Artificial Intelligence
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The document discusses the definition and concepts surrounding artificial intelligence (AI), emphasizing that true intelligence should not be limited to mimicking human behavior but should also encompass the creation of machines that can learn and adapt independently. It critiques how intelligence is defined, noting that much goes unexamined when limiting it to human-like qualities, and posits that the scientific method and evolutionary processes can aid in developing intelligent systems. The paper suggests simplifying AI solutions and learning from nature as crucial for innovation and effective problem-solving in AI development.
![Some DefinitionsWaterman (1986, p. 10) offered that artificial intelligence was “the part of computer science concerned with developing intelligent computer programs.” This tautological tatement offers no basis for designing an intelligent machine or program.<br />Rich (1983, p. 1) offered, “Artificial intelligence (AI) is the study of how to <br />make computers do things at which, at the moment, people are better,” which was echoed even as recently as 1999 by Lenat (in Moody, 1999). But this definition, if regarded statically, precludes the very existence of artificial intelligence. Once a computer program exceeds the capabilities of a human, the program is no longer in the domain of AI. Russell (quoted in Ubiquity, 2004) offered, “An intelligent system is one whose expected utility is the highest that can be achieved by any system with the same computational limitations.” But this definition appears to offer intelligence to a calculator, for there can be no higher expected utility than getting four as the right answer to two plus two. It might even extend to a pebble, sitting at equilibrium on a bottom of a pond, with no computational ability whatsoever. It is no wonder that we have not achieved our dreams when our efforts have been defined so poorly.<br />Schildt claimed, “An intelligent program is one that exhibits behavior similar to that of a<br />human when confronted with a similar problem. It is not necessary that the program actually solve, or attempt to solve, the problem in the same way that a human would.” What then if there were no humans? What if humans had never evolved? Would this preclude the possibility of intelligent machines? What about intelligent machines on other planets? Is this precluded because no humans reside on other planets? Humans are intelligent, but they are only one example of intelligence, which must be defined properly in order to engage in a meaningful discourse about the possibility of creating intelligent machines, be they based in silicon or carbon. <br />That artificial intelligence is an extremely fragmented collection of endeavors is as true today as it was in 1991. Yet the vision of what is to be created remains prominent today, even as it did when Minsky (1991) wrote: “Why can’t we build, once and for all, machines that grow and<br />improve themselves by learning from experience? Why can’t we simply explain what we want, and then let our machines do experiments or read some books or go to school, the sorts of things that people do. Our machines today do no such things.”<br />1.3 Definition of Intelligence<br />If one word were to be used to describe research in artificial intelligence, that word might be fragmented. It has been this way for many decades. Opinions as to the cause of this scattered effort are varied, even themselves fragmented. Atmar (1976) remarked: Perhaps the major problem is our viewpoint. Intelligence is generally regarded as a uniquely human quality. And yet we, as humans, do not understand ourselves, our capabilities, or our origins of thought. In our rush to catalogue and emulate our own staggering array of behavioral responses, it is only logical to suspect that investigations into the primal causative factors of intelligence have been passed over in order to more rapidly obtain the immediate consequences of intelligence.<br />Minsky (1991), on the other hand, assigned blame to attempts at unifying theories of intelligence: “There is no one best way to represent knowledge or to solve problems, and the limitations of current machine intelligence largely stem from <br />seeking unified theories or trying to repair the deficiencies of theoretically neat but conceptually impoverished ideological positions.” It is, however, difficult to unify criticisms that can move fluidly between intelligence, theories of intelligence, knowledge representation, and problem solving. These things are not all the same, and blame may be better assigned to a lack of clarity in distinguishing between these aspects of the broader issue of what constitutes intelligence. A prerequisite to embarking upon research in artificial intelligence should be a definition of the term intelligence. As noted above (Atmar, 1976), many definitions of intelligence have relied on this property being uniquely human and often reflect a highly anthropocentric view. I will argue here that this viewpoint is entirely misplaced and has hindered our advancement of machine intelligence for decades. Twenty years ago, Schank stated: No question in AI has been the subject of more intense debate than that of assessing machine intelligence and understanding. People unconsciously feel that calling something other than humans intelligent denigrates humans and reduces their vision of<br />themselves as the center of the universe. Dolphins are intelligent. Whales are intelligent.<br />Apes are intelligent. Even dogs and cats are intelligent. Certainly, other living systems can be described as being intelligent, without ascribing specific intelligence to any individual member of the system. Any proposed definition of intelligence should not rely on comparisons to individual organisms.<br />1.4 The Turing Test<br />Turing (1950) considered the question, “Can machines think?” Rather than define the terms “machines” or “think,” Turing proposed a test that begins with three people: a man (A), a woman (B), and an interrogator (C). The interrogator is to be separated from both A and B, say, in a closed room (Figure 1-1) but may ask questions of both A and B. The interrogator’s objective is to determine which (A or B) is the woman and, by consequence, which is the man. It is A’s objective to cause C to make an incorrect identification. Turing provided the following example of a question posed to the man: <br />“C: Will X [C’s name for A] please tell me the length of his or her hair?”<br />“A: My hair is shingled, and the longest strands are about nine inches long.”<br />Player A may be deceitful, if he so desires. In contrast, the object for B is to help the interrogator. Turing suggested that the probable best strategy for her is to give truthful answers. In order that the pitch of the voice or other external clues may not aid in C’s decision, a teleprinter was to be used for communication between the rooms.<br />Turing then replaced the original question, “Can machines think?” with the following: “We now ask the question, ‘What will happen when a machine takes the part of A in this game?’ Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman.” This question separates the physical and intellectual capabilities of humans.<br />The form of interrogation prevents C from using sensory information regarding A’s or B’s physical characteristics. Presumably, if the interrogator were able to show no increased ability to decide between A and B when the machine was playing as opposed to when the man was playing, then the machine would be declared to have passed the test. Whether or not the machine should then be judged capable of thinking was left unanswered. Turing in fact dismissed this original question as being “too meaningless to deserve discussion.”<br />1.5 Intelligence, The Scientific Method, And Evolution<br />Following a similar line of thought to that described above, Ornstein (1965) argued that all learning processes are adaptive. The most important aspect of such learning processes is the “development of implicit or explicit techniques to accurately estimate the probabilities of future events.” Similar notions were offered by Atmar (1976, 1979). When faced with a changing environment, the adaptation of behavior becomes little more than a shot in the dark if the system is incapable of predicting future events. Ornstein (1965) suggested that as predicting future events is the “forte of science,” it is sensible to examine the scientific method for useful cues in the search for effective learning machines. The scientific method is an iterative process that facilitates the gaining of new knowledge about the underlying processes of an observable environment. Unknown aspects of the environment are estimated. Data are collected in the form of previous observations or known results and combined with newly acquired measurements. After the removal of known erroneous data, a class of models of the environment that is consistent with the data is generalized. This process is necessarily<br />inductive. The class of models is then generally reduced by parametrization, a deductive process. The specific hypothesized model (or models) is then tested for its ability to predict future aspects of the environment. Models that prove worthy are modified, extended, or combined to form new hypotheses that carry on a “heredity of reasonableness” (Fogel, 1964). This process is iterated until a sufficient level of credibility is achieved. “As the hypotheses correspond more and more closely with the logic of the environment they provide an ‘understanding’ that is demonstrated in terms of improved goal-seeking behavior in the face of that environment”<br />It appears reasonable to seek methods to mechanize the scientific method in an algorithmic formulation so that a machine may carry out the procedure and similarly gain knowledge about its environment and adapt its behavior to meet goals. The scientific method can be used to describe a process of human investigation of the universe, or of learning processes in general.<br />The Scientific Method<br />1.6 Evolving Artificial Intelligence<br />It is natural to conclude that by simulating the evolutionary learning process on a computer, the machine can become intelligent, that it can adapt its behavior to meet goals in a range of environments. Expert systems, knowledge-based systems, neural networks, fuzzy systems, and other systems generate behavior, in the form of stimulus– response pairs, as a function of the environment. None of these frameworks, however, is intrinsically intelligent. Each is a mathematical function that takes input and yields output. Only when a learning mechanism is imposed on these frameworks is it meaningful to discuss the intelligence that emerges from such a system. Although other means for creating learning systems are available (e.g., reinforcement), evolution provides the most fundamental learning mechanism that can be<br />applied generally across each of the frameworks and combinations of these frameworks.<br />This is evidenced in all the life around us. “Intelligence is a basic property of life” (Atmar). It has occurred at the earliest instance of natural selection and has pervaded all subsequent living organisms. In many ways, life is intelligence, and the processes cannot be easily partitioned. To date, the majority of self-described efforts in artificial intelligence have relied<br />on comparisons to human behavior and human intelligence. This has occurred even<br />in the face of high-visibility failures and overextended claims and projections. Steels (in Manuel, 2003) remarked: “Most people in the field have known for a long time that mimicking exclusively human intelligence is not a good path for progress, neither from the viewpoint of building practical applications nor from the viewpoint of progressing in the scientific understanding of intelligence.” Put another way, the “artificial” is not nearly as important as the “intelligence.” It is not enough to “fake” the intelligence or mimic its overt consequences following the Turing Test. And it is simply sophistry to say that some software is an example of artificial intelligence because “people in the AI community worked on it” (Russell, in Ubiquity, 2004). Significant advancement in making intelligent machines—machines that adapt their<br />behavior to meet goals in a range of environments—requires more than simply claiming we are doing AI. Numerous opinions about the proper goal for artificial intelligence research have been expressed. But intuitively, intelligence must be the same process in living organisms as it is in machines. In the late 1980s, Genesereth and Nilsson (1987) offered: “Artificial Intelligence is the study of intelligent behavior. Its ultimate goal is a theory of intelligence that accounts for the behavior of naturally occurring intelligent entities and that guides the creation of artificial entities capable of intelligent behavior.” This was a worthy objective 15 years ago and it remains so today. The thesis promoted here is that evolutionary processes account for such intelligent behavior and can be simulated and used for the creation of intelligent machines. <br />1.7 Simulation Of Human Expertise<br />The acceptance of the Turing Test focused attention on mimicking human behavior. At the time (1950), it was beyond any reasonable consideration that a computer could pass the Turing Test. Rather than focus on imitating human behavior in conversation, attention was turned to more limited domains of interest. Simple two-person games of strategy were selected. These games received attention for at least three reasons: (1) Their rules are static, known to both players, and easy to express in a computer program; (2) they are examples from a class of problems concerned with reasoning about actions; and (3) the ability of a game-playing computer can be measured against human experts. The majority of research in game playing has been aimed at the development of heuristics that can be applied to two-person, zero-sum, nonrandom games of perfect information (Jackson, 1985). The term zero-sum indicates that any potential gain to one player will be reflected as a corresponding loss to the other player. The term nonrandom means that the allocation and positioning of resources in the game (e.g.,<br />pieces on a chess board) is purely deterministic. Perfect information indicates that both players have complete knowledge regarding the disposition of both players’ resources (e.g., tic-tac-toe, not poker). The general protocol was to examine an expert’s decisions during a game so as to discover a consistent set of parameters or questions that are evaluated during his<br />or her decision-making process. These conditions could then be formulated in an algorithm<br />that is capable of generating behavior that is similar to that of the expert when faced with identical situations. It was believed that if a sufficient quantity or “coverage” of heuristics could be programmed into the computer, the sheer speed and infallible computational ability of the computer would enable it to match or even exceed the ability of the human expert.<br />2. Future of Artificial Intelligence<br />Artificial Intelligence is often misunderstood. People’s eyes often glaze over when we begin to discuss AI. However, there is a common misconception that understanding AI requires an IQ of 200 and a PHD in rocket science. The truth is that AI when broken down to its nucleus is often nothing more than a simulation of nature and or intelligence in natural things. Nature gives us a glimpse of the genius in the rhythms and rules we live with but often take-for-granted. As briefly described below it is my belief that if engineers and developers strive to simplify the design process, they can begin to understand the concepts that will take technology to indescribable places; an ironic “catch 22.” Most people would agree that genius is clearly found in nature. However, rarely is any single creature or naturally occurring object in nature referred to as “a genius”; rather, nature reveals its intelligence in its designs, which we as Artificial Intelligence pioneers seek to replicate digitally. Commonly, researchers implement complex solutions to solve complex problems, often resulting in new and bigger problems.<br />Astonishing innovation is generally found in the simplest solutions, which are typically easy to<br />implement after you know what the solution is. Falling into the trap of over engineering is incredibly widespread today. Many engineers are not good innovators at all but play a costly game of “following the follower” in their designs. Engineers, for example, will typically examine how similar complex problems have been solved in the past and use those solutions as<br />their starting point, mistakenly assuming that the presupposition for the original design was ideal and the best methodology.<br />For example: intersections and traffic signals in many American cities are highly engineered using complex computer and AI technology to manage the flow of traffic. However this very complex solution, (electronically detecting traffic which then influences signal timing) results in one major flaw, which is, 50% of all traffic at a signaled intersection is stopped 100% of the time. Consider all the productivity lost in America because of this one overly elaborate and completely inefficient design; then juxtapose the “round-a-bout” intersection (commonly found in Europe and Australia), where all traffic merges into and out of a small circle intersection without stopping. Amazingly a roundabout is nothing more than a paved<br />circle and requires no signals or detection devices what-so-ever. Its natural and its extraordinarily simple.<br />Further, creativity in organizations is often squelched by systems evolved while smothered in<br />bureaucracy. Seldom is a corporate employee compensation plan designed to foster real innovation. For example, some large corporations compensate in-house software developers hourly, with production standards based upon how many “lines of code” are written. The reality is that “good code” requires fewer lines (and characters) to produce. In this example developers are ironically rewarded for being inefficient and lazy coders. The standard compensation plan which trades an employee’s time for money is not only old and tired, but it is fundamentally flawed. Unless the work itself is enormously exciting, the motivation is to waste time, not to innovate.<br />References](https://image.slidesharecdn.com/artificialintelligence-1284314167218-phpapp01/85/Artificial-Intelligence-6-320.jpg)
Artificial Intelligence
- 1. <br /> Artificial Intelligence<br />Akshay<br />B.Sc. IT <br />Sem – V<br />T W D<br /> Artificial Intelligence<br />What is AI ?
- 2. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines. The problem is that we cannot yet characterize in general what kinds of computational procedures we want to call intelligent. We understand some of the mechanisms of intelligence and not others Intelligence involves mechanisms, and AI research has discovered how to make computers carry out some of them and not others. If doing a task requires only mechanisms that are well understood today, computer programs can give very impressive performances on these tasks. Such programs should be considered “somewhat intelligent”.
- 5. Calculators are not intelligent. Calculators give the right answers to challenging math problems, but everything they “know” is preprogrammed by people. They can never learn anything new, and outside of their limited domain of utility, they have the expertise of a stone. Calculators are able to solve problems entirely because people are already able to solve those same problems. Since the earliest days of computing, we have envisioned machines that could go beyond our own ability to solve problems—intelligent machines. We have generated many computing devices that can solve mathematical problems of enormous complexity, but mainly these too are merely “calculators.” They are preprogrammed to do exactly what we want them to do. They accept input and generate the correct output. They may do it at blazingly fast speeds, but their underlying mechanisms depend on humans having already worked out how to write the programs that control their behavior. The dream of the intelligent machine is the vision of creating something that does not depend on having people pre-program its problem- solving behavior. Put another way, artificial intelligence should not seek to merely solve problems, but should rather seek to solve the problem of how to solve problems.
- 6. Some DefinitionsWaterman (1986, p. 10) offered that artificial intelligence was “the part of computer science concerned with developing intelligent computer programs.” This tautological tatement offers no basis for designing an intelligent machine or program.<br />Rich (1983, p. 1) offered, “Artificial intelligence (AI) is the study of how to <br />make computers do things at which, at the moment, people are better,” which was echoed even as recently as 1999 by Lenat (in Moody, 1999). But this definition, if regarded statically, precludes the very existence of artificial intelligence. Once a computer program exceeds the capabilities of a human, the program is no longer in the domain of AI. Russell (quoted in Ubiquity, 2004) offered, “An intelligent system is one whose expected utility is the highest that can be achieved by any system with the same computational limitations.” But this definition appears to offer intelligence to a calculator, for there can be no higher expected utility than getting four as the right answer to two plus two. It might even extend to a pebble, sitting at equilibrium on a bottom of a pond, with no computational ability whatsoever. It is no wonder that we have not achieved our dreams when our efforts have been defined so poorly.<br />Schildt claimed, “An intelligent program is one that exhibits behavior similar to that of a<br />human when confronted with a similar problem. It is not necessary that the program actually solve, or attempt to solve, the problem in the same way that a human would.” What then if there were no humans? What if humans had never evolved? Would this preclude the possibility of intelligent machines? What about intelligent machines on other planets? Is this precluded because no humans reside on other planets? Humans are intelligent, but they are only one example of intelligence, which must be defined properly in order to engage in a meaningful discourse about the possibility of creating intelligent machines, be they based in silicon or carbon. <br />That artificial intelligence is an extremely fragmented collection of endeavors is as true today as it was in 1991. Yet the vision of what is to be created remains prominent today, even as it did when Minsky (1991) wrote: “Why can’t we build, once and for all, machines that grow and<br />improve themselves by learning from experience? Why can’t we simply explain what we want, and then let our machines do experiments or read some books or go to school, the sorts of things that people do. Our machines today do no such things.”<br />1.3 Definition of Intelligence<br />If one word were to be used to describe research in artificial intelligence, that word might be fragmented. It has been this way for many decades. Opinions as to the cause of this scattered effort are varied, even themselves fragmented. Atmar (1976) remarked: Perhaps the major problem is our viewpoint. Intelligence is generally regarded as a uniquely human quality. And yet we, as humans, do not understand ourselves, our capabilities, or our origins of thought. In our rush to catalogue and emulate our own staggering array of behavioral responses, it is only logical to suspect that investigations into the primal causative factors of intelligence have been passed over in order to more rapidly obtain the immediate consequences of intelligence.<br />Minsky (1991), on the other hand, assigned blame to attempts at unifying theories of intelligence: “There is no one best way to represent knowledge or to solve problems, and the limitations of current machine intelligence largely stem from <br />seeking unified theories or trying to repair the deficiencies of theoretically neat but conceptually impoverished ideological positions.” It is, however, difficult to unify criticisms that can move fluidly between intelligence, theories of intelligence, knowledge representation, and problem solving. These things are not all the same, and blame may be better assigned to a lack of clarity in distinguishing between these aspects of the broader issue of what constitutes intelligence. A prerequisite to embarking upon research in artificial intelligence should be a definition of the term intelligence. As noted above (Atmar, 1976), many definitions of intelligence have relied on this property being uniquely human and often reflect a highly anthropocentric view. I will argue here that this viewpoint is entirely misplaced and has hindered our advancement of machine intelligence for decades. Twenty years ago, Schank stated: No question in AI has been the subject of more intense debate than that of assessing machine intelligence and understanding. People unconsciously feel that calling something other than humans intelligent denigrates humans and reduces their vision of<br />themselves as the center of the universe. Dolphins are intelligent. Whales are intelligent.<br />Apes are intelligent. Even dogs and cats are intelligent. Certainly, other living systems can be described as being intelligent, without ascribing specific intelligence to any individual member of the system. Any proposed definition of intelligence should not rely on comparisons to individual organisms.<br />1.4 The Turing Test<br />Turing (1950) considered the question, “Can machines think?” Rather than define the terms “machines” or “think,” Turing proposed a test that begins with three people: a man (A), a woman (B), and an interrogator (C). The interrogator is to be separated from both A and B, say, in a closed room (Figure 1-1) but may ask questions of both A and B. The interrogator’s objective is to determine which (A or B) is the woman and, by consequence, which is the man. It is A’s objective to cause C to make an incorrect identification. Turing provided the following example of a question posed to the man: <br />“C: Will X [C’s name for A] please tell me the length of his or her hair?”<br />“A: My hair is shingled, and the longest strands are about nine inches long.”<br />Player A may be deceitful, if he so desires. In contrast, the object for B is to help the interrogator. Turing suggested that the probable best strategy for her is to give truthful answers. In order that the pitch of the voice or other external clues may not aid in C’s decision, a teleprinter was to be used for communication between the rooms.<br />Turing then replaced the original question, “Can machines think?” with the following: “We now ask the question, ‘What will happen when a machine takes the part of A in this game?’ Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman.” This question separates the physical and intellectual capabilities of humans.<br />The form of interrogation prevents C from using sensory information regarding A’s or B’s physical characteristics. Presumably, if the interrogator were able to show no increased ability to decide between A and B when the machine was playing as opposed to when the man was playing, then the machine would be declared to have passed the test. Whether or not the machine should then be judged capable of thinking was left unanswered. Turing in fact dismissed this original question as being “too meaningless to deserve discussion.”<br />1.5 Intelligence, The Scientific Method, And Evolution<br />Following a similar line of thought to that described above, Ornstein (1965) argued that all learning processes are adaptive. The most important aspect of such learning processes is the “development of implicit or explicit techniques to accurately estimate the probabilities of future events.” Similar notions were offered by Atmar (1976, 1979). When faced with a changing environment, the adaptation of behavior becomes little more than a shot in the dark if the system is incapable of predicting future events. Ornstein (1965) suggested that as predicting future events is the “forte of science,” it is sensible to examine the scientific method for useful cues in the search for effective learning machines. The scientific method is an iterative process that facilitates the gaining of new knowledge about the underlying processes of an observable environment. Unknown aspects of the environment are estimated. Data are collected in the form of previous observations or known results and combined with newly acquired measurements. After the removal of known erroneous data, a class of models of the environment that is consistent with the data is generalized. This process is necessarily<br />inductive. The class of models is then generally reduced by parametrization, a deductive process. The specific hypothesized model (or models) is then tested for its ability to predict future aspects of the environment. Models that prove worthy are modified, extended, or combined to form new hypotheses that carry on a “heredity of reasonableness” (Fogel, 1964). This process is iterated until a sufficient level of credibility is achieved. “As the hypotheses correspond more and more closely with the logic of the environment they provide an ‘understanding’ that is demonstrated in terms of improved goal-seeking behavior in the face of that environment”<br />It appears reasonable to seek methods to mechanize the scientific method in an algorithmic formulation so that a machine may carry out the procedure and similarly gain knowledge about its environment and adapt its behavior to meet goals. The scientific method can be used to describe a process of human investigation of the universe, or of learning processes in general.<br />The Scientific Method<br />1.6 Evolving Artificial Intelligence<br />It is natural to conclude that by simulating the evolutionary learning process on a computer, the machine can become intelligent, that it can adapt its behavior to meet goals in a range of environments. Expert systems, knowledge-based systems, neural networks, fuzzy systems, and other systems generate behavior, in the form of stimulus– response pairs, as a function of the environment. None of these frameworks, however, is intrinsically intelligent. Each is a mathematical function that takes input and yields output. Only when a learning mechanism is imposed on these frameworks is it meaningful to discuss the intelligence that emerges from such a system. Although other means for creating learning systems are available (e.g., reinforcement), evolution provides the most fundamental learning mechanism that can be<br />applied generally across each of the frameworks and combinations of these frameworks.<br />This is evidenced in all the life around us. “Intelligence is a basic property of life” (Atmar). It has occurred at the earliest instance of natural selection and has pervaded all subsequent living organisms. In many ways, life is intelligence, and the processes cannot be easily partitioned. To date, the majority of self-described efforts in artificial intelligence have relied<br />on comparisons to human behavior and human intelligence. This has occurred even<br />in the face of high-visibility failures and overextended claims and projections. Steels (in Manuel, 2003) remarked: “Most people in the field have known for a long time that mimicking exclusively human intelligence is not a good path for progress, neither from the viewpoint of building practical applications nor from the viewpoint of progressing in the scientific understanding of intelligence.” Put another way, the “artificial” is not nearly as important as the “intelligence.” It is not enough to “fake” the intelligence or mimic its overt consequences following the Turing Test. And it is simply sophistry to say that some software is an example of artificial intelligence because “people in the AI community worked on it” (Russell, in Ubiquity, 2004). Significant advancement in making intelligent machines—machines that adapt their<br />behavior to meet goals in a range of environments—requires more than simply claiming we are doing AI. Numerous opinions about the proper goal for artificial intelligence research have been expressed. But intuitively, intelligence must be the same process in living organisms as it is in machines. In the late 1980s, Genesereth and Nilsson (1987) offered: “Artificial Intelligence is the study of intelligent behavior. Its ultimate goal is a theory of intelligence that accounts for the behavior of naturally occurring intelligent entities and that guides the creation of artificial entities capable of intelligent behavior.” This was a worthy objective 15 years ago and it remains so today. The thesis promoted here is that evolutionary processes account for such intelligent behavior and can be simulated and used for the creation of intelligent machines. <br />1.7 Simulation Of Human Expertise<br />The acceptance of the Turing Test focused attention on mimicking human behavior. At the time (1950), it was beyond any reasonable consideration that a computer could pass the Turing Test. Rather than focus on imitating human behavior in conversation, attention was turned to more limited domains of interest. Simple two-person games of strategy were selected. These games received attention for at least three reasons: (1) Their rules are static, known to both players, and easy to express in a computer program; (2) they are examples from a class of problems concerned with reasoning about actions; and (3) the ability of a game-playing computer can be measured against human experts. The majority of research in game playing has been aimed at the development of heuristics that can be applied to two-person, zero-sum, nonrandom games of perfect information (Jackson, 1985). The term zero-sum indicates that any potential gain to one player will be reflected as a corresponding loss to the other player. The term nonrandom means that the allocation and positioning of resources in the game (e.g.,<br />pieces on a chess board) is purely deterministic. Perfect information indicates that both players have complete knowledge regarding the disposition of both players’ resources (e.g., tic-tac-toe, not poker). The general protocol was to examine an expert’s decisions during a game so as to discover a consistent set of parameters or questions that are evaluated during his<br />or her decision-making process. These conditions could then be formulated in an algorithm<br />that is capable of generating behavior that is similar to that of the expert when faced with identical situations. It was believed that if a sufficient quantity or “coverage” of heuristics could be programmed into the computer, the sheer speed and infallible computational ability of the computer would enable it to match or even exceed the ability of the human expert.<br />2. Future of Artificial Intelligence<br />Artificial Intelligence is often misunderstood. People’s eyes often glaze over when we begin to discuss AI. However, there is a common misconception that understanding AI requires an IQ of 200 and a PHD in rocket science. The truth is that AI when broken down to its nucleus is often nothing more than a simulation of nature and or intelligence in natural things. Nature gives us a glimpse of the genius in the rhythms and rules we live with but often take-for-granted. As briefly described below it is my belief that if engineers and developers strive to simplify the design process, they can begin to understand the concepts that will take technology to indescribable places; an ironic “catch 22.” Most people would agree that genius is clearly found in nature. However, rarely is any single creature or naturally occurring object in nature referred to as “a genius”; rather, nature reveals its intelligence in its designs, which we as Artificial Intelligence pioneers seek to replicate digitally. Commonly, researchers implement complex solutions to solve complex problems, often resulting in new and bigger problems.<br />Astonishing innovation is generally found in the simplest solutions, which are typically easy to<br />implement after you know what the solution is. Falling into the trap of over engineering is incredibly widespread today. Many engineers are not good innovators at all but play a costly game of “following the follower” in their designs. Engineers, for example, will typically examine how similar complex problems have been solved in the past and use those solutions as<br />their starting point, mistakenly assuming that the presupposition for the original design was ideal and the best methodology.<br />For example: intersections and traffic signals in many American cities are highly engineered using complex computer and AI technology to manage the flow of traffic. However this very complex solution, (electronically detecting traffic which then influences signal timing) results in one major flaw, which is, 50% of all traffic at a signaled intersection is stopped 100% of the time. Consider all the productivity lost in America because of this one overly elaborate and completely inefficient design; then juxtapose the “round-a-bout” intersection (commonly found in Europe and Australia), where all traffic merges into and out of a small circle intersection without stopping. Amazingly a roundabout is nothing more than a paved<br />circle and requires no signals or detection devices what-so-ever. Its natural and its extraordinarily simple.<br />Further, creativity in organizations is often squelched by systems evolved while smothered in<br />bureaucracy. Seldom is a corporate employee compensation plan designed to foster real innovation. For example, some large corporations compensate in-house software developers hourly, with production standards based upon how many “lines of code” are written. The reality is that “good code” requires fewer lines (and characters) to produce. In this example developers are ironically rewarded for being inefficient and lazy coders. The standard compensation plan which trades an employee’s time for money is not only old and tired, but it is fundamentally flawed. Unless the work itself is enormously exciting, the motivation is to waste time, not to innovate.<br />References
- 7. 3.1 Evolutionary Computation By David B. Fogel
- 8. 3.2 The Future of Artificial Intelligence By Andrew D. Hyder
- 9. The CARS Checklist for Information Quality
- 10. About the References Book : Evolutionary Computation(2006)<br />Dr. David B. Fogel, Chairman of the Board.Dr. Fogel's experience includes over 20 years of applying computational intelligence methods and statistical experimental design to real-world problems in industry, medicine, and defense. Prior to joining Natural Selection, Inc. in 1993, he was a Systems Analyst for Titan Systems, Inc. (1984-1988), and a Senior Principal Engineer at ORINCON Corporation (1988-1993). He received the Ph.D. in engineering sciences from the University of California at San Diego in 1992, and has subsequently taught undergraduate and graduate courses in evolutionary computation, stochastic processes, and statistical process control.<br />Document : The Future of Artificial Intelligence(2009) By Andrew D. Hyder<br />Andrew Dean Hyder – Founder, CEO, President and Chairman of the Board/Director<br />Subjex Corporation. Mr. Hyder is a pioneer in the fields of artificial intelligence (AI),<br />Capital Market Quantitative Analysis, Natural Language Dialogue (NLD), and Search<br />Engine Optimization (SEO). He is also a published author of the book “Investment<br />Catch-Phrase Fallacy: The New Risks of Traditional Investing” and several noteworthy<br />white papers on technology and futurism.<br />Mr. Hyder is the inventor of the “SCAR” ratio which calculates the “Simple–to–<br />Compounded–Accelerated–Returns” of a portfolio. This ratio is changing how the industry analyzes and measures its investment practices.<br />In the document I’ve used two latest reference documents as per their requirements. I’ve also give the definitions and facts by their time-lines. Both reference documents are written by the experts in their fields. Evidence in my document is based on the facts written by the highly experienced authors.<br />The Café Advice
- 11. Was the source authentic ?
- 12. Ans : Yes.
- 13. Have I used proper time-lines to define any particular thing ?
- 14. Ans : Yes.
- 15. Have I used some history about the topic ?
- 16. Ans : Yes.
- 17. Have I used very general language ?
- 18. Ans : No , As the topic is highly technical so there was nothing to do with general things.
- 19. Have I satisfied each and every point of the topic ?
- 20. Ans : No , As the topic was very new to me so I was unable to break-down some parts (topics) but I’d tried my best to give my best. I was week in some areas.