Artificial Intelligence is unquestionably a sensitive topic that affects the human race. In and of itself, the word conjures up dystopian social visions in which so-called supercomputers have subjugated or even replaced the inferior species. We are pretty depressed at the prospect of a brainy machine that is more than likely superior and on par with our brain. Artificial Intelligent systems, as a refined category, are revolutionizing the support of human decision-making processes, especially in some circumstances. Coordination, data delivery, data interpretation, forecasting, improving data continuity, supplying user knowledge in more appropriate ways, quantifying complexity, and proposing a course of action are examples of this revolution. Therefore, this poses a very troubling question to ourselves that we need to answer as humanity: is artificial intelligence surpassing the human decision making?

To answer this question, we need to look at some concerns surrounding this issue. And we need to ask ourselves whether these concerns are realistic or not. Some advocates of artificial intelligence claim that such kind of apprehensions or concerns is the fallouts semantic misapprehension (Abraham, Grosan, Tran, & Jain 337). They argue that artificial intelligence cannot be likened to artificial life. Artificial Intelligence primarily denotes merely a computer which only appears to be intelligent by making a data analysis and generating a response that may be of help when it comes to human decision making. One such example is the ‘smart agents’ who ask you particular queries then give a feedback recommendations basing on your responses in a user-friendly setting (Jain & De Wilde 131). Alternative example consists of computers which can learn from errors in a limited manner, for instance, the chess program of IBM that beat Kasparov. Therefore, the essential aspect of this outlook is that computers are limited to their capacity; their intelligence is, in effect and only artificial at best.

Additionally, a more extreme view is taken by other supporters of Artificial Intelligence. Commonly branded as a ‘learning computer,’ they give a computer description which not only can respond to inputs but learn from it. The computer is in a better position to interact with its environment, make mistakes, and execute its code re-writing to tackle the resultant circumstances (Mackworth 51). Thus, this view brings the computer nearer to being equated with a human; the very recent computer is likened to a small baby that begins with a particular set amount of instruction or info, and then develops into a very rational and intelligent adult (Abraham, Grosan, Tran, & Jain 338). The computer at now is in a good position to make an analysis, reason, and in particular, terms, make an intelligent decision basing on past experiences. Such type of computers is surpassing the humans regarding decision making through desirably evaluating and formulating situations exceptionally faster enough to advise their human counterparts or sometimes implement decisions on their own. We see such kind of computers in the Wargame movies, in which it is utilized in simulating the military strategy.

Here, nonetheless, is where particular line crossing arise. To some people, the learning computer carries in itself a potential that can make it extremely intelligent compared to humans, however, to other people it’s inevitable. By looking at the far end of the spectrum, certain view considers human acumen as nothing more than an exceptionally complex computer, and find no good reason as to why computers, when they emerge to be fast and big enough, cannot advance into something of the similar kind. This position is entirely disparate from the first stance, as the computer is far much more than just a mere tool which helps us in our day to day activities. As a result, the existence of human race is reduced to being that of solely a computer which has complexly evolved (Abraham, Grosan, Tran, & Jain 337). This scenario unquestionably suggests that we are only expedient and useful till a more efficient, and a faster machine come along. This kind of situation offends and frightens several individuals and hereafter resulting to the fiction works talked about earlier.

Where is the point, then, whereby we change from innovative prospect to science fiction? Looking at the instigation of the Artificial Intelligence spectrum, it is easy for one to barely dispute the likelihood of a computer which appears to be intelligent artificially in a limited manner, as the initial prototypes of such kind of computers are already in existence (Mackworth 52). However, the learning computer poses lots of question marks, nonetheless the primary premise remains to be only a computer effecting data analysis and making decisions, the idea is not all ridiculous to many individuals (Abraham, Grosan, Tran, & Jain 337). It is the very last affirmations which create animosity between those claiming to be artificial intelligent pros and those who are contrarily labeled as anti-artificial Intelligent.

Many individuals, such as myself, who are considered as the opponents of AI (anti-AI), not at any point repudiate the likelihood or even the credibility of basic artificial intelligent which is outlined in the first two circumstances. However, the perception of a small computer advancing to a parity level with humans or rather surpassing the humans in decision making is quite a difficulty to swallow. The minute we begin or start talking about artificial Intelligent at this very level, the implication of the term changes drastically, and the subject raises some certain objections. Essentially, it narrows down to the question about the difference between humans and computers. To be considered brainy on the human level, a being first must be in a position to take in the entire inputs surrounding it, make an evaluation, and thus come up with an intelligent decision (Abraham, Grosan, Tran, & Jain 337). When it comes to doing this, a computer is quite perfect, however, let us examine the input nature. For sure, a computer can tackle numbers and make comparisons of results with the redefined or predefined right and wrong standards. The problem occurs when factors such as emotions come in and create confusion. In what way does one quantify anger, distress, and happiness? How does one enumerate love?

The argument in contrast to this typically focuses on the human itself, claiming that the capability to analyze and have sentiments is as well as a learned thing-an evolution product. If humanity was in a position to develop these proficiencies as a youngster, then what hinders computer from doing the same. At the ultimate end, there cannot be a definitive answer, as the deduction one approaches are majorly dependent on his or her opinion about the human life in the first place (Abraham, Grosan, Tran, & Jain 337). People who believe in the natural evolution of humanity they find no problem picturing a similar progression with computers, assisted by us along the way. However, those who do not support evolution asserts that human possess a spirit which is very distinct to him alone, and they just cannot resolve this certainty with such type of a computer.

In particular terms, the ability of man to create is a character which no other animal appears to have. Alongside with the copy-cat and instinct comportment, human beings they not only have the capacity to create, but a robust desire to do so (Abraham, Grosan, Tran, & Jain 337). Maybe this is why they have a desire to create the most incredible thing of all-the one that can as well create. This defining characteristic is the one that I believe renders this very last version of artificial intelligence entirely impossible.

The Machine-learning algorithms are also by far great making decisions about medical diagnostics, job opportunities, credit, personalized recommendations, and advertising among other aspects. This AI concept of machine-learning algorithm appears to be surpassing the human decision making (Abraham, Grosan, Tran, & Jain 337). Additionally, the CMU’s Quantitative Input Influence (QII) measures can offer an approximate weight of each factor in the ultimate decision. A unique feature of QII measures is that it can illustrate the decisions of the machine learning systems of large class.

It’s quite hard to describe precisely the term decision. However, every one of us largely come to an agreement that he or she has previously experienced the concept. Whether she or he exercises his or her free will or rather turns to certain causal necessity type, is another question of philosophy we do not like to argue about (Abraham, Grosan, Tran, & Jain 337). The intuitive perception of free will bestowed to human beings in deciding on between different alternatives finds is a high point of argument in this paper.

Alternatively, it is also vital to specify what we refer to while using the term “Artificial Intelligence” (AI). There are in any case two diverse views concerning Artificial Intelligence. The view compares AI to “Artificial Sciences,” or rather the science of planning, designing and developing computer-based artifacts which perform some human tasks (Fitch 67).Taking on this opinion has the benefit of kicking out most of the philosophical debates regarding the feasibility and intelligent nature of the Artificial Intelligent projects about human decision making.

This outlook of AI holds fairly a small number of links with decision up to the point that an artifact cannot correctly consider making a decision. Therefore, if there is any resolution, then ultimately has certainly beforehand been developed by the system’s designer of the system. It still applies when in any case if he or she is in a position to trace for whichever input data set, and the instructions triggered. Alternatively, the notion of “decision” is antinomy about the program idea (Abraham, Grosan, Tran, & Jain 337). Whenever a task is automated, the decision does not exist as the activities are determined depending on every likely situation that may arise.

Nevertheless certainly if an artifact is not in any way able to make any decision, its developer has in earlier times sculpted a process of decision making embedded within the system. And so this leads to a question: in what way can we prototype and program decision processes in the artifacts? The very natural response to this query is that it suffices to tell the difference on how individuals make task-decision at hand and to reproduce the same process on the computer. Thus, if say we accept a view of Artificial Intelligent as not denoting “human intelligence,” we just have to focus on the human reasoning.

The application or use of Artificial intelligence is not new to us. Modern developments have essentially made AI techniques available to a large audience as indicated by a rise the number of apps in areas such as support system’s intelligent decisions (Jain 159). The artificial intelligent is currently being applied in the decision support systems. For instance, it is used in helping the decision maker to choose among actions in stressful and real-time decision problems; giving a dynamic feedback with the intelligent agents and tackling the uncertainties in the decision glitches. Also assist by ensuring the information is up-to-date and cutting down on the information overload (Abraham, Grosan, Tran, & Jain 337). The prominent Artificial Intelligent professional corporations acknowledge the present effort by concentrating on problems and hammers. Many current illustrations from the literature are provided indicating the practical application of different artificial intelligent techniques.

An expertise system was created to directly automate the production and separation of petroleum facilities (Chan 143). Such kind of system gives access to the firm in the remote locations by automatically, gathering, transferring, and evaluating data for analysis (Abraham, Grosan, Tran, & Jain 337). This system is in a better position to screen operations, detect anomalies, and recommend actions to the human depending on the limited proficiency attained during the system development.

Also, a Case Based Reasoning (CBR) is being used in the health services in diverse areas. The recent use of the CBR is majorly in the bioinformatics whereby it supports disabled people and the elderly, biomedicine, lastly feature & case mining (Abraham, Grosan, Tran, & Jain 337). Latest developments are in the designing of CBR systems to cater for the complexity of biomedicine, integration into the clinical environment and to interact and communicate with various methodologies and approaches.

Concerted decision making and exchange of expertise can be enhanced with artificial intelligent even in complex decisions of clinical health care through integration of a social setting (Jain et al. 59). In very classy neonatal intensive care units, nurses, parents, doctors and other parties must work together in deciding as to whether to start, discontinue or continue, limit the intensive treatment of the newborn (Abraham, Grosan, Tran, & Jain 337). Therefore, the system incorporates the probable treatment aftermaths with the perspective of parents and interpretation of physicians (Tonfoni & Jain 127). It gives a communication methodology that conveys complex information in a structured way which is still customized and personalized to enhance the process of decision making.

In my opinion, it is evident then, that in the certainty, the differences the artificial intelligent proponents and those labeled to be against it are not as radical as it may appear. At any given point, the intense disagreements as to whether artificial intelligence is overtaking human decision making stem from the misapprehension of the term. Whenever that is established, the argument changes to the differences concerning personal opinions regarding nature of human and not the technical difficulties. Undeniably, the work equally will carry on in both segments of artificially intelligent, most probably the primary version of Artificial Intelligence will be actualized shortly. Though, as I have specified above, I strongly have confidence in the mission to create an artificial human, which will affect acknowledgment of the fact that there are some things which a man cannot just do.

In summary, from the arguments above, it is evident that the application or use of Artificial intelligence is not something new. Contemporary developments have essentially made Artificial Intelligent techniques available to a large audience as signposted by a rise the number of apps in areas such as support system’s intelligent decisions. The artificial intelligent is currently being applied in the decision support systems. A sophisticated category of Artificial Intelligent systems is revolutionizing the support of human decision-making process, particularly under certain settings across many sectors specifically in health care, online game betting, and business forecasting.

Works Cited

Jain, L.C., De Wilde, P. (eds.): Practical Applications of Computational Intelligence Techniques. Kluwer Academic Publishers, Norwell, 2001.

Jain, L.C. (ed.): Electronic Technology Directions Towards, vol. 1. IEEE Computer Society Press, USA, 2000.

Tonfoni, G., Jain, L.C. (eds.): Innovations in Decision Support Systems, Advanced Knowledge International, Australia, 2003.

Abraham, A., Grosan, C., Tran, C., Jain, L.C.: A Concurrent Neural Network-Genetic Programming Model for Decision Support Systems. In: Proceedings of the Second International Conference on Knowledge Management, pp. 377–384, 2005.

Jain, A., et al. (eds.): Artificial Intelligence Techniques in Breast Cancer Diagnosis and Prognosis. World Scientific Publishing Company, Singapore, 2000.

Mackworth, A.: The Coevolution of AI and AAAI. AI Magazine 26, 51–52, 2005.

Chan, C.W.: An expert decision support system for monitoring and diagnosis of petroleum production and separation processes. Expert Systems with Applications 29, 131–143, 2005.

Fitch, P.: Is there a role for Artificial Intelligence in future Electronic Support Measures? In: Proceedings of the 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Springer, Berlin, 2006.

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