“another winter day, has come and gone away, in Paris and Rome, but I wanna go home….”



Hello world, I’m in Paris right now and I use my sleeping time to do this journal because it’s still 12 pm here and 6 am in the morning in Indonesia. This is both my November and December journal I forgot that I haven’t submitted the November one yet, so this is it. So, happy holiday J *sarcasm*

November Journal

In this journal we should put our subject as the topic and the main idea to think and I choose this subject because I hate it so much. Math. Why do we have to study math in our school? Like, what the use of this subject actually? I have been thinking of this for my whole life. Why we have to use our logic? When there are lots of thing can be happen spontaneously without thinking. Every school in this world and subject sometimes use math too to count. Now after I have done some research on this one. I knew that we do have to study math to find the exact answer and to train our logic. Using math, we can easily give some exercise to test our logic. For example when you do functions in your math class, it was actually to test your brain to know how to make machine and stuff when you got in to machinery later for our university. Some functions in math usually are mixed with our other topic that we have learned before. Why that is happen? Because it can test our logic whether you understand the last topic or you just memorize the topic instead of understanding it. And why some people do love math and some don’t? It is actually just the way our thinking or our mind gives the perception about math itself. When you do love math it means you are the type of person that really loves to use your logic. Like those inventor of telephone and another technologies, they should love math first before they can make new brilliant invention because there are tons of math topics they should use to create one invention and the rest are creative ideas for the designs. And for some people who didn’t love math, it is because their mind and emotion combine become one and believe that “I hate math” so yes, then you become the person that doesn’t like math. When you actually understand the idea and the use of the theory of math you would love the ‘beauty’ of math. I’m not the type of person of both sides because deep down there I actually love math but sometimes I’m too lazy to do the calculation and stuff. Actually everybody can do math as long as they have motivation and really want to know and understand more about math. Genius people are everywhere but they don’t use it well usually those people who got good score are those people with lower logic skill but they have high motivation and they really do want to learn more and being a curious person that always want to know more about new theories and stuff. For those people who were born to be a genius but didn’t use it well, they can grow their motivation and get good score too with less struggle but they have to be diligent and keep learning more about the new topic. Thanks.

December Journal

For this journal I choose “The Chess Master and The Computer” story. The story was talking about artificial intelligence. The real life problem in here is people nowadays are misunderstood about the meaning or the use of artificial intelligence such as computer. Computer and its software are only some of the example of thousands or even billions of another artificial intelligence. How do people can believe and trust artificial intelligence such as computer to teach them a new theory than an earlier age human? As I said before humans nowadays are misunderstood about the using of artificial intelligence such as computer. Like in the story, the author told us about the chess playing game using a computer machine, there is this genius that can play all the chess playing game in machine and people are amazed with that. But for me myself I do amazed with that but unlike this person who invented this machine called “Deep Blue” that this master chess cannot beat, he was obsessed to make a new machine and use so many kinds of formulas and thousands of steps to beat that guy. I think he should have made a new machine and new ideas, its better. Because of the inventor of Deep Blue, the chess master got in to the headline in every newspaper in town and people start to talk about it. Since then, people undoubtedly start to learn something new from computer software and websites until now. So many people invented new software to teach small kids with knowledge and with fun way. For me it’s good for them because it way more simpler than their parents have to teach them all of the things that they should learn but the bad influence for this children are they will get addicted to technologies and it is very hard to keep them away from it and also they do make people get easier to catch up the newest news easily and talk freely in the internet world but what about the society around them? Would there be any socialize between persons? I watched this film called “Wall-e” it gives a good moral for me because in the story the people were using walking chair as the substitute for their feet and using this computer to communicate with others and those people became fatter and fatter and when the technologies were dead they can’t walk properly because they were too big and it’s all because they were to addicted to the technologies. From the movies I got a good moral that we should not use technologies to much because it’s bad influences for all of us but right now? People are get more and more addicted to technologies. Yes, they still walk, yes; they still talk with each other. But as the time keep walking who will know what will happen next after this? Me myself also already addicted to my Blackberry and I know it’s not a good thing but when people get addicted it’s really hard to stop until they realize it is DANGEROUS for them. So back to the knowledge issue I mentioned above, how do people can believe and trust artificial intelligence such as computer to teach them a new theory than an earlier age human, there is no exact answer for this question because it is depends on human believes and perception because once they’re believe and addicted it’s very hard for them to get away from the things they believe.

“The Chess Master and The Computer”

by Garry Kasparov

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. From the human perspective, or at least from my perspective, those were the good old days of man vs. machine chess.

Eleven years later I narrowly defeated the supercomputer Deep Blue in a match. Then, in 1997, IBM redoubled its efforts—and doubled Deep Blue’s processing power—and I lost the rematch in an event that made headlines around the world. The result was met with astonishment and grief by those who took it as a symbol of mankind’s submission before the almighty computer. (“The Brain’s Last Stand” read the Newsweek headline.) Others shrugged their shoulders, surprised that humans could still compete at all against the enormous calculating power that, by 1997, sat on just about every desk in the first world.

It was the specialists—the chess players and the programmers and the artificial intelligence enthusiasts—who had a more nuanced appreciation of the result. Grandmasters had already begun to see the implications of the existence of machines that could play—if only, at this point, in a select few types of board configurations—with godlike perfection. The computer chess people were delighted with the conquest of one of the earliest and holiest grails of computer science, in many cases matching the mainstream media’s hyperbole. The 2003 book Deep Blue by Monty Newborn was blurbed as follows: “a rare, pivotal watershed beyond all other triumphs: Orville Wright’s first flight, NASA’s landing on the moon….”

The AI crowd, too, was pleased with the result and the attention, but dismayed by the fact that Deep Blue was hardly what their predecessors had imagined decades earlier when they dreamed of creating a machine to defeat the world chess champion. Instead of a computer that thought and played chess like a human, with human creativity and intuition, they got one that played like a machine, systematically evaluating 200 million possible moves on the chess board per second and winning with brute number-crunching force. As Igor Aleksander, a British AI and neural networks pioneer, explained in his 2000 book, How to Build a Mind:

By the mid-1990s the number of people with some experience of using computers was many orders of magnitude greater than in the 1960s. In the Kasparov defeat they recognized that here was a great triumph for programmers, but not one that may compete with the human intelligence that helps us to lead our lives.

It was an impressive achievement, of course, and a human achievement by the members of the IBM team, but Deep Blue was only intelligent the way your programmable alarm clock is intelligent. Not that losing to a $10 million alarm clock made me feel any better.

My hopes for a return match with Deep Blue were dashed, unfortunately. IBM had the publicity it wanted and quickly shut down the project. Other chess computing projects around the world also lost their sponsorship. Though I would have liked my chances in a rematch in 1998 if I were better prepared, it was clear then that computer superiority over humans in chess had always been just a matter of time. Today, for $50 you can buy a home PC program that will crush most grandmasters. In 2003, I played serious matches against two of these programs running on commercially available multiprocessor servers—and, of course, I was playing just one game at a time—and in both cases the score ended in a tie with a win apiece and several draws.

Inevitable or not, no one understood all the ramifications of having a super-grandmaster on your laptop, especially what this would mean for professional chess. There were many doomsday scenarios about people losing interest in chess with the rise of the machines, especially after my loss to Deep Blue. Some replied to this with variations on the theme of how we still hold footraces despite cars and bicycles going much faster, a spurious analogy since cars do not help humans run faster while chess computers undoubtedly have an effect on the quality of human chess.

Another group postulated that the game would be solved, i.e., a mathematically conclusive way for a computer to win from the start would be found. (Or perhaps it would prove that a game of chess played in the best possible way always ends in a draw.) Perhaps a real version of HAL 9000 would simply announce move 1.e4, with checkmate in, say, 38,484 moves. These gloomy predictions have not come true, nor will they ever come to pass. Chess is far too complex to be definitively solved with any technology we can conceive of today. However, our looked-down-upon cousin, checkers, or draughts, suffered this fate quite recently thanks to the work of Jonathan Schaeffer at the University of Alberta and his unbeatable program Chinook.

The number of legal chess positions is 1040, the number of different possible games, 10120. Authors have attempted various ways to convey this immensity, usually based on one of the few fields to regularly employ such exponents, astronomy. In his book Chess Metaphors, Diego Rasskin-Gutman points out that a player looking eight moves ahead is already presented with as many possible games as there are stars in the galaxy. Another staple, a variation of which is also used by Rasskin-Gutman, is to say there are more possible chess games than the number of atoms in the universe. All of these comparisons impress upon the casual observer why brute-force computer calculation can’t solve this ancient board game. They are also handy, and I am not above doing this myself, for impressing people with how complicated chess is, if only in a largely irrelevant mathematical way.

This astronomical scale is not at all irrelevant to chess programmers. They’ve known from the beginning that solving the game—creating a provably unbeatable program—was not possible with the computer power available, and that effective shortcuts would have to be found. In fact, the first chess program put into practice was designed by legendary British mathematician Alan Turing in 1952, and he didn’t even have a computer! He processed the algorithm on pieces of paper and this “paper machine” played a competent game.

Rasskin-Gutman covers this well-traveled territory in a book that achieves its goal of being an overview of overviews, if little else. The history of the study of brain function is covered in the first chapter, tempting the reader to skip ahead. You might recall axons and dendrites from high school biology class. We also learn about cholinergic and aminergic systems and many other things that are not found by my computer’s artificially intelligent English spell-checking system—or referenced again by the author. Then it’s on to similarly concise, if inconclusive, surveys of artificial intelligence, chess computers, and how humans play chess.

There have been many unintended consequences, both positive and negative, of the rapid proliferation of powerful chess software. Kids love computers and take to them naturally, so it’s no surprise that the same is true of the combination of chess and computers. With the introduction of super-powerful software it became possible for a youngster to have a top- level opponent at home instead of needing a professional trainer from an early age. Countries with little by way of chess tradition and few available coaches can now produce prodigies. I am in fact coaching one of them this year, nineteen-year-old Magnus Carlsen, from Norway, where relatively little chess is played.

The heavy use of computer analysis has pushed the game itself in new directions. The machine doesn’t care about style or patterns or hundreds of years of established theory. It counts up the values of the chess pieces, analyzes a few billion moves, and counts them up again. (A computer translates each piece and each positional factor into a value in order to reduce the game to numbers it can crunch.) It is entirely free of prejudice and doctrine and this has contributed to the development of players who are almost as free of dogma as the machines with which they train. Increasingly, a move isn’t good or bad because it looks that way or because it hasn’t been done that way before. It’s simply good if it works and bad if it doesn’t. Although we still require a strong measure of intuition and logic to play well, humans today are starting to play more like computers.

The availability of millions of games at one’s fingertips in a database is also making the game’s best players younger and younger. Absorbing the thousands of essential patterns and opening moves used to take many years, a process indicative of Malcolm Gladwell’s “10,000 hours to become an expert” theory as expounded in his recent book Outliers. (Gladwell’s earlier book, Blink, rehashed, if more creatively, much of the cognitive psychology material that is re-rehashed in Chess Metaphors.) Today’s teens, and increasingly pre-teens, can accelerate this process by plugging into a digitized archive of chess information and making full use of the superiority of the young mind to retain it all. In the pre-computer era, teenage grandmasters were rarities and almost always destined to play for the world championship. Bobby Fischer’s 1958 record of attaining the grandmaster title at fifteen was broken only in 1991. It has been broken twenty times since then, with the current record holder, Ukrainian Sergey Karjakin, having claimed the highest title at the nearly absurd age of twelve in 2002. Now twenty, Karjakin is among the world’s best, but like most of his modern wunderkind peers he’s no Fischer, who stood out head and shoulders above his peers—and soon enough above the rest of the chess world as well.

Excelling at chess has long been considered a symbol of more general intelligence. That is an incorrect assumption in my view, as pleasant as it might be. But for the purposes of argument and investigation, chess is, in Russkin-Gutman’s words, “an unparalleled laboratory, since both the learning process and the degree of ability obtained can be objectified and quantified, providing an excellent comparative framework on which to use rigorous analytical techniques.”

Here I agree wholeheartedly, if for different reasons. I am much more interested in using the chess laboratory to illuminate the workings of the human mind, not the artificial mind. As I put it in my 2007 book, How Life Imitates Chess, “Chess is a unique cognitive nexus, a place where art and science come together in the human mind and are then refined and improved by experience.” Coincidentally the section in which that phrase appears is titled “More than a metaphor.” It makes the case for using the decision-making process of chess as a model for understanding and improving our decision-making everywhere else.

This is not to say that I am not interested in the quest for intelligent machines. My many exhibitions with chess computers stemmed from a desire to participate in this grand experiment. It was my luck (perhaps my bad luck) to be the world chess champion during the critical years in which computers challenged, then surpassed, human chess players. Before 1994 and after 2004 these duels held little interest. The computers quickly went from too weak to too strong. But for a span of ten years these contests were fascinating clashes between the computational power of the machines (and, lest we forget, the human wisdom of their programmers) and the intuition and knowledge of the grandmaster.

In what Rasskin-Gutman explains as Moravec’s Paradox, in chess, as in so many things, what computers are good at is where humans are weak, and vice versa. This gave me an idea for an experiment. What if instead of human versus machine we played as partners? My brainchild saw the light of day in a match in 1998 in León, Spain, and we called it “Advanced Chess.” Each player had a PC at hand running the chess software of his choice during the game. The idea was to create the highest level of chess ever played, a synthesis of the best of man and machine.

Although I had prepared for the unusual format, my match against the Bulgarian Veselin Topalov, until recently the world’s number one ranked player, was full of strange sensations. Having a computer program available during play was as disturbing as it was exciting. And being able to access a database of a few million games meant that we didn’t have to strain our memories nearly as much in the opening, whose possibilities have been thoroughly catalogued over the years. But since we both had equal access to the same database, the advantage still came down to creating a new idea at some point.

Having a computer partner also meant never having to worry about making a tactical blunder. The computer could project the consequences of each move we considered, pointing out possible outcomes and countermoves we might otherwise have missed. With that taken care of for us, we could concentrate on strategic planning instead of spending so much time on calculations. Human creativity was even more paramount under these conditions. Despite access to the “best of both worlds,” my games with Topalov were far from perfect. We were playing on the clock and had little time to consult with our silicon assistants. Still, the results were notable. A month earlier I had defeated the Bulgarian in a match of “regular” rapid chess 4–0. Our advanced chess match ended in a 3–3 draw. My advantage in calculating tactics had been nullified by the machine.

This experiment goes unmentioned by Russkin-Gutman, a major omission since it relates so closely to his subject. Even more notable was how the advanced chess experiment continued. In 2005, the online chess-playing site Playchess.com hosted what it called a “freestyle” chess tournament in which anyone could compete in teams with other players or computers. Normally, “anti-cheating” algorithms are employed by online sites to prevent, or at least discourage, players from cheating with computer assistance. (I wonder if these detection algorithms, which employ diagnostic analysis of moves and calculate probabilities, are any less “intelligent” than the playing programs they detect.)

Lured by the substantial prize money, several groups of strong grandmasters working with several computers at the same time entered the competition. At first, the results seemed predictable. The teams of human plus machine dominated even the strongest computers. The chess machine Hydra, which is a chess-specific supercomputer like Deep Blue, was no match for a strong human player using a relatively weak laptop. Human strategic guidance combined with the tactical acuity of a computer was overwhelming.

The surprise came at the conclusion of the event. The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.

The “freestyle” result, though startling, fits with my belief that talent is a misused term and a misunderstood concept. The moment I became the youngest world chess champion in history at the age of twenty-two in 1985, I began receiving endless questions about the secret of my success and the nature of my talent. Instead of asking about Sicilian Defenses, journalists wanted to know about my diet, my personal life, how many moves ahead I saw, and how many games I held in my memory.

I soon realized that my answers were disappointing. I didn’t eat anything special. I worked hard because my mother had taught me to. My memory was good, but hardly photographic. As for how many moves ahead a grandmaster sees, Russkin-Gutman makes much of the answer attributed to the great Cuban world champion José Raúl Capablanca, among others: “Just one, the best one.” This answer is as good or bad as any other, a pithy way of disposing with an attempt by an outsider to ask something insightful and failing to do so. It’s the equivalent of asking Lance Armstrong how many times he shifts gears during the Tour de France.

The only real answer, “It depends on the position and how much time I have,” is unsatisfying. In what may have been my best tournament game at the 1999 Hoogovens tournament in the Netherlands, I visualized the winning position a full fifteen moves ahead—an unusual feat. I sacrificed a great deal of material for an attack, burning my bridges; if my calculations were faulty I would be dead lost. Although my intuition was correct and my opponent, Topalov again, failed to find the best defense under pressure, subsequent analysis showed that despite my Herculean effort I had missed a shorter route to victory. Capablanca’s sarcasm aside, correctly evaluating a small handful of moves is far more important in human chess, and human decision-making in general, than the systematically deeper and deeper search for better moves—the number of moves “seen ahead”—that computers rely on.

There is little doubt that different people are blessed with different amounts of cognitive gifts such as long-term memory and the visuospatial skills chess players are said to employ. One of the reasons chess is an “unparalleled laboratory” and a “unique nexus” is that it demands high performance from so many of the brain’s functions. Where so many of these investigations fail on a practical level is by not recognizing the importance of the process of learning and playing chess. The ability to work hard for days on end without losing focus is a talent. The ability to keep absorbing new information after many hours of study is a talent. Programming yourself by analyzing your decision-making outcomes and processes can improve results much the way that a smarter chess algorithm will play better than another running on the same computer. We might not be able to change our hardware, but we can definitely upgrade our software.

With the supremacy of the chess machines now apparent and the contest of “Man vs. Machine” a thing of the past, perhaps it is time to return to the goals that made computer chess so attractive to many of the finest minds of the twentieth century. Playing better chess was a problem they wanted to solve, yes, and it has been solved. But there were other goals as well: to develop a program that played chess by thinking like a human, perhaps even by learning the game as a human does. Surely this would be a far more fruitful avenue of investigation than creating, as we are doing, ever-faster algorithms to run on ever-faster hardware.

This is our last chess metaphor, then—a metaphor for how we have discarded innovation and creativity in exchange for a steady supply of marketable products. The dreams of creating an artificial intelligence that would engage in an ancient game symbolic of human thought have been abandoned. Instead, every year we have new chess programs, and new versions of old ones, that are all based on the same basic programming concepts for picking a move by searching through millions of possibilities that were developed in the 1960s and 1970s.

Like so much else in our technology-rich and innovation-poor modern world, chess computing has fallen prey to incrementalism and the demands of the market. Brute-force programs play the best chess, so why bother with anything else? Why waste time and money experimenting with new and innovative ideas when we already know what works? Such thinking should horrify anyone worthy of the name of scientist, but it seems, tragically, to be the norm. Our best minds have gone into financial engineering instead of real engineering, with catastrophic results for both sectors.

Perhaps chess is the wrong game for the times. Poker is now everywhere, as amateurs dream of winning millions and being on television for playing a card game whose complexities can be detailed on a single piece of paper. But while chess is a 100 percent information game—both players are aware of all the data all the time—and therefore directly susceptible to computing power, poker has hidden cards and variable stakes, creating critical roles for chance, bluffing, and risk management.

These might seem to be aspects of poker based entirely on human psychology and therefore invulnerable to computer incursion. A machine can trivially calculate the odds of every hand, but what to make of an opponent with poor odds making a large bet? And yet the computers are advancing here as well. Jonathan Schaeffer, the inventor of the checkers-solving program, has moved on to poker and his digital players are performing better and better against strong humans—with obvious implications for online gambling sites.

Perhaps the current trend of many chess professionals taking up the more lucrative pastime of poker is not a wholly negative one. It may not be too late for humans to relearn how to take risks in order to innovate and thereby maintain the advanced lifestyles we enjoy. And if it takes a poker-playing supercomputer to remind us that we can’t enjoy the rewards without taking the risks, so be 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. From the human perspective, or at least from my perspective, those were the good old days of man vs. machine chess.

Eleven years later I narrowly defeated the supercomputer Deep Blue in a match. Then, in 1997, IBM redoubled its efforts—and doubled Deep Blue’s processing power—and I lost the rematch in an event that made headlines around the world. The result was met with astonishment and grief by those who took it as a symbol of mankind’s submission before the almighty computer. (“The Brain’s Last Stand” read the Newsweek headline.) Others shrugged their shoulders, surprised that humans could still compete at all against the enormous calculating power that, by 1997, sat on just about every desk in the first world.

It was the specialists—the chess players and the programmers and the artificial intelligence enthusiasts—who had a more nuanced appreciation of the result. Grandmasters had already begun to see the implications of the existence of machines that could play—if only, at this point, in a select few types of board configurations—with godlike perfection. The computer chess people were delighted with the conquest of one of the earliest and holiest grails of computer science, in many cases matching the mainstream media’s hyperbole. The 2003 book Deep Blue by Monty Newborn was blurbed as follows: “a rare, pivotal watershed beyond all other triumphs: Orville Wright’s first flight, NASA’s landing on the moon….”

The AI crowd, too, was pleased with the result and the attention, but dismayed by the fact that Deep Blue was hardly what their predecessors had imagined decades earlier when they dreamed of creating a machine to defeat the world chess champion. Instead of a computer that thought and played chess like a human, with human creativity and intuition, they got one that played like a machine, systematically evaluating 200 million possible moves on the chess board per second and winning with brute number-crunching force. As Igor Aleksander, a British AI and neural networks pioneer, explained in his 2000 book, How to Build a Mind:

By the mid-1990s the number of people with some experience of using computers was many orders of magnitude greater than in the 1960s. In the Kasparov defeat they recognized that here was a great triumph for programmers, but not one that may compete with the human intelligence that helps us to lead our lives.

It was an impressive achievement, of course, and a human achievement by the members of the IBM team, but Deep Blue was only intelligent the way your programmable alarm clock is intelligent. Not that losing to a $10 million alarm clock made me feel any better.

My hopes for a return match with Deep Blue were dashed, unfortunately. IBM had the publicity it wanted and quickly shut down the project. Other chess computing projects around the world also lost their sponsorship. Though I would have liked my chances in a rematch in 1998 if I were better prepared, it was clear then that computer superiority over humans in chess had always been just a matter of time. Today, for $50 you can buy a home PC program that will crush most grandmasters. In 2003, I played serious matches against two of these programs running on commercially available multiprocessor servers—and, of course, I was playing just one game at a time—and in both cases the score ended in a tie with a win apiece and several draws.

Inevitable or not, no one understood all the ramifications of having a super-grandmaster on your laptop, especially what this would mean for professional chess. There were many doomsday scenarios about people losing interest in chess with the rise of the machines, especially after my loss to Deep Blue. Some replied to this with variations on the theme of how we still hold footraces despite cars and bicycles going much faster, a spurious analogy since cars do not help humans run faster while chess computers undoubtedly have an effect on the quality of human chess.

Another group postulated that the game would be solved, i.e., a mathematically conclusive way for a computer to win from the start would be found. (Or perhaps it would prove that a game of chess played in the best possible way always ends in a draw.) Perhaps a real version of HAL 9000 would simply announce move 1.e4, with checkmate in, say, 38,484 moves. These gloomy predictions have not come true, nor will they ever come to pass. Chess is far too complex to be definitively solved with any technology we can conceive of today. However, our looked-down-upon cousin, checkers, or draughts, suffered this fate quite recently thanks to the work of Jonathan Schaeffer at the University of Alberta and his unbeatable program Chinook.

The number of legal chess positions is 1040, the number of different possible games, 10120. Authors have attempted various ways to convey this immensity, usually based on one of the few fields to regularly employ such exponents, astronomy. In his book Chess Metaphors, Diego Rasskin-Gutman points out that a player looking eight moves ahead is already presented with as many possible games as there are stars in the galaxy. Another staple, a variation of which is also used by Rasskin-Gutman, is to say there are more possible chess games than the number of atoms in the universe. All of these comparisons impress upon the casual observer why brute-force computer calculation can’t solve this ancient board game. They are also handy, and I am not above doing this myself, for impressing people with how complicated chess is, if only in a largely irrelevant mathematical way.

This astronomical scale is not at all irrelevant to chess programmers. They’ve known from the beginning that solving the game—creating a provably unbeatable program—was not possible with the computer power available, and that effective shortcuts would have to be found. In fact, the first chess program put into practice was designed by legendary British mathematician Alan Turing in 1952, and he didn’t even have a computer! He processed the algorithm on pieces of paper and this “paper machine” played a competent game.

Rasskin-Gutman covers this well-traveled territory in a book that achieves its goal of being an overview of overviews, if little else. The history of the study of brain function is covered in the first chapter, tempting the reader to skip ahead. You might recall axons and dendrites from high school biology class. We also learn about cholinergic and aminergic systems and many other things that are not found by my computer’s artificially intelligent English spell-checking system—or referenced again by the author. Then it’s on to similarly concise, if inconclusive, surveys of artificial intelligence, chess computers, and how humans play chess.

There have been many unintended consequences, both positive and negative, of the rapid proliferation of powerful chess software. Kids love computers and take to them naturally, so it’s no surprise that the same is true of the combination of chess and computers. With the introduction of super-powerful software it became possible for a youngster to have a top- level opponent at home instead of needing a professional trainer from an early age. Countries with little by way of chess tradition and few available coaches can now produce prodigies. I am in fact coaching one of them this year, nineteen-year-old Magnus Carlsen, from Norway, where relatively little chess is played.

The heavy use of computer analysis has pushed the game itself in new directions. The machine doesn’t care about style or patterns or hundreds of years of established theory. It counts up the values of the chess pieces, analyzes a few billion moves, and counts them up again. (A computer translates each piece and each positional factor into a value in order to reduce the game to numbers it can crunch.) It is entirely free of prejudice and doctrine and this has contributed to the development of players who are almost as free of dogma as the machines with which they train. Increasingly, a move isn’t good or bad because it looks that way or because it hasn’t been done that way before. It’s simply good if it works and bad if it doesn’t. Although we still require a strong measure of intuition and logic to play well, humans today are starting to play more like computers.

The availability of millions of games at one’s fingertips in a database is also making the game’s best players younger and younger. Absorbing the thousands of essential patterns and opening moves used to take many years, a process indicative of Malcolm Gladwell’s “10,000 hours to become an expert” theory as expounded in his recent book Outliers. (Gladwell’s earlier book, Blink, rehashed, if more creatively, much of the cognitive psychology material that is re-rehashed in Chess Metaphors.) Today’s teens, and increasingly pre-teens, can accelerate this process by plugging into a digitized archive of chess information and making full use of the superiority of the young mind to retain it all. In the pre-computer era, teenage grandmasters were rarities and almost always destined to play for the world championship. Bobby Fischer’s 1958 record of attaining the grandmaster title at fifteen was broken only in 1991. It has been broken twenty times since then, with the current record holder, Ukrainian Sergey Karjakin, having claimed the highest title at the nearly absurd age of twelve in 2002. Now twenty, Karjakin is among the world’s best, but like most of his modern wunderkind peers he’s no Fischer, who stood out head and shoulders above his peers—and soon enough above the rest of the chess world as well.

Excelling at chess has long been considered a symbol of more general intelligence. That is an incorrect assumption in my view, as pleasant as it might be. But for the purposes of argument and investigation, chess is, in Russkin-Gutman’s words, “an unparalleled laboratory, since both the learning process and the degree of ability obtained can be objectified and quantified, providing an excellent comparative framework on which to use rigorous analytical techniques.”

Here I agree wholeheartedly, if for different reasons. I am much more interested in using the chess laboratory to illuminate the workings of the human mind, not the artificial mind. As I put it in my 2007 book, How Life Imitates Chess, “Chess is a unique cognitive nexus, a place where art and science come together in the human mind and are then refined and improved by experience.” Coincidentally the section in which that phrase appears is titled “More than a metaphor.” It makes the case for using the decision-making process of chess as a model for understanding and improving our decision-making everywhere else.

This is not to say that I am not interested in the quest for intelligent machines. My many exhibitions with chess computers stemmed from a desire to participate in this grand experiment. It was my luck (perhaps my bad luck) to be the world chess champion during the critical years in which computers challenged, then surpassed, human chess players. Before 1994 and after 2004 these duels held little interest. The computers quickly went from too weak to too strong. But for a span of ten years these contests were fascinating clashes between the computational power of the machines (and, lest we forget, the human wisdom of their programmers) and the intuition and knowledge of the grandmaster.

In what Rasskin-Gutman explains as Moravec’s Paradox, in chess, as in so many things, what computers are good at is where humans are weak, and vice versa. This gave me an idea for an experiment. What if instead of human versus machine we played as partners? My brainchild saw the light of day in a match in 1998 in León, Spain, and we called it “Advanced Chess.” Each player had a PC at hand running the chess software of his choice during the game. The idea was to create the highest level of chess ever played, a synthesis of the best of man and machine.

Although I had prepared for the unusual format, my match against the Bulgarian Veselin Topalov, until recently the world’s number one ranked player, was full of strange sensations. Having a computer program available during play was as disturbing as it was exciting. And being able to access a database of a few million games meant that we didn’t have to strain our memories nearly as much in the opening, whose possibilities have been thoroughly catalogued over the years. But since we both had equal access to the same database, the advantage still came down to creating a new idea at some point.

Having a computer partner also meant never having to worry about making a tactical blunder. The computer could project the consequences of each move we considered, pointing out possible outcomes and countermoves we might otherwise have missed. With that taken care of for us, we could concentrate on strategic planning instead of spending so much time on calculations. Human creativity was even more paramount under these conditions. Despite access to the “best of both worlds,” my games with Topalov were far from perfect. We were playing on the clock and had little time to consult with our silicon assistants. Still, the results were notable. A month earlier I had defeated the Bulgarian in a match of “regular” rapid chess 4–0. Our advanced chess match ended in a 3–3 draw. My advantage in calculating tactics had been nullified by the machine.

This experiment goes unmentioned by Russkin-Gutman, a major omission since it relates so closely to his subject. Even more notable was how the advanced chess experiment continued. In 2005, the online chess-playing site Playchess.com hosted what it called a “freestyle” chess tournament in which anyone could compete in teams with other players or computers. Normally, “anti-cheating” algorithms are employed by online sites to prevent, or at least discourage, players from cheating with computer assistance. (I wonder if these detection algorithms, which employ diagnostic analysis of moves and calculate probabilities, are any less “intelligent” than the playing programs they detect.)

Lured by the substantial prize money, several groups of strong grandmasters working with several computers at the same time entered the competition. At first, the results seemed predictable. The teams of human plus machine dominated even the strongest computers. The chess machine Hydra, which is a chess-specific supercomputer like Deep Blue, was no match for a strong human player using a relatively weak laptop. Human strategic guidance combined with the tactical acuity of a computer was overwhelming.

The surprise came at the conclusion of the event. The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.

The “freestyle” result, though startling, fits with my belief that talent is a misused term and a misunderstood concept. The moment I became the youngest world chess champion in history at the age of twenty-two in 1985, I began receiving endless questions about the secret of my success and the nature of my talent. Instead of asking about Sicilian Defenses, journalists wanted to know about my diet, my personal life, how many moves ahead I saw, and how many games I held in my memory.

I soon realized that my answers were disappointing. I didn’t eat anything special. I worked hard because my mother had taught me to. My memory was good, but hardly photographic. As for how many moves ahead a grandmaster sees, Russkin-Gutman makes much of the answer attributed to the great Cuban world champion José Raúl Capablanca, among others: “Just one, the best one.” This answer is as good or bad as any other, a pithy way of disposing with an attempt by an outsider to ask something insightful and failing to do so. It’s the equivalent of asking Lance Armstrong how many times he shifts gears during the Tour de France.

The only real answer, “It depends on the position and how much time I have,” is unsatisfying. In what may have been my best tournament game at the 1999 Hoogovens tournament in the Netherlands, I visualized the winning position a full fifteen moves ahead—an unusual feat. I sacrificed a great deal of material for an attack, burning my bridges; if my calculations were faulty I would be dead lost. Although my intuition was correct and my opponent, Topalov again, failed to find the best defense under pressure, subsequent analysis showed that despite my Herculean effort I had missed a shorter route to victory. Capablanca’s sarcasm aside, correctly evaluating a small handful of moves is far more important in human chess, and human decision-making in general, than the systematically deeper and deeper search for better moves—the number of moves “seen ahead”—that computers rely on.

There is little doubt that different people are blessed with different amounts of cognitive gifts such as long-term memory and the visuospatial skills chess players are said to employ. One of the reasons chess is an “unparalleled laboratory” and a “unique nexus” is that it demands high performance from so many of the brain’s functions. Where so many of these investigations fail on a practical level is by not recognizing the importance of the process of learning and playing chess. The ability to work hard for days on end without losing focus is a talent. The ability to keep absorbing new information after many hours of study is a talent. Programming yourself by analyzing your decision-making outcomes and processes can improve results much the way that a smarter chess algorithm will play better than another running on the same computer. We might not be able to change our hardware, but we can definitely upgrade our software.

With the supremacy of the chess machines now apparent and the contest of “Man vs. Machine” a thing of the past, perhaps it is time to return to the goals that made computer chess so attractive to many of the finest minds of the twentieth century. Playing better chess was a problem they wanted to solve, yes, and it has been solved. But there were other goals as well: to develop a program that played chess by thinking like a human, perhaps even by learning the game as a human does. Surely this would be a far more fruitful avenue of investigation than creating, as we are doing, ever-faster algorithms to run on ever-faster hardware.

This is our last chess metaphor, then—a metaphor for how we have discarded innovation and creativity in exchange for a steady supply of marketable products. The dreams of creating an artificial intelligence that would engage in an ancient game symbolic of human thought have been abandoned. Instead, every year we have new chess programs, and new versions of old ones, that are all based on the same basic programming concepts for picking a move by searching through millions of possibilities that were developed in the 1960s and 1970s.

Like so much else in our technology-rich and innovation-poor modern world, chess computing has fallen prey to incrementalism and the demands of the market. Brute-force programs play the best chess, so why bother with anything else? Why waste time and money experimenting with new and innovative ideas when we already know what works? Such thinking should horrify anyone worthy of the name of scientist, but it seems, tragically, to be the norm. Our best minds have gone into financial engineering instead of real engineering, with catastrophic results for both sectors.

Perhaps chess is the wrong game for the times. Poker is now everywhere, as amateurs dream of winning millions and being on television for playing a card game whose complexities can be detailed on a single piece of paper. But while chess is a 100 percent information game—both players are aware of all the data all the time—and therefore directly susceptible to computing power, poker has hidden cards and variable stakes, creating critical roles for chance, bluffing, and risk management.

These might seem to be aspects of poker based entirely on human psychology and therefore invulnerable to computer incursion. A machine can trivially calculate the odds of every hand, but what to make of an opponent with poor odds making a large bet? And yet the computers are advancing here as well. Jonathan Schaeffer, the inventor of the checkers-solving program, has moved on to poker and his digital players are performing better and better against strong humans—with obvious implications for online gambling sites.

Perhaps the current trend of many chess professionals taking up the more lucrative pastime of poker is not a wholly negative one. It may not be too late for humans to relearn how to take risks in order to innovate and thereby maintain the advanced lifestyles we enjoy. And if it takes a poker-playing supercomputer to remind us that we can’t enjoy the rewards without taking the risks, so be it.

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After Life

This afternoon, I watched 2012 on HBO and I was wondering what would happen to the world after the disaster happen? Is the after life really exists or not? I believe from stories from my teachers, friends and parents they told me if there is another life after we are dead so I’m thinking what would happen there? Is it sins and kindnesses to people that count us where we should be after the real life in the world? So life that we live in this world is just kind of practice to face the after life? Many people told me that the after life is harder than the real life in world. If the after life divided by places and it’s count by our sins and kindnesses in the world to decide us then why God created evil spirit? God is being unfair in here for me because when some people are can face the temptation by the evil spirit than they sure can be put in heaven? What about those people who always make sins in the world? It’s not because only their decision, the people around them who also got caught by the evil spirit also convince them to join their sins.

Like another case here, in the tv series ‘Ghost Whisperer’ why does the ghost or the spirit of the dead people still there? They said there is an after life after we die but why they keep hanging around us and do bad or good things? Many stories have been told to our generations but which one is the right one? What people told me was when people dead they directly go to black place where they should choose the right path to heaven and there are many road that we should pass and if we fail we have to stay at hell for years but there are also stories that told me after we die we still have 40 days in the world and they don’t know for what it is about. From the stories here I got confused but now back to real question here does the after life exist? I think yes, it does exist, but for me it’s only people perception, if they didn’t believe about hell and heaven they maybe live without belief in their life and do not have goal and wondering what is life got to do with them, and yes that’s true and me also somehow asking to myself actually what am I going to be after the life is over, and I know there is no answer for this question and there will be a very difficult and long explanation to explain the after life itself.

 

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what is love? baby don’t hurt me baby don’t hurt me no more~ -_-

In this love there is such a thing called LOVE. You all may know this because who have never had this feelings before? Oh yes we all do have this feeling. I was wondering, why do we need to feel love in this world? There are a lot of romance movies, drama, books, poems, music, and everything usually is about love. Love is beautiful, wonderful, amazing, there is this feeling when you fall in love when you think about someone that you love you will feel really happy and spontaneously you can’t stop smiling when you remember about nice things with the person you love. That’s what will you experience when you are fall in love. But there’s the time when that someone who makes you happy, smiling, and dancing inside your deep loving heart is bored about you and try to find someone else to replace you. Is it the ending supposed to be? Is it your happiness meant to be? Love, sometimes, not amazing and really nice as we think about. Love sometimes can make us depressed, stressed, sick, or even sometimes can makes us commit suicide for only the person we love. So actually why God make a such thing called love if it is only going to break someone’s heart and even make them real depress? When people get broken heart they feel real stress out and can’t think about anything normal. For example I have a friend that can’t express her love to this one person that she loves very much. One day she texted him about her feeling. Then after that they can only be friends not more than friends. They bbm-ing, they texting, they chit chatting like other friends do, but this is rarely happened. When the guy is supposed to be leaving to overseas for continuing his study, this girl felt real depressed, cannot think clearly, she cried, she can’t move on. Love actually not only for a girl and boy who fell in love. But it can be also for our family and friends that really close to us. Sometimes we have the same kind of trouble like my example above. Broken heart, back stabber, jealous, actually these kind of feeling is come from love. For me love is really nice, sweet and real beautiful. Why is that? Because once you broken hearted you will find happiness in the future. When we broke up with our relationship partner, we still have friends and family to cheer me up. Well, there is this moment when you need to be alone. But when you need them they will always be there for you and make you happy so I think that’s all real love about is. Love is a gift from God that should make us care to each other and God himself. Love from others is something we should be thankful of, because without love, our life is just a dark night without stars, a dark night where there is no beautiful light to shine, no life to live.

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Inception

I was watching a movie called inception few weeks ago. In that movie, they told the audience that we can go inside other person’s dream. that makes me real confused, why a person or more can get in to someone’s dream and plan what they want to do inside their dream. This is so not real like how do yo know if you were in a dream or in the real world if your dream is what you have been doing in the real world? When I watched this movie I need to think real hard to get what the movie means and this movie is really complicated. I was wondering all these weeks about the end of the movie if it is the dream world or the real world? how the main actor know if it is a real world or his dream? with this question I remembered my lesson in TOK class that always ask about this two question “what do you know?” and “how do you know?” these two question relate to the movie that I’ve watched. With this, I can assume this knowledge he got from his faith, if he didn’t believe in himself that this is his real world or his dream he of course got confused.

At the same movie, in some parts I also wondering the dead wife of the main character is still showed up in his dream when he was in his mission. How can a dead body showed up still alive in a person dream but to do her revenge because of grudge to her husband. It doesn’t make any sense about this movie. How can it be like that? and also the thing that made me confused how they can get in to the dream inside a dream of one person and also have an architect and helped them to create the design of the dream. This is what I always wanted, I always hope that we can live in our dream and we can arrange it by ourself without any struggle and problem with the outside world, I know it sounds autism but it is better to be that way without problems.

When I was wondering all these weeks after I watched the movie I think that this movie is related to movie “The Matrix” that I have watched in TOK class in the end of grade 10 at the induction program. “The Matrix” also talked about our dream that we spent this whole years. From the numbers that the technology made we can do anything. But how do they know if they live in their dreams if the Matrix didn’t take their cable and took them to the reality world? What did they feel to be in the world that not they have been there before? I don’t want to be in the world like the ‘reality’ world in the matrix. And also I don’t want to get confused about which one is the reality world and the dream even though the dream world is often better than the real world but I don’t want to be like the main character in Inception that always thinking about her dead wife and they came up alive in your dream, that is so creepy because you already know that they were already passed away.

From this perception of mine I can say that from here I just want to live in my real life without dreaming about what would it be when I can live in my dreams because it is just my fantasize and I really want to live in my real life and graduate from IB.

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