Measuring Artificial Intelligence - Symbolic Artificial Intelligence vs Connectionist Artificial Intelligence - Essay Example

Once talking about the possibilities of creating ‘thinking machines’, Turing properly said that this task should start by defining of what is understood as ‘machines’ and of what is ‘think’. Regarding the pursuit of modeling intelligence, two large avenues were opened by researchers in almost the same epoch: Symbolic Artificial Intelligence – SAI – and Connectionist Artificial Intelligence – CAI – based respectively on symbols and rules and in artificial neurons. It seems that the time is come to start thinking (and acting) to establish a standard of comparison, that could objectively tell how far we have gone along the road of constructing ever better AI systems. Devising an Intelligence Quotient IQ – for machines or any intelligent system would be, perhaps, advancement but unfortunately, the history of the development of techniques to measure human IQ, the first source checked to find applications to AI, points to a very fuzzy zone. Admitting that possibility, we present some hypothesis. For example, introducing some metric to evaluate the redundancy of the rules of an intelligent System, or the efficiency of a given topology in an artificial Neural Network could bring new insights on ranking AI paradigms and indicate which the most promising ones are. On one hand, if we look back a bit more seriously on the development issues of Science and Technology in this age, the twenty first century, it appears to be full of promises as well as perils. The previous century experienced fantastic advances in Science and spectacular technological achievements and ground breaking discoveries in medicine and health care. The current century is on the verge of even greater and more prolific things to come in terms of artificial intelligence and related disciplines. Genetics, Molecular Biology and Biotechnology are some of the related disciplines in artificial intelligence. Robotics and Artificial Intelligence are closely related to duplicating complex animal and further claiming of making systems and machines that not only think, but possess feelings and emotions as well. On the other hand, we very well know the results of Science and technological Advancement have been a blessing but also have come with some blemish. The fast and continuous development in this discourse of Science and Technology has filled us with promises and perils; challenges as well as opportunities. Lately, Artificial Intelligence and other disciplines – like Nanotechnology, Neurophysiology, Mathematics, Computer Science etc – of cognition sciences are making great and in a manner of speaking convoluted claims. At present it not only has evidence of the subtle relationship between mind and brain but researchers are working hard to resemble the brain processes and power to come up with intelligent behavior. The visible results or artificial life and the consistent research is, intelligent computers, for example, expert, Deep Blue , information systems like Cyborgs , robots and several other artificial intelligent systems that can perform better than the human brains at least in some contexts. All of these are for a fact great ground breaking achievements in Science and Technology and they are making human life better Cosy and comfortable. Since man got consciousness he has been seeking the power to control life and death. He wants to imitate God and struggles hard to create beings similar to himself. But many philosophical and practical difficulties have appeared. The great obstacles concerning the problems of reconstructing the human body are now apparently being progressively worked out by important advances in biological and chemical sciences, aided by modern technologies. This view is easily corroborated by the daily news in the popular media, which routinely announces new achievements, e.g. the increasing progress in the description of human genome, the ability of cloning animals and artificially generating fragments of natural tissues, among many other similar feats. Another field where modern scientific research has allocated a large amount of resources is the construction of intelligent machines. This has been accomplished along with the correlated problem of creating a human body. Two ancient problems have been elegantly solved and it could be said that these newly created beings would have AI – Artificial Intelligence. Until now, we cannot even precise what “natural intelligence” is, which means that we are still uncertain if the attribute of intelligence occurs only in humans or, according to some authors, also in animals 1. But one straightforward objective of AI, the “study and pursuit of mental faculties to be implemented with the use of computers”2 seems clear to anyone involved in this broad area of Computer Science. Then, in a first stage, we endeavor to find models that may be able to mirror what we expect by ‘intelligence’ and as a second step we try to use these models in computers systems to solve problems. Artificial Intelligence systems have increased the consistency and speed of problem solving and decision making with incomplete information giving solutions to complex issues that cannot be resolved by conventional computing. There are several categories, the most familiar are neural networks, genetic algorithms, intelligent agents and expert systems. There are new tool to monitor competence, quality and the effect of these systems. Today AI is in use in sophisticated establishments such as NASA and military bases and at some level in our homes. Experts system category systems can play chess and help in medical diagnosis since they fill the blank when human user experts are not easy to find or train and retain, or are too exorbitant. Organizations, for instance, Citibank, Fingerhut, Police Department, Insurance Companies and private security firms all use neural networks, because they emulate the way the biological human brain works. The kind of decisions neural networks are predominantly useful are those that include image or pattern recognition. This is because neural networks are able to learn from processed information. Neural networks have the ability to analyze large quantities of input data to draw characteristics and patterns in situations where the rules or logic are unknown. The neural network can be used to review loan applications, create patterns or manage user profiles of applications grouped into either: denied or approved or also for checking credit card fraud. Genetic algorithm mimics the evolutionary, survival in evaluation of the fittest process to come up with increasingly better solutions to existing problems. These algorithms essentially are optimizing systems and they find the input combinations that give the best and desired outputs. Investment companies, Business executives and Telecommunication companies take advantage of these systems. Intelligent agents are special purpose systems that are like knowledge based information systems and can accomplish specific tasks on behalf of its user group. Advantages of Artificial Intelligence These machines make stressful and complex work that would be normally performed by humans. They can complete the tasks faster than humans assigned the same tasks. Using robotics; unexplored landscape, outer space has been discovered and also rudimentary home activities have been performed. AI systems pose less danger, stress and injury to humans as the work is done by AI machines. The machines aid mentally, visually and hearing impaired humans. They can be used for games to create the atmosphere where the player doesn’t feel like they are playing against just machines. Using AI machines one can get less errors and defects in decision making. The effective use of time and resource is achieved as opposed to wasting it. Turing, in his considerations about the possibilities of building thinking machines properly said that this task should start by the definitions of what could be accepted as ‘machines’ and of what is ‘think’. In connection with the first concept, Turing himself supplies an answer: “This special property of digital computers, that they can mimic any discrete state machine, is described by saying that they are universal machines”. In this way they are able to execute any computable process, including the simulation of analog computers. But, even if the strong Turing assumption is true, the other side, that is, the concept of “thinking” appears far more complicated. Accordingly, Turing, in a scheme largely known as having the inspiration of a genius, proposed an escape solution. It is his famous test that, in all, admits as intelligent a machine that could act “as intelligently as” a human being facing the same circumstances. In fact, we are confronting here with consequences of the mind-body problem, and “a number of philosophers consider it as the most difficult of all human problems, that is, the relation between our minds and the universe ... and its modern version generally has the form of the question: how does our mind relate to our brain?”3. A set of different strategies are adopted to shed some light on this issue, and Psychology has knowledge areas like Psychometrics that, by supposing that the mental abilities are measurable (a matter not universally settled), works with tests to quantify them. From those studies come some insights regarding possible measures of AI, which will be discussed in the sequence. The availability of a simple and reliable instrument to measure AI would be of great value since it would furnish an important comparative instrument to evaluate intelligent software and hardware systems, or in a more general expression, intelligent machines. The development of an index that yields the intelligence degree of such a system would permit, for instance, to make decisions about which are more adequate, efficient, or cheaper among a number of machines under appraisal. Investigation on how to determine this index might bring new ideas and new directions to the development of applications in the AI area and others. But the examples regarding the Psychology field offer no reason to optimism, even showing it is not a simple task. With the difficulties already pointed out for the “human IQ”, speaking about an IQ for machines should be thought in a loose sense, having in mind that attributing intelligence to machines is still a high controversial issue. Is that measurement possible? Denying it beforehand is embarrassing, on account of the small amount of knowledge currently available on the theme. Moreover, a formal, classic inductive demonstration that a method of measurement is appropriate is not easy, if even possible. Perhaps one possibility would be to develop not just one, but several instruments, combining them accordingly in regard to the different AI paradigms. 5. Algebraic considerations It seems difficult in the present incipient stage of research in intelligence to define and rank the set of IQ measures of machine intelligence. Hence, this would only be possible if all the machines were working with the same problem and the same AI methodology and the measures would necessarily reflect that methodology. We could have, maybe, a complete ranking for a particular methodology, but not for all sets of IQ measures of diverse intelligent machines operating under other methodologies. In fact, the materialization of intelligence employing a certain paradigm may be rather different when using another, even if both materializations are aimed to the solution of the same problem. The set of those IQ measures would have scientific and practical interest only if their elements could be quantitatively compared to each other. These conditions seem too simple; yet one should be aware that extant systems admitted as intelligent and solving the same problem, are not easily compared 4 5. It appears that the same flaws that affect the measurement of “human IQ” are also present when we deal with the “machine IQ”. In other words, as we do not know exactly what is to be measured, other practical predicaments arise, as for example, how to measure, with what, where, when, etc. an IQ for machines. The area of AI is growing both in the effort dedicated to research and in operational products already commercialized. Perhaps some kind of objective measures of how far we have walked in the roads that presumably lead to intelligence could help. As recalled in the beginning, Turing’s ideas about intelligent machines must be seriously taken. The concepts of intelligence, machine, working capacity, as well as many others in Computer Science, are neither completely nor adequately defined. Until they are, admitting the real possibility of this achievement, the set formed by the union of measures of the IQs of machines, could not be accepted as having complete order, because we have not yet established a way to compare the basic elements of the paradigms involved. Nevertheless, we believe that the concept of intelligence, taken alone, is too abstract to be objectively measured. To make sense, it must be associated with some task. Nowadays, there is a noticeable trend in the popular literature that tries to show that there is more than one type of intelligence, and the different kinds regard specific and distinct abilities of understanding something. If we compare a computer running a program with a human being, maybe we could say that that the hardwired part of that machine would correspond to the genetic code of the human, while the software would stand for the acquired knowledge (training, education) the biological component. So, like humans, perhaps computers also have different intelligences. Works Cited Zohar and I.N. Marshall (2000). “SQ: Connecting with our Spiritual Intelligence”. New York: Bloomsbury Pub. E. Cherniac and D. McDermott .(1985). Introduction to Artificial Intelligence. Massachusetts: Addison- Wesley. A.M. Turing. (1984). Computing Machinery and Intelligence. Vol. 59, no.236, p. 433-460. Churchland, P. S., Neurophilosophy. (1986). Toward a Unified Science of the Mind/Brain, Cambridge, MA: MIT Press/ A Bradford Book. A .Newell. (1982). Physical Symbol Systems: Cognitive Science. Vol. 18, no. 3, pp. 87-127. J. Falqueto, J. M. Barreto and P.S.S. Borges.(2000). “Amplification of perspectives in the use of Evolutionary Computation”. IEEE Symposium on Bio-Informatics and Bio Engineering, Arlington, VA. C. R. Darwin. (1859). On the origin of species by means of natural selection .Connecticut: Grolier Enterprises Corp D. R. Tveter (1998). The pattern recognition basis of Artificial Intelligence. Los Alamitos, CA: IEEE Computer Society Press. E. Post. (1943). Formal reductions of the general combinatorial problem. New York: American Journal of Mathematics, vol. 65, no. 4, pp.197-268. Read More