly being placed into a special chess school under the direction of former world champion Mikhail Botvinnik.

I was lucky to find chess, which was like a native language to me, but it wasn’t luck that chess found me. With that in mind, I have worked since 2002 to bring chess into education systems around the world. Chess is excellent for boosting children’s cognitive development and academic skills, but growing the base also means finding more top-level talent.

America’s recognition of chess’s benefits may help explain a development that merits wider recognition: This is a golden age for chess in the United States. The . . .

Continue reading.

Written by LeisureGuy

23 March 2019 at 4:33 pm

AlphaZero’s attacking game in chess

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This is quite an interesting game because of the way AlphaZero evaluates the worth of pieces: very positional weighting, apparently. AlphaZero is self-taught so far as play is concerned: the program was fed only the rules, with no guidance regarding values of pieces, game strategy, game tactics, or the scores of any human games. AlphaZero then played millions of games against itself and learned from that experience. In this video AlphaGo is playing the strongest chess engine to date, Stockfish. I found it quite interesting. Anna Rudolph provides a very helpful commentary, so sound on for this one.

 

Written by LeisureGuy

4 January 2019 at 9:13 am

How the Artificial-Intelligence Program AlphaZero Mastered Its Games

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James Somers writes in the New Yorker:

A few weeks ago, a group of researchers from Google’s artificial-intelligence subsidiary, DeepMind, published a paper in the journal Science that described an A.I. for playing games. While their system is general-purpose enough to work for many two-person games, the researchers had adapted it specifically for Go, chess, and shogi (“Japanese chess”); it was given no knowledge beyond the rules of each game. At first it made random moves. Then it started learning through self-play. Over the course of nine hours, the chess version of the program played forty-four million games against itself on a massive cluster of specialized Google hardware. After two hours, it began performing better than human players; after four, it was beating the best chess engine in the world.

The program, called AlphaZero, descends from AlphaGo, an A.I. that became known for defeating Lee Sedol, the world’s best Go player, in March of 2016. Sedol’s defeat was a stunning upset. In “AlphaGo,” a documentary released earlier this year on Netflix, the filmmakers follow both the team that developed the A.I. and its human opponents, who have devoted their lives to the game. We watch as these humans experience the stages of a new kind of grief. At first, they don’t see how they can lose to a machine: “I believe that human intuition is still too advanced for A.I. to have caught up,” Sedol says, the day before his five-game match with AlphaGo. Then, when the machine starts winning, a kind of panic sets in. In one particularly poignant moment, Sedol, under pressure after having lost his first game, gets up from the table and, leaving his clock running, walks outside for a cigarette. He looks out over the rooftops of Seoul. (On the Internet, more than fifty million people were watching the match.) Meanwhile, the A.I., unaware that its opponent has gone anywhere, plays a move that commentators called creative, surprising, and beautiful. In the end, Sedol lost, 1-4. Before there could be acceptance, there was depression. “I want to apologize for being so powerless,” he said in a press conference. Eventually, Sedol, along with the rest of the Go community, came to appreciate the machine. “I think this will bring a new paradigm to Go,” he said. Fan Hui, the European champion, agreed. “Maybe it can show humans something we’ve never discovered. Maybe it’s beautiful.”

AlphaGo was a triumph for its creators, but still unsatisfying, because it depended so much on human Go expertise. The A.I. learned which moves it should make, in part, by trying to mimic world-class players. It also used a set of hand-coded heuristics to avoid the worst blunders when looking ahead in games. To the researchers building AlphaGo, this knowledge felt like a crutch. They set out to build a new version of the A.I. that learned on its own, as a “tabula rasa.”

The result, AlphaGo Zero, detailed in a paper published in October, 2017, was so called because it had zero knowledge of Go beyond the rules. This new program was much less well-known; perhaps you can ask for the world’s attention only so many times. But in a way it was the more remarkable achievement, one that no longer had much to do with Go at all. In fact, less than two months later, DeepMind published a preprint of a third paper, showing that the algorithm behind AlphaGo Zero could be generalized to any two-person, zero-sum game of perfect information (that is, a game in which there are no hidden elements, such as face-down cards in poker). DeepMind dropped the “Go” from the name and christened its new system AlphaZero. At its core was an algorithm so powerful that you could give it the rules of humanity’s richest and most studied games and, later that day, it would become the best player there has ever been. Perhaps more surprising, this iteration of the system was also by far the simplest.

A typical chess engine is a hodgepodge of tweaks and shims made over decades of trial and error. The best engine in the world, Stockfish, is open source, and it gets better by a kind of Darwinian selection: someone suggests an idea; tens of thousands of games are played between the version with the idea and the version without it; the best version wins. As a result, it is not a particularly elegant program, and it can be hard for coders to understand. Many of the changes programmers make to Stockfish are best formulated in terms of chess, not computer science, and concern how to evaluate a given situation on the board: Should a knight be worth 2.1 points or 2.2? What if it’s on the third rank, and the opponent has an opposite-colored bishop? To illustrate this point, David Silver, the head of research at DeepMind, once listed the moving parts in Stockfish. There are more than fifty of them, each requiring a significant amount of code, each a bit of hard-won chess arcana: the Counter Move Heuristic; databases of known endgames; evaluation modules for Doubled Pawns, Trapped Pieces, Rooks on (Semi) Open Files, and so on; strategies for searching the tree of possible moves, like “aspiration windows” and “iterative deepening.”

AlphaZero, by contrast, has only two parts: a neural network and an algorithm called Monte Carlo Tree Search. (In a nod to the gaming mecca, mathematicians refer to approaches that involve some randomness as “Monte Carlo methods.”) The idea behind M.C.T.S., as it’s often known, is that a game like chess is really a tree of possibilities. If I move my rook to d8, you could capture it or let it be, at which point I could push a pawn or move my bishop or protect my queen. . . . The trouble is that this tree gets incredibly large incredibly quickly. No amount of computing power would be enough to search it exhaustively. An expert human player is an expert precisely because her mind automatically identifies the essential parts of the tree and focusses its attention there. Computers, if they are to compete, must somehow do the same.

This is where the neural network comes in. AlphaZero’s neural network receives, as input, the layout of the board for the last few moves of the game. As output, it estimates how likely the current player is to win and predicts which of the currently available moves are likely to work best. The M.C.T.S. algorithm uses these predictions to decide where to focus in the tree. If the network guesses that ‘knight-takes-bishop’ is likely to be a good move, for example, then the M.C.T.S. will devote more of its time to exploring the consequences of that move. But it balances this “exploitation” of promising moves with a little “exploration”: it sometimes picks moves it thinks are unlikely to bear fruit, just in case they do.

At first, the neural network guiding this search is fairly stupid: it makes its predictions more or less at random. As a result, the Monte Carlo Tree Search starts out doing a pretty bad job of focussing on the important parts of the tree. But the genius of AlphaZero is in how it learns. It takes these two half-working parts and has them hone each other. Even when a dumb neural network does a bad job of predicting which moves will work, it’s still useful to look ahead in the game tree: toward the end of the game, for instance, the M.C.T.S. can still learn which positions actually lead to victory, at least some of the time. This knowledge can then be used to improve the neural network. When a game is done, and you know the outcome, you look at what the neural network predicted for each position (say, that there’s an 80.2 per cent chance that castling is the best move) and compare that to what actually happened (say, that the percentage is more like 60.5); you can then “correct” your neural network by tuning its synaptic connections until it prefers winning moves. In essence, all of the M.C.T.S.’s searching is distilled into new weights for the neural network.

With a slightly better network, of course, the search gets slightly less misguided—and this allows it to search better, thereby extracting better information for training the network. On and on it goes, in a feedback loop that ratchets up, very quickly, toward the plateau of known ability.

When the AlphaGo Zero and AlphaZero papers were published, a small army of enthusiasts began describing the systems in blog posts and YouTube videos and building their own copycat versions. Most of this work was explanatory—it flowed from the amateur urge to learn and share that gave rise to the Web in the first place. But a couple of efforts also sprung up to replicate the work at a large scale. The DeepMind papers, after all, had merely described the greatest Go- and chess-playing programs in the world—they hadn’t contained the source code, and the company hadn’t made the programs themselves available to players. Having declared victory, its engineers had departed the field.

Gian-Carlo Pascutto, a computer programmer who works at the Mozilla Corporation, had a track record of building competitive game engines, first in chess, then in Go. He followed the latest research. As the combination of Monte Carlo Tree Search and a neural network became the state of the art in Go A.I.s, Pascutto built the world’s most successful open-source Go engines—first Leela, then LeelaZero—which mirrored the advances made by DeepMind. The trouble was that DeepMind had access to Google’s vast cloud and Pascutto didn’t. To train its Go engine, DeepMind used five thousand of Google’s “Tensor Processing Units”—chips specifically designed for neural-network calculations—for thirteen days. To do the same work on his desktop system, Pascutto would have to run it for seventeen hundred years.

To compensate for his lack of computing power, Pascutto distributed the effort. LeelaZero is a federated system: anyone who wants to participate can download the latest version, donate whatever computing power he has to it, and upload the data he generates so that the system can be slightly improved. The distributed LeelaZero community has had their system play more than ten million games against itself—a little more than AlphaGo Zero. It is now one of the strongest existing Go engines.

It wasn’t long before the idea was extended to chess. In December of last year, when the AlphaZero preprint was published, “it was like a bomb hit the community,” Gary Linscott said. Linscott, a computer scientist who had worked on Stockfish, used the existing LeelaZero code base, and the new ideas in the AlphaZero paper, to create Leela Chess Zero. (For Stockfish, he had developed a testing framework so that new ideas for the engine could be distributed to a fleet of volunteers, and thus vetted more quickly; distributing the training for a neural network was a natural next step.) There were kinks to sort out, and educated guesses to make about details that the DeepMind team had left out of their papers, but within a few months the neural network began improving. The chess world was already obsessed with AlphaZero: posts on chess.com celebrated the engine; commentators and grandmasters pored over the handful of AlphaZero games that DeepMind had released with their paper, declaring that this was “how chess ought to be played,” that the engine “plays like a human on fire.” Quickly, Lc0, as Leela Chess Zero became known, attracted hundreds of volunteers. As they contributed their computer power and improvements to the source code, the engine got even better. Today, one core contributor suspects that it is just a few months away from overtaking Stockfish. Not long after, it may become better than AlphaZero itself.

When we spoke over the phone, Linscott marvelled that a project like his, which would once have taken a talented doctoral student several years, could now be done by an interested amateur in a couple of months. Software libraries for neural networks allow for the replication of a world-beating design using only a few dozen lines of code; the tools already exist for distributing computation among a set of volunteers, and chipmakers such as Nvidia have put cheap and powerful G.P.U.s—graphics-processing chips, which are perfect for training neural networks—into the hands of millions of ordinary computer users. An algorithm like M.C.T.S. is simple enough to be implemented in an afternoon or two. You don’t even need to be an expert in the game for which you’re building an engine. When he built LeelaZero, Pascutto hadn’t played Go for about twenty years.

David Silver, the head of research at DeepMind, has pointed out a seeming paradox at the heart of his company’s recent work with games:  . . .

Continue reading.

Written by LeisureGuy

28 December 2018 at 11:10 am

Posted in Chess, Games, Go, Software, Technology

Chessplayer distribution by sex and skill

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This graphic is from SparkChess.com, which developed the little chess-playing program I have on my Mac, which I like quite a bit. It has different “player” engines, including one, Claire, I can occasionally beat. (At the link, you also can play Claire—and some of the others.) In fact, in posting this I went to the on-line site and played Claire and beat her/it: Stonewall Attack, me as White.

Written by LeisureGuy

18 December 2018 at 10:38 am

Stockfish takes apart a classic chess problem, blowing up the accepted solution

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Worth watching if you like chess:

Written by LeisureGuy

26 April 2018 at 9:55 pm

Posted in Chess, Games

AlphaZero and the Protected Passed Pawn

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I thought this commentary was particularly good, though I realize tastes differ. Fascinating game.

Written by LeisureGuy

12 January 2018 at 11:16 pm

I enjoyed this one

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I am playing White, “Claire” on SparkChess is Black:

  1. e4 d5
  2. e5 f6
  3. f4 fxe5
  4. fxe5 Nc6
  5. d4 Rb8
  6. h3 Bd7
  7. Nf3 b6
  8. Bb5 Nxe5
  9. Bxd7+ Nxd7
  10. Ne5 Nxe5
  11. dxe5 Rb7
  12. e6 Qd6
  13. Qh5+ g6
  14. Qe2 Qg3+
  15. Kd1 a5
  16. Rf1 Ra7
  17. Rxf8+ Kxf8
  18. Qf1+ Ke8
  19. Qf7+ Kd8
  20. Qf8#

 

Written by LeisureGuy

9 September 2017 at 2:56 pm

Posted in Chess

The Greatest King Walk in History of Chess: Short vs Timman 1991

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Written by LeisureGuy

5 May 2017 at 6:23 pm

Posted in Chess, Video

Wow! A complete course in chess tactics, theory AND practice (problems included)

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Really a comprehensive job. This Open Culture post points out this amazing free resource. The Open Culture post also has links to free ebook downloads of the book.

Written by LeisureGuy

13 April 2017 at 8:39 pm

Posted in Books, Chess

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