Key Messages
Garry Kasparov’s Incredible Story: From A Chess Grandmaster To Tech Guru And Beyond
Garry Kasparov’s Deep Thinking teaches us profound lessons about the future of artificial intelligence.
How? Through the cultural story of chess!
Through this remarkable book, Kasparov takes us on a journey to examine how technology has changed our lives over the past fifty years, as well as what we might expect from the rapidly evolving world around us.
Kasparov is uniquely qualified to shed light on this topic, having famously gone up against the cutting-edge technology of IBM’s Deep Blue in a series of highly-publicized chess matches in the late 90s.
He explains the mechanics of both chess and artificial intelligence, showing us why computer technicians aren’t to be trusted and which lunchbox item caused an uproar at the 1978 World Chess Championship!
We’ll get an insight into basic programming principles behind Google Assistant and Amazon’s Alexa as well.
So if you’re looking to get to grips with the future of artificial intelligence – through the prism of chess – then Kasparov’s Deep Thinking is your guide.
Chess: Ancient, Controversial And Cherished In Russia But Often Undervalued In The West
Chess is viewed in vastly different ways when comparing the West to Russia.
Where it once had a place in Western culture for centuries, it finds itself too often associated with nerdy stereotypes and ostracized by society.
The author, Garry Kasparov, has worked hard to challenge this stigma, but very little has changed.
In stark contrast, chess is held in high regard in Russia.
From Tsarist times and into the Soviet Union era, chess was extensively promoted and widely played so much so that elite chess players were exempt from military service during civil wars.
No matter how deeply rooted these social attitudes may be here in the West, there is some hope for change – thanks to school chess programs where children are rediscovering that chess can actually be fun, without the traditional prejudices attached to it.
How Computers Became Chess Grandmasters Thanks To Moore’S Law And Alpha-Beta Algorithm
The computers’ capability to play chess improved drastically over the years, and in a short span of time was able to go from just about beating novice players to challenging grandmasters.
In 1956, the first chess-playing computer, MANIAC 1, was developed at a laboratory in Los Alamos but could not compete on equal footing with an experienced player due to its limited memory which resulted in it having to play on a board of only 36 squares without bishops.
Nevertheless, the same machine managed to beat a novice player later that year – making it the first instance of artificial intelligence besting a human at an intellectual game.
This substantial improvement was made possible by Moore’s law which stated that computers would double their processing speeds every two years and with upcoming advancements like alpha-beta algorithm – refined in the 1970s – allowing them to automatically reject any less effective move than the current being considered coupled with capacity to evaluate several moves ahead, before long they were powerful enough to challenge even grandmasters.
By 1977, their abilities had advanced enough for them compete with the top 5% of human players.
We Should Embrace Technological Progress And Learn To Adapt To The Changing Labour Market
It’s no wonder that computers are replacing humans in various service industries.
We’ve seen it through the ages, starting with the Industrial Revolution when agricultural and manufacturing equipment started to replace manual labor.
Then came precise machinery during the 1960s and 1970s, followed by e-services duing the Information Revolution that wiped out many jobs connected to service and support.
It’s only logical then that computer programs will soon start to take over even more prestigious professions, such as those of doctors and lawyers.
While some people might get riled up about computers putting humans out of work, there is no need for this kind of sentimentality.
Technological progress has invariably been a net positive for mankind – leading to increases in quality of life and human rights.
So if you find yourself complaining about machines taking over manual labor, remember how privileged we are to have access to devices that provide us with so much information at our fingertips.
We just have to learn how to adapt.
Clerks and cashiers can no longer go back to manufacturing jobs; they’ll need to be equipped with new skills related to technology and services as they emerge instead.
So while it may be sad that traditional work is gradually getting replaced by machines, it doesn’t mean things will stay this way forever; there’s always something new around the corner!
The Possibility Of Machine-Generated Innovation Through Artificial Intelligence
The development of artificial intelligence is revolutionizing the way we play chess.
In September 2016, Kasparov visited a robotics event in Oxford and spoke with a robot called Artie, showing just how quickly AI has developed since then.
Machines are now capable of asking their own questions instead of relying solely on set human prompts for automated response-questions.
Scientists are working to see if machines can formulate their own questions from data that they harvest.
This will allow them to go beyond just providing data and maybe even come up with completely novel strategies and plays which can be taught to humans as well.
In the world of chess, computer programs no longer have to have strategies directly programmed into them – mathematical rules themselves are enough for machines to figure out how to play chess on their own.
As AI continues to advance, these machines will be able to surprise us not only with their strategies, but also with the data they can produce.
The Superiority Of Psychological Chess Strategies Over Cold Computation
For humans, chess is an incredibly intense psychological game.
Legendary player Emanuel Lasker understood this, and his view of the game was that the best move need not necessarily be the most logical one from a tactical perspective; it should be the move to make your opponent uncomfortable.
To achieve this, careful analysis of your opponents’ games must take place in order to uncover their weaknesses and select moves likely to psychologically destabilize them.
Yet when computers are involved in chess matches, it’s a completely different scenario.
Computers have no emotions; for them, playing chess is purely about strategy and computing which combination of moves will put them in a winning position.
In fact, by 1985 computer programs were powerful enough to calculate all possible moves over the next three or four turns and choose the most appropriate one – provided they are given at least five moves ahead notice.
Brute Force Is The Key To Success For Artificial Intelligence, But It Can Also Lead To Baffling Mistakes
Donald Michie showed in 1960 that feeding computers large amounts of raw data can result in brilliant programs.
He proved this by using tic-tac-toe as an example.
By feeding the computer a huge number of game moves and examples, it was able to learn basic principles from them.
We see this sort of machine learning process now with modern translation programs like Google Translate.
They don’t actually comprehend human languages; they’re just given millions of examples of sentences and translations made by people, based on which they’re able to string together a reasonable interpretation for any text you enter.
However, despite their brilliance, computers that rely heavily on large volumes of data can occasionally make massive errors due to oversights or misinterpretations by the computer when trying to apply those data sets.
This is exactly what happened when Michie and other researchers tried to build a chess-playing machine in the 80s.
The machine became good at playing chess, but eventually made moves like suddenly sacrificing its queen without apparent reason due to the fact that it had learned from grandmasters that sacrificing the queen could lead to a checkmate—without realizing there were several other factors necessary for such a gambit to work.
In conclusion, massive amounts of data can be used together with machines to produce remarkable programs, but they will still have their limits and are prone to making mistakes if not handled properly.
The Realization Of Losing To The Machines: Garry Kasparov’S Experience With Artificial Intelligence
In Deep Thinking, author and world-renowned chess player Garry Kasparov speaks to the importance of learning how to lose gracefully.
For many people, losing is never easy – but when it comes to going up against a computer in a game of chess, you must be prepared to accept defeat.
Kasparov recounts his early days of playing the game, admitting that he wasn’t always the best loser.
He suffered through sleepless nights and threw tantrums after a loss – anything to not accept defeat.
However, often it was this intense passion that allowed him to play through 2400 career matches with only 170 losses.
But when facing supercomputers like Fritz 3 and IBM’s Deep Blue, Kasparov realized that no matter how well he played, most times the computers could calculate more possibilities than him in mere seconds leading him down an inevitable path of defeat.
It was during these games that Kasparov had to learn how to properly lose; with acceptance and humility rather than desperation and avoidance.
Chess Is A Game Far From Immune To Foul Play
It’s no secret that competitive sports come with a certain level of unsportsmanlike conduct.
The game of chess is also no stranger to foul play.
Anatoly Karpov and Viktor Korchnoi, two of the most influential players in the 1970s, had a particularly intense rivalry where each accused the other of cheating.
Karpov went so far as to hire a psychologist to use hypnosis or distraction techniques against his opponent, while Korchnoi attempted to get back at him by having Indian sect members meditate and stare at him.
But it’s not just humans who are susceptible to cheating – computers are too.
Competition regulations today strictly control how much human intervention can be done on computers in order for them to make fair decisions.
When Deep Blue faced off against Garry Kasparov in 1997, two incidents occured where the computer crashed and subsequently restarted, resulting in different moves than it would have if it hadn’t malfunctioned.
Some suggest that this could potentially be used as an means of giving computers an unfair advantage during matches – an example of why such interventions need to be carefully regulated.
So while computers have been able win games like chess through raw processing power, they’re still vulnerable against different types of foul play – something we must continue to monitor and mitigate going forward into more complex board games like Go.
Wrap Up
In Deep Thinking by Garry Kasparov, we are presented with the idea of a revolution in artificial intelligence.
We learn that AI is quickly surpassing human intelligence and is able to do things like beat chess grandmasters with the help of its sheer computing power, incredible data processing skills, and newfound ability to analyze data independently and develop solutions.
This book provides a powerful conclusion to this conversation: as AI continues to evolve, so will our own capacity for understanding life’s mysteries – both on a personal level and within the universe itself.
