How Technology Has Changed Our Lives Essay Example For Students

How Technology Has Changed Our Lives Essay Technology has traditionally evolved as the result ofhuman needs. Invention, when prized andrewarded, will invariably rise-up to meet the freemarket demands of society. It is in this realm thatArtificial Intelligence research and the resultantexpert systems have been forged. Much of the materialthat relates to the field of Artificial Intelligencedeals with human psychology and the nature ofconsciousness. Exhaustive debate on consciousness andthe possibilities of consciousnessness in machines hasadequately, in my opinion, revealed that it is mostunlikely that we will ever converse or interract witha machine of artificial consciousness. In JohnSearles collection of lectures, Minds, Brains andScience, arguments centering around the mind-bodyproblem alone is sufficient to convince a reasonableperson that there is no way science will ever unravelthe mysteries of consciousness. Key to Searlesanalysis of consciousness in the context of ArtificialIntelligence machines are refutations of stro ng andweak AI theses. Strong AI Theorists (SATs) believethat in the future, mankind will forge machines thatwill think as well as, if not better than humans. Tothem, pesent technology constrains this achievement. The Weak AI Theorists (WATs), almost converse to theSATs, believe that if a machine performs functionsthat resemble a humans, then there mustbe a correlation between it and consciousness. Tothem, there is no technological impediment to thinkingmachines, because our most advanced machines alreadythink. It is important to review Searles refutationsof these respective theorists proposition toestablish a foundation (for the purpose of this essay)for discussing the applications of ArtificialIntelligence, both now and in the future. Strong AI Thesis Strong AI Thesis, according to Searle,can be described in four basic propositions. Proposition one categorizes human thought as theresult of computational processes. Given enoughcomputational power, memory, inputs, etc., machineswill be able to think, if you believe thisproposition. Proposition two, in essence, relegatesthe human mind to the software bin. Proponents of thisproposition believe that humans just happen to havebiological computers that run wetware as opposed tosoftware. Proposition three, the Turing proposition,holds that if a conscious being can be convinced that,through context-input manipulation, a machine isintelligent, then it is. proposition four is where theends will meet the means. It purports that when we areable to finally understand the brain, we will be ableto duplicate its functions. Thus, if we replicate thecomputational power of the mind, we will thenunderstand it. Through argument and experimentation,Searle is able to refute or severely diminish these propositions. Searle argues that machines may wellbe able to understand syntax, but not thesemantics, or meaning communicated thereby. Essentially, he makes his point by citing the famousChinese Room Thought Experiment. It is here hedemonstrates that a computer (a non-chinese speaker,a book of rules and the chinese symbols) can fool anative speaker, but have no idea what he is saying. Byproving that entities dont have to understand whatthey are p rocessing to appear as understanding refutesproposition one. Proposition two is refuted by thesimple fact that there are no artificial minds ormind-like devices. Proposition two is thus a matter ofscience fiction rather than a plausible theory A goodchess program, like my (as yet undefeated) Chessmaster4000 Trubo refutes proposition three by passing aTuring test. It appears to be intelligent, but I knowit beats me through number crunching and symbolmanipulation. The Chessmaster 4000 example is also anadequate refutation of Professor Simons fourthproposition: you can understand a process if you canreproduce it. Because the Software Toolworkscompany created a program for my computer thatsimulates the behavior of a grandmasterin the game, doesnt mean that the computer is indeedintelligent. Weak AI Thesis There are five basic propositions thatfall in the Weak AI Thesis (WAT) camp. The first ofthese states that the brain, due to its complexity ofoperation, must function something like a computer,the most sophisticated of human invention. The secondWAT propositionstates that if a machines output, ifit were compared to that of a human counterpartappeared to be the result ofintelligence, then the machine must be so. Propositionthreeconcerns itself with the similaritybetween how humans solve problems and howcomputers do so. By solving problemsbased on information gathered from their respectivesurroundings and memory and by obeyingrules of logic, it is proven that machines canindeed think. The fourth WATproposition deals with the fact that brains are knownto havecomputational abilities and that aprogram therein can be inferred. Therefore, the mindisjust a big program (wetware). Thefifth and final WAT proposition states that, since themind appears to be wetware, dualismis valid. Proposition one of the Weak AI Thesisis refuted by gazing into the past. People havehistorically associated the state ofthe art technology of the time to have elements ofintelligence and consciou sness. Anexample of this is shown in the telegraph system ofthelatter part of the last century. Acid Rain (3433 words) EssayModern neural network systemproperties include a greatly enhanced computationalabilitydue to the parallelism of theircircuitry. They have also proven themselves in fieldssuch asmapping, where minor errors aretolerable, there is alot of example-data, and whererulesare generally hard to nail-down. Educating neural networks begins byprogramming a backpropigation of error, which isthe foundational operating systemsthat defines the inputs and outputs of the system. Thebest example I can cite is the Windowsoperating system from Microsoft. Of-course,personal computers dont learn byexample, but Windows-based software will not runoutside (or in the absence) ofWindows. One negative feature of educatingneural networks by backpropigation of error is aphenomena known as, overfitting. Overfitting errors occur when conflictinginformationis memorized, so the neural networkexhibits a degraded state of function as a result. Atthe worst, the expert system maylock-up, but it is more common to see an impeded stateof operation. By running programs inthe operating shell that review data against a database, these problems have beenminimalized. In the real world, we are seeing anincreasing prevalence of neural networks. To fullyrealize the potential benefits ofneural networks our lives, research must be intenseandglobal in nature. In the course of myresearch on this essay, I was privy to severalinstitutions and organizationsdedicated to the collaborative development of neuralnetworkexpert systems. To be a success, research anddevelopment of neural networking must address societalproblems of high interest andintrigue. Motivating the talents of the computingindustry willbe the only way we will fully realizethe benefits and potential power of neural networks. There would be no support, naturally,if there was no short-term progress. Research anddevelopment of neural networks must beintensive enough to show results before interestwanes. New technology must be developedthrough basic research to enhance the capabilities ofneural net expert systems. It isgenerally acknowledged that the future of neuralnetworks depends on overcoming manytechnological challenges, such as datacross-talk (caused by radio frequency generation ofrapid data transfer) and limited databandwidth. Real-world applications of theseintelligent neural network expert systems include,according to the ArtificialIntelligence Center, Knowbots/Infobots and intelligentHelp desks. These are primarily easily accessibleentities that will host a wealth of data and adviceforprospective users. Autonomous vehiclesare another future application of intelligent neuralnetworks. There may come a time in thefuture where planes will fly themselves and taxiswill deliver passengers without humanintervention. Translation is a wonderful possibilityof these expert systems. Imagine theability to have a device translate your English spokenwords into Mandarin Chinese! This goesbeyond simple languages and syntacticalmanipulation. Cultural gulfs inlanguage would also be the focus of such devices. Through the course of Mind andMachine, we have established that artificialintelligencesfunction will not be to replicate theconscious state of man, but to act as an auxiliary tohim. Proponents of Strong AI Thesisand Weak AI Thesis may hold out, but the inevitablewill manifest itself in the end. It may be easy to ridicule thoseproponents, but I submit that in their research intomakingconscio us machines, they are doing thefield a favor in the innovations and discoveriesthey make. In conclusion, technology will prevailin the field of expert systems only if the philosophybehind them is clear and strong. Weshould not strive to make machines that may supplantour causal powers, but rather onesthat complement them. To me, these expert systemswill not replace man they shouldnt. We will see a future where we shall increasingly findourselves working beside intelligentsystems.