I first read about Lord Dunsany - I am happy to report his full name was Edward John Moreton Drax Plunkett - in a collection of Arthur C. Clarke's non-fiction.

I first read about Lord Dunsany - I am happy to report his full name was Edward John Moreton Drax Plunkett - in a collection of Arthur C. Clarke's non-fiction.

Video games are often blamed for unemployment, violence in society and addiction – including by partisan politicians raising moral concerns. Blaming video games for social or moral decline might feel like something new.

Anatoly Karpov, the 66-year-old former World Chess Champion, was comfortable playing chess underneath the bright lights and in front of the cameras on a television studio set. His opponent, Mikhail “Misha” Osipov, had never played on quite so big a stage before.

The story of IBM’s Deep Blue computer defeating world chess champion Garry Kasparov in 1997 has been told so many times that it’s practically shorthand for the philosophical debate over man vs. machine. But the story lacks subtlety and perhaps the right moral.

Chou Chun-hsun, one of the world's top players of the ancient game of Go, sat hunched over a board covered with a grid of closely spaced lines. To the untrained eye, the bean-size black and white stones scattered across the board formed a random d...

LONDON — It was Game 8 of the World Chess Championship, and the four-time winner and defending champion, 42-year-old Viswanathan Anand of India, playing white, was a game down to the Israeli Boris Gelfand, 43. Gelfand, perhaps buoyed by his success in Game 7, had chosen an unexpectedly sharp line against Anand, who is renowned for his ability to calculate quickly on the board.

The screen of Deep Junior, the computer that the chess champion Gary Kasparov faced in 2003. Chip East/Reuters The screen of Deep Junior, the computer that the chess champion Gary Kasparov faced in 2003. Commenting live, the Hungarian grandmaster Peter Leko, a challenger for the world title in 2005, preferred Gelfand’s position. But just as he was expressing surprise at Anand’s strategy, Anand’s 17th move brought the game to a sudden close. Anand had deceived both his challenger and one of the strongest players in the world.

But many lesser players watching the game live with specialized computer chess engines weren’t flummoxed by Anand’s play; programs like Houdini had flagged Anand’s trap a few moves earlier. Computers have so flattened the game of chess that even novices like me can make some sense of the moves being played at the highest level.

Grandmasters of comparable skill now come to championship games with computer-generated analysis of their opponents’ opening lines and likely moves. Home preparation has always been important, but computers have made it much more so and have thereby changed the nature of the game. Now risky plays are almost inevitably punished because they’ve been anticipated, making Anand’s play in Game 8 of the recent championship a rare exception.

Computers don’t play chess perfectly — the game is far too complicated for that — but they play in a way that’s more exciting and more decisive. They also play better than humans. Which is why since chess is no longer about just two humans facing each other anyway — thanks to pre-game computer-assisted preparation — it makes sense to allow the use of computers during competitive games. (Of course, for the sake of fairness, the two players would have equal access to the same computer engine.) This idea, known as “advanced chess,” has been endorsed by the former chess champion Gary Kasparov.

So far, experiments with advanced chess suggest that the powers of man and machine combined don’t just make for a stronger game than a man’s alone; they also seem to make for a stronger game than a machine’s alone. Allowing chess players the assistance of the best computer chess engine available during top tournaments would ensure that the contests really do showcase the very best chess being played on earth.

It would also teach us important things about the world.

Take, for example, a game that’s winding down with this particular configuration: rook and a bishop versus two knights. This situation came up in a world championship qualifying game in 2007, and the match concluded in a draw. But computer analysis showed that the game was really a forced win for black in 208 moves. This revealed not just a strategic truth about chess, but also a phenomenological truth, a truth about reality, that would otherwise have remained inaccessible.

Computers have made possible a famous proof in mathematics — the four-color theorem — but most mathematicians continue to hope the proof can be found without the assistance of computers. With chess, though, some truths are simply unknowable without a computer. As computers get better at chess, letting the best chess players work with them more would give us a better understanding of the game, our own limits and the world.

Chess has problems. Not for most of us, perhaps - not for the bluffers and the fudgers and the seat-of-the-pants players who prod a path through matchups in which each side's strategy is a winsome, wobbling comedy of errors. No, chess has problems at the grandmaster level.

Prior to 1849, there was no such thing as a “normal chess set.” At least not like we think of it today. Over the centuries that chess had been played, innumerable varieties of sets of pieces were created, with regional differences in designation and appearance.

In an address to the New York State Chess Association in 1952, Duchamp attempted to define the link between chess and art: "I believe that every chess player experiences a mixture of two aesthetic pleasures: first, the abstract image akin to the poetic idea of writing; secondly, the sensuous pleasure of the ideographic execution of that image on the chessboard. From my close contacts with artists and chess players, I have come to the conclusion that while all artists are not chess players, all chess players are artists."

At one level, I am sure that is right. I don't know anyone devoted to chess who is purely motivated by the desire to win; that is, for whom it is simply a mental sport, as everyone outside the game seems to suppose. When we sit down to play our intention is to win; but we also start the game looking at the pieces in their original positions and feeling overcome with a sense of the possibility of creating something beautiful with them. At the end of the game we are almost invariably disappointed. If we lose, of course, that's bad; but also if we win, yet then discover that we missed a more incisive way of concluding the game, we are filled with what I can only describe as a sense of artistic dissatisfaction — that we have made a crude daub on an otherwise harmonious work of art.

Earlier this year, IBM celebrated the 15-year anniversary of its supercomputer Deep Blue beating chess champion Garry Kasparov. According to a new book, however, it may have been an accidental glitch rather than computing firepower that gave Deep Blue the win. At the Washington Post, Brad Plumer high

Gadgetry of any sort has a rocky history in chess.

In the late 18th century, for example, a Hungarian engineer named Wolfgang von Kempelen toured Europe with a machine called The Turk, which he promoted as a mechanical chess master. Legend holds that Napoleon and Ben Franklin are among the chess aficionados who lost to Kempelen's brainchild. Decades after those big wins, word got out that The Turk, which Kempelen built to woo Empress Maria Theresa Walburga Amalia Christina of Austria, was a royal scam: For all its pulleys and wheels, Kempelen always made sure an accomplished and totally human chess player was hiding inside the machine, making all the right moves.

The Virginia scandal involved the opposite ruse, in which a machine surreptitiously called the shots for a player. The chess engines this scheme centered on are relatively new: Computers only surpassed humans at the chessboard during young Smiley's lifetime. Scientists had an easier time designing digital brains that could produce atom bombs or navigate lunar landings than they did fashioning a machine that could play chess worth a darn. Plainly, until relatively recently, chess was too complicated for computers. An analysis of chess's complicatedness in Wired determined that the number of possible positions in an average 40-move game is 10 to the 128th power, a sum "vastly larger than the number of atoms in the known universe."

Artificial intelligence began with an ambitious research agenda: To endow machines with some of the traits we value most highly in ourselves—the faculty of reason, skill in solving problems, creativity, the capacity to learn from experience. Early results were promising. Computers were programmed to play checkers and chess, to prove theorems in geometry, to solve analogy puzzles from IQ tests, to recognize letters of the alphabet. Marvin Minsky, one of the pioneers, declared in 1961: “We are on the threshold of an era that will be strongly influenced, and quite possibly dominated, by intelligent problem-solving machines.”

Fifty years later, problem-solving machines are a familiar presence in daily life. Computer programs suggest the best route through cross-town traffic, recommend movies you might like to see, recognize faces in photographs, transcribe your voicemail messages and translate documents from one language to another. As for checkers and chess, computers are not merely good

Wow. It’s been a while since I updated my blog. I intend to get active again here soon, with regular updates on my research. For now, I thought it might be worth posting a text I’ve been mulling over for a while (!) Yesterday I came across this old TED presentation by Daniel Hillis, and it set off a bunch of bells tolling in my head. His book The Pattern on the Stone was one I leafed through a few months back whilst hunting for some analogies about (digital) materiality. The resulting brainstorm is what follows. (This blog post, from even longer ago, acts as a natural introduction: On (Text and) Exaptation) In the 1960s and 70s Roland Barthes named “The Text” as a network of production and exchange. Whereas “the work” was concrete, final – analogous to a material – “the text” was more like a flow, a field or event – open ended. Perhaps even infinite. In, From Work to Text, Barthes wrote: The metaphor of the Text is that of the network… (Barthes 1979) This semiotic approach to discourse, by initiating the move from print culture to “text” culture, also helped lay the ground for a contemporary politics of content-driven media. Skipping backwards through From Work to Text, we find this statement: The text must not be understood as a computable object. It would be futile to attempt a material separation of works from texts. I am struck here by Barthes” use of the phrase “computable object”, as well as his attention to the “material”. Katherine Hayles in her essay, Text is Flat, Code is Deep, (Hayles 2004) teases out the statement for us: ‘computable’ here mean[s] to be limited, finite, bound, able to be reckoned. Written twenty years before the advent of the microcomputer, his essay stands in the ironic position of anticipating what it cannot anticipate. It calls for a movement away from works to texts, a movement so successful that the ubiquitous ‘text’ has all but driven out the media-specific term book. Hayles notes that the “ubiquity” of Barthes” term “Text” allowed – in its wake – an erasure of media-specific terms, such as “book”. In moving from, The Work to The Text, we move not just between different politics of exchange and dissemination, we also move between different forms and materialities of mediation. (Manovich 2002)For Barthes the material work was computable, whereas the network of the text – its content – was not.

In 1936, the year that Alan Turing wrote his iconic paper ‘On Computable Numbers’, a German engineer by the name of Konrad Zuse built the first working digital computer. Like its industrial predecessors, Zuse’s computer was designed to function via a series of holes encoding its program. Born as much out of convenience as financial necessity, Zuse punched his programs directly into discarded reels of 35mm film-stock. Fused together by the technologies of weaving and cinema, Zuse’s computer announced the birth of an entirely new mode of textuality. The Z3, the world’s first working programmable, fully automatic computer, arrived in 1941. (Manovich 2002) A year earlier a young graduate by the name of Claude Shannon had published one of the most important Masters theses in history. In it he demonstrated that any logical expression of Boolean algebra could be programmed into a series of binary switches. Today computers still function with a logic impossible to distinguish from their mid-20th century ancestors. What has changed is the material environment within which Boolean expressions are implemented. Shannon’s work first found itself manifest in the fragile rows of vacuum tubes that drove much of the technical innovation of the 40s and 50s. In time, the very same Boolean expressions were firing, domino-like, through millions of transistors etched onto the surface of silicon chips. If we were to query the young Shannon today, he might well gawp in amazement at the material advances computer technology has gone through. But, if Shannon was to examine either your digital wrist watch or the world’s most advanced supercomputer in detail, he would once again feel at home in the simple binary – on/off – switches lining those silicon highways. Here the difference between how computers are implemented and what computers are made of digs the first of many potholes along our journey. We live in an era not only practically driven by the computer, but an era increasingly determined by the metaphors computers have injected into our language. Let us not make the mistake of presupposing that brains (or perhaps minds) are “like” computers. Tempting though it is to reduce the baffling complexities of the human being to the functions of the silicon chip, the parallel processor or Wide Area Network this reduction occurs most usefully at the level of metaphor and metonym. Again the mantra must be repeated that computers function through the application of Boolean logic and binary switches, something that can not be said about the human brain with any confidence a posteriori. Later I will explore the consequences on our own understanding of ourselves enabled by the processing paradigm, but for now, or at least the next few paragraphs, computers are to be considered in terms of their rigid implementation and flexible materiality alone. At the beginning of his popular science book, The Pattern on the Stone, (Hillis 1999) W.  Daniel Hillis narrates one of his many tales on the design and construction of a computer. Built from tinker-toys the computer in question was/is functionally complex enough to “play” tic-tac-toe (noughts and crosses). The tinker-toy was chosen to indicate the apparent simplicity of computer design, but as Hillis argues himself, he may very well have used pipes and valves to create a hydraulic computer, driven by water pressure, or stripped the design back completely, using flowing sand, twigs and twine or any other recipe of switches and connectors. The important point is that the tinker-toy tic-tac-toe computer functions perfectly well for the task it is designed for, perfectly well, that is, until the tinker-toy material begins to fail. This failure is what Chapter 1 of this thesis is about: why it happens, why its happening is a material phenomenon and how the very idea of “failure” is suspect. Tinker-toys fail because the mechanical operation of the tic-tac-toe computer puts strain on the strings of the mechanism, eventually stretching them beyond practical use. In a perfect world, devoid of entropic behaviour, the tinker-toy computer may very well function forever, its users setting O or X conditions, and the computer responding according to its program in perfect, logical order. The design of the machine, at the level of the program, is completely closed; finished; perfect. Only materially does the computer fail (or flail), noise leaking into the system until inevitable chaos ensues and the tinker-toys crumble back into jumbles of featureless matter. This apparent closure is important to note at this stage because in a computer as simple as the tic-tac-toe machine, every variable can be accounted for and thus programmed for. Were we to build a chess playing computer from tinker-toys (pretending we could get our hands on the, no doubt, millions of tinker-toy sets we”d need) the closed condition of the computer may be less simple to qualify. Tinker-toys, hydraulic valves or whatever material you choose, could be manipulated into any computer system you can imagine, even the most brain numbingly complicated IBM supercomputer is technically possible to build from these fundamental materials. The reason we don”t do this, why we instead choose etched silicon as our material of choice for our supercomputers, exposes another aspect of computers we need to understand before their failure becomes a useful paradigm. A chess playing computer is probably impossible to build from tinker-toys, not because its program would be too complicated, but because tinker-toys are too prone to entropy to create a valid material environment. The program of any chess playing application could, theoretically, be translated into a tinker-toy equivalent, but after the 1,000th string had stretched, with millions more to go, no energy would be left in the system to trigger the next switch along the chain. Computer inputs and outputs are always at the mercy of this kind of entropy: whether in tinker-toys or miniature silicon highways. Noise and dissipation are inevitable at any material scale one cares to examine. The second law of thermo dynamics ensures this. Claude Shannon and his ilk knew this, even back when the most advanced computers they had at their command couldn”t yet play tic-tac-toe. They knew that they couldn”t rely on materiality to delimit noise, interference or distortion; that no matter how well constructed a computer is, no matter how incredible it was at materially stemming entropy (perhaps with stronger string connectors, or a built in de-stretching mechanism), entropy nonetheless was inevitable. But what Shannon and other computer innovators such as Alan Turing also knew, is that their saviour lay in how computers were implemented. Again, the split here is incredibly important to note:

Flexible materiality: How and of what a computer is constructed e.g. tinker-toys, silicon Rigid implementation: Boolean logic enacted through binary on/off switches (usually with some kind of input à storage à feedback/program function à output). Effectively, how a computer works

Boolean logic was not enough on its own. Computers, if they were to avoid entropy ruining their logical operations, needed to have built within them an error management protocol. This protocol is still in existence in EVERY computer in the world. Effectively it takes the form of a collection of parity bits delivered alongside each packet of data that computers, networks and software deal with. The bulk of data contains the binary bits encoding the intended quarry, but the receiving element in the system also checks the main bits alongside the parity bits to determine whether any noise has crept into the system. What is crucial to note here is the error-checking of computers happens at the level of their rigid implementation. It is also worth noting that for every eight 0s and 1s delivered by a computer system, at least one of those bits is an error checking function. W. Daniel Hillis puts the stretched strings of his tinker-toy mechanism into clear distinction and in doing so, re-introduces an umbrella term set to dominate this chapter: I constructed a later version of the Tinker Toy computer which fixed the problem, but I never forgot the lesson of the first machine: the implementation technology must produce perfect outputs from imperfect inputs, nipping small errors in the bud. This is the essence of digital technology, which restores signals to near perfection at every stage. It is the only way we know – at least, so far – for keeping a complicated system under control. (Hillis 1999, 18)   Bibliography  Barthes, Roland. 1979. ‘From Work to Text.’ In Textual Strategies: Perspectives in Poststructuralist Criticism, ed. Josue V. Harari, 73–81. Ithaca, NY: Cornell University Press. Hayles, N. Katherine. 2004. ‘Print Is Flat, Code Is Deep: The Importance of Media-Specific Analysis.’ Poetics Today 25 (1) (March): 67–90. doi:10.1215/03335372-25-1-67. Hillis, W. 1999. The Pattern on the Stone : the Simple Ideas That Make Computers Work. 1st paperback ed. New York: Basic Books. Manovich, Lev. 2002. The Language of New Media. 1st MIT Press pbk. ed. Cambridge  Mass.: MIT Press.      

Grandmasters of comparable skill now come to championship games with computer-generated analysis of their opponents’ opening lines and likely moves. Home preparation has always been important, but computers have made it much more so and have thereby changed the nature of the game. Now risky plays are almost inevitably punished because they’ve been anticipated, making Anand’s play in Game 8 of the recent championship a rare exception.

Computers don’t play chess perfectly — the game is far too complicated for that — but they play in a way that’s more exciting and more decisive. They also play better than humans. Which is why since chess is no longer about just two humans facing each other anyway — thanks to pre-game computer-assisted preparation — it makes sense to allow the use of computers during competitive games. (Of course, for the sake of fairness, the two players would have equal access to the same computer engine.)

Cephalopods

When you visit the History of Computer Chess exhibit at the Computer History Museum in Mountain View, California, the first machine you see is "The Turk."

In 1770, a Hungarian engineer and diplomat named Wolfgang von Kempelen presented a remarkable invention to the court of Maria Theresa, ruler of Hungary and Austria. It consisted of a mechanical figure dressed in (what Europeans saw as) Oriental garb, presiding over a cabinet upon which a chess board sat. Full of gears ostentatiously placed in a front side drawer, The Turk was cranked up by hand, after which an opponent could sit down and play a game against the dummy.

"Even among the skeptics who insisted it was a trick, there was disagreement about how the automaton worked, leading to a series of claims and counterclaims," writes author Tom Standage. "Did it rely on mechanical trickery, magnetism, or sleight of hand? Was there a dwarf, or a small child, or a legless man hidden inside it?"

In 1985, in Hamburg, I played against thirty-two different chess computers at the same time in what is known as a simultaneous exhibition. I walked from one machine to the next, making my moves over a period of more than five hours. The four leading chess computer manufacturers had sent their top models, including eight named after me from the electronics firm Saitek.

It illustrates the state of computer chess at the time that it didn’t come as much of a surprise when I achieved a perfect 32–0 score, winning every game, although there was an uncomfortable moment. At one point I realized that I was drifting into trouble in a game against one of the “Kasparov” brand models. If this machine scored a win or even a draw, people would be quick to say that I had thrown the game to get PR for the company, so I had to intensify my efforts. Eventually I found a way to trick the machine with a sacrifice it should have refused.