Most people think that computers are smarter than their creators (human beings), whereas others believe differently. Some believe that by using the latest technology of multicore processors which are able to perform exponentially large calculations within a blink of eye, these computers of 21st century possess unparalleled processing power and are thus difficult to be outperformed in the highly thoughtful game of chess by mere humans. On the other hand, there are also some who still have utter faith in the unimaginable power of human brain. After giving me necessary directions for moving forward in my quest to find the limits of a human’s brain against that of the supercomputer’s CPU(the central processing unit), my high school’s chess teacher, …show more content…
It gives us insight into the ups and downs of the deep Blue team on its way to finally defeating Gary Kasparov. It also includes appendices that completely record Deep Blue's matches. This book, a technological milestone, is not just a triumph, but a rare, pivotal watershed. The book offers a detailed account of IBM's Deep Blue chess program, the people who created it, and its historic battles with World Chess Champion Garry Kasparov. It establishes the point in history when mankind's exciting new tool, the computer, came of age and competed with its human creators in the ultimate intellectual competition: a game of chess. The text examines the progress made by the creators of Deep Blue, beginning with the1989 two-game match against Kasparov. This book gave me very crucial information on how diligently the humans struggled to create such a master piece, the Deep Blue, which was ultimately able to defeat even the great grandmaster Gary …show more content…
Because Deep Blue had no track record as a chess player, Kasparov could not prepare for this match as he has for other matches by studying his opponent's previously played games. Levinson and his coworkers at the University of California, Santa Cruz developed a computer program, called Morph that learned to play chess starting only with a list of legal moves. That a computer which relies largely on speedily checking the consequences of billions of possible moves could come so close to matching the human capabilities required to play the game at its highest level was a striking achievement for the team that designed, built, and programmed Deep
Smarter than You Think starts out with a cautionary tale of how in 1997 world chess champion Garry Kasparov was beaten by Deep Blue, an I.B.M. supercomputer. This was a considered a milestone in artificial intelligence. If a computer could easily defeat a chess champion, what would happen to the game and its players? A year after Kasparov was defeated by the program he decided to see what would happen when a computer and person were paired up. He called this collaboration the centaur; A hybrid consisting of the algorithms and history logs of chess as well as the brain to “analyze their opponents’ strengths and weaknesses, as well as their moods.” ...
Andy Clark strongly argues for the theory that computers have the potential for being intelligent beings in his work “Mindware: Meat Machines.” The support Clark uses to defend his claims states the similar comparison of humans and machines using an array of symbols to perform functions. The main argument of his work can be interpreted as follows:
In the essay "Toward An Intelligence Beyond Man’s" by Robert Jastrow, the author showed his view on computer intelligence and predicted that computer intelligence will be a new kind of evolution. Jastrow stateed that computer nowadays is as intelligent as human brain; they can communicate with human, learn from experience, and raise logical questions. The more complex the computer, the better they imitate human. He predicted that computer will as important as life in future years. Then, Jastrow used the example of Arthur Samuel and IBM computer to show computers can learn faster through motivation, even they do not have emotions and drives as human do. He also points out that computer and human brain share some characteristics; they both freeze out when handle too many tasks, and they outclass fast decisions under a crisis. Jastrow said even human still have the control power, computers learn much faster than humans’ intelligence. Then, in an ultimate situation, computers and human w ill become partners; they completely depends on each other to survive. However, Jastrow thought this partnership will not stay long; as computer will become more and more clever , but human evolution of intelligence is almost finished. He suggested that computer will be the new kind of intelligence which surpass human, as a new evolution of life. He said the history had proved it takes a million year for human evolution. It took less time , compare to a billion years of evolution from worm to human. By the incredibly fast rate of technology improvement, Jastrow thought computer will evolve in a much shorter period of time.
And the skills we try to learn can be less strenuous to obtain. He takes us back to kasparov and how when he was coming about in the soviet union only a few kids that showed promise could get lessons from a grand master and then be able to access records to famous chess games. Clive Thompson considers the fact that computers have leveled the playing field. Now any kid anywhere in the world that has access to the internet can learn more about chess games. Playing an artificial opponent made the game a little faster and the instincts of a player became fast as well. A player could also experiment and see what the outcome of different moves could be. This also means grandmaster players are being produced at a much younger age than ever before. He makes a reference to grand master Bobby Fischer who became a grand master at age fifteen. He does this to show how with the emergence of computers new grand masters are getting younger and younger. Such as Sergey Karjakin who became grand master in two thousand and two at the age of twelve. This clearly shows how computers speed up the learning
Carr starts off his argument by referencing a “2001 a space odyssey” released in 1968 about a computer named HAL that tries to kill the astronauts that are on the spaceship that HAL controls. Carr uses an excerpt from this movie to incite fear into his readers and fear clouds judgement and causes irrational ideas to be formed. This movie is an over exaggerated sci-fi thriller and not a realistic representation of what computers are becoming. At the conclusion of his argument Carr does not forget to leave his readers the way he greeted them, Carr quotes 2001: a space odyssey “i can feel it. I’m afraid” (Carr 328). Although emotions are a strong way to engage with a reader, strong emotions also distract readers from the actual argument and encourage the reader to make a decision based on their feeling rather than their actual brain. The fact that Carr uses emotion to convince his readers is quite ironic, considering he is arguing that new technology is limiting our ability to use our brains. In contrast Thompson’s article uses logic and reason to make his argument. At the same time Thompson’s article still engages readers and is just as interesting to read as Carr’s essay. Thompson’s article starts off pondering whether computers or humans are better at chess. To answer this
Once Deep Blue supercomputer defeated chess grandmaster Kasparov, he, Kasparov, thought what would happen if “humans and computers collaborated” (Thompson 343)? Kasparov figured that it would be a symbiotic relationship in which “each might benefit from the other’s peculiar powers” (Thompson344). A Notably example would a 2005 “freestyle” chess tournament, which consisted of teams with computers and chess players. With a tournament full of computers and chess grandmasters, the winners were amateur chess players Cramton and Zackary (Thompson345). The reason why these players were able to win is because they were “expert[s] at collaborating with computers.” By themselves these players would not have the skills to take on such talented players, but since Cramton and Zackary were able to know “when to rely on human smarts and when to rely on the machine’s advice” they were able to succeed (Thompson 345). These players were able to harness the power of the symbiotic relationship between man and machine. In conclusion, when it comes down to the wire on “who’s smarter-humans or machines; the answer is neither, it’s both working side by side” (Thompson 347). In addition, the benefits of these digital gadgets can be summarized into three
The Chinese room argument certainly shows a distinction between a human mind and strong AI. However, it seems that the depths of human understanding can also be a weakness to how it compares to strong AI and the way that knowledge and understanding is derived.
These projects come to live in the Research division at IBM. In 2005 Paul Horn, director of the division wanted to try to create a machine able to pass the Turing Test. No machine had done it. But researchers didn’t believe that it would get the public’s attention in the way that Deep Blue had. Horn thought of another game where it would...
The official foundations for "artificial intelligence" were set forth by A. M. Turing, in his 1950 paper "Computing Machinery and Intelligence" wherein he also coined the term and made predictions about the field. He claimed that by 1960, a computer would be able to formulate and prove complex mathematical theorems, write music and poetry, become world chess champion, and pass his test of artificial intelligences. In his test, a computer is required to carry on a compelling conversation with humans, fooling them into believing they are speaking with another human. All of his predictions require a computer to think and reason in the same manner as a human. Despite 50 years of effort, only the chess championship has come true. By refocusing artificial intelligence research to a more humanlike, cognitive model, the field will create machines that are truly intelligent, capable of meet Turing's goals. Currently, the only "intelligent" programs and computers are not really intelligent at all, but rather they are clever applications of different algorithms lacking expandability and versatility. The human intellect has only been used in limited ways in the artificial intelligence field, however it is the ideal model upon which to base research. Concentrating research on a more cognitive model will allow the artificial intelligence (AI) field to create more intelligent entities and ultimately, once appropriate hardware exists, a true AI.
For years philosophers have enquired into the nature of the mind, and specifically the mysteries of intelligence and consciousness. (O’Brien 2017) One of these mysteries is how a material object, the brain, can produce thoughts and rational reasoning. The Computational Theory of Mind (CTM) was devised in response to this problem, and suggests that the brain is quite literally a computer, and that thinking is essentially computation. (BOOK) This idea was first theorised by philosopher Hilary Putnam, but was later developed by Jerry Fodor, and continues to be further investigated today as cognitive science, modern computers, and artificial intelligence continue to advance. [REF] Computer processing machines ‘think’ by recognising information
The traditional notion that seeks to compare human minds, with all its intricacies and biochemical functions, to that of artificially programmed digital computers, is self-defeating and it should be discredited in dialogs regarding the theory of artificial intelligence. This traditional notion is akin to comparing, in crude terms, cars and aeroplanes or ice cream and cream cheese. Human mental states are caused by various behaviours of elements in the brain, and these behaviours in are adjudged by the biochemical composition of our brains, which are responsible for our thoughts and functions. When we discuss mental states of systems it is important to distinguish between human brains and that of any natural or artificial organisms which is said to have central processing systems (i.e. brains of chimpanzees, microchips etc.). Although various similarities may exist between those systems in terms of functions and behaviourism, the intrinsic intentionality within those systems differ extensively. Although it may not be possible to prove that whether or not mental states exist at all in systems other than our own, in this paper I will strive to present arguments that a machine that computes and responds to inputs does indeed have a state of mind, but one that does not necessarily result in a form of mentality. This paper will discuss how the states and intentionality of digital computers are different from the states of human brains and yet they are indeed states of a mind resulting from various functions in their central processing systems.
There are expert systems that can solve complex problems that humans train their whole lives for. In 1997, IBM's Deep Blue defeated the world champion in a game of chess (Karlgaard, p43). Expert systems design buildings, configure airplanes, and diagnose breathing problems. NASA's Deep Space One probe left with software that lets the probe diagnose problems and fix itself (Lyons).
Crevier, D. (1999). AI: The tumultuous history of the search for Artificial Intelligence. Basic Books: New York.
...on, adaptation, and planning for the future. The computer is unable to win because it cannot think like a human, and that is why we humans are smarter than computers to this day (The Daily Galaxy 1-3).
In the past few decades we have seen how computers are becoming more and more advance, challenging the abilities of the human brain. We have seen computers doing complex assignments like launching of a rocket or analysis from outer space. But the human brain is responsible for, thought, feelings, creativity, and other qualities that make us humans. So the brain has to be more complex and more complete than any computer. Besides if the brain created the computer, the computer cannot be better than the brain. There are many differences between the human brain and the computer, for example, the capacity to learn new things. Even the most advance computer can never learn like a human does. While we might be able to install new information onto a computer it can never learn new material by itself. Also computers are limited to what they “learn”, depending on the memory left or space in the hard disk not like the human brain which is constantly learning everyday. Computers can neither make judgments on what they are “learning” or disagree with the new material. They must accept into their memory what it’s being programmed onto them. Besides everything that is found in a computer is based on what the human brain has acquired though experience.