Psychology Museum, London (Randell, 1973). In 1843, Lady

Psychology is one of the main sources of artificial intelligence. The founder of the psychology, Wilhelm Wundt was focus on the thinking processes as model human thinking during his working life. Alan Turing brought new question to thinking processes with the question of “Can machines think?”. Psychology is the study of human brain and its nature, besides artificial intelligence is the branch which deals with the intelligence in machine. To understanding the intelligence of a machine its needed to compare with human intelligence, as artificial intelligence means the intelligence shown by a machine like a human being. This paper is a preliminary attempt to focus on the brief early history and development of artificial intelligence and than analyze the relation between psychology and artificial intelligence.
History of Artificial Intelligence
There were not to exist a formal, generally accepted definition for Artificial Intelligence, various suggestions for specifying the field of Artificial Intelligence have been made over the years. John McCarthy, who introduced the term in 1956, defines artificial intelligences as “the science and engineering of making intelligent machines”. Until the early 1950s, artificial intelligence was appeared in the area of fiction, legend, novel. First studies carried by Charles Babbage and his colleague the Countess Lovelace. 
The first electronic digital computers were completed in the late 1940’s. But nearly all the important functional characteristics of these computers had been invented over a hundred years earlier by Charles Babbage. When he died, in 1871, he leaved behind an huge collection of engineering drawings and documents. After his death his son Henry Babbage completed the Analytical Engine. This machine and Babbage’s engineering drawings are now in the Science Museum, London (Randell, 1973).  In 1843, Lady Lovelace published a long and detailed description of Babbage’s Analytical Engine. And in contrast to his inferences about machines can do only what we tell them to do, “She added that the question of whether such an engine could be said to think would have to remain open until they actually constructed one and tried it out” (Babbage, 1889, p.  318). Babbage and Lady Lovelace focused on building a quick chess machine in order to finance the building of the larger Analytical Engine. Afterward, two chess machines which played the endgame were constructed by Spanish inventor Leonardo Torres y Quevedo in 1915. Randell (1973) convey that while Quevedo declined to claim that his automata were actually thinking, he suggested that it is better refine definitions of that process, and that his automata could certainly do many things which were popularly classified as thinking .
After WWII, a number of people independently started to work on intelligent machines. The English mathematician Alan Turing was the first to work on AI and he gave a lecture on it in 1947. Alan Turing (1950) emphasized the artificial intelligence by following:
“I propose to consider the question, “Can machines think?” This should begin with definitions of the meaning of the terms “machine” and “think.” The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?”” 
According to McCarthy (2007) Turing was also  the first to decide that Artificial Intelligence was best researched by programming computers rather than by building machines. By the late 1950s, there were many researchers on Artificial Intelligence, and most of them were basing their work on programming computers. The intelligent machine was an idea whose time had come, and it was not only that the computer presented a medium with which such a dream could be realized. There was an alteration of events, most notably was the shift from the physicist’s notion of energy, to a new paradigm, the cyberneticist’s notion of information. And there were the continuing efforts to describe psychological and biological phenomena in mathematical terms.
Because of these overlaps, a young assistant professor of mathematics at Dartmouth College named John McCarthy suggested to his friends to work on these problems. He believe that some improvements could be made if only all of the people at work on these problems. In the summer of 1956 , McCarthy and his three friends, Marvin Minsky, Nathaniel Rochester, and Claude Shannon  submitted “a proposal for Dartmount summer research project on artificial intelligence” to the Rockefeller Foundation. The study is “to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” (McCarthy et al, 2006). This proposal was the first time the term artificial intelligence had been used officially. Minsky and McCarthy were inventing artificial intelligence, on the other hand Alan Newell and Herb Simon were using computers to simulate cognitive processes. Miller (2003) cited that Newell and Simon seems “1956 could be taken as the critical year for the development of information processing psychology in Human Problem Solving.” 
From 1957 to 1974, Artificial Intelligence developed. Computers have capability to storage more information and became faster and cheaper, and more accessible. Besides, machine learning algorithms enhanced and people became better at algorithm to apply to their problem. There were new significant devlopments as Newell and Simon’s General Problem Solver and Joseph Weizenbaum’s Eliza. They draw a road toward the goals of problem solving and the interpretation of spoken language (Copeland, p. 13). On the other hand, Artificial Intelligence researchers had to deal with two very basic limitations, not enough memory, and processing speeds. Also Artificial Intelligence research had its government funding cut, and interest draw back. It was resumed in the 1980s, with the U.S. and Britain providing funding because of catch up Japan’s new “fifth generation” computer project (Hendler, 2008). The period between 1974 and 1980 has become known as ‘The First AI Winter’. With the introduction of “Expert Systems” The First AI Winter was ended. In the 1980’s, artificial intelligence was start to rise again with two reason, an expansion of the algorithmic toolkit and a boost of funds. John Hopfield and David Rumelhart stressed “deep learning” techniques which conduce computers to learn using experience (Hopfield , 1988). On the other hand Edward Feigenbaum introduced expert systems which reproduce the decision making process of a human expert (Engelmore and Feigenbaum, 1993). The program would ask an expert in a field how to respond in a given situation, and once this was learned for virtually every situation, non-experts could receive advice from that program
In the early 1990s, Artificial Intelligence research shifted its focus to something called an intelligent agent (Russel, 2003, p. 34). These  intelligent agents can be used for news retrieval services, online shopping, and browsing the web. Intelligent agents are also sometimes called agents or bots. With the use of Big Data programs, they have gradually evolved into personal digital assistants, or virtual assistants.
Artificial Intelligence and Psychology
Cognitive psychology and artificial intelligence support the common idea that the brain is an information processing device. If it possessed the detailed description of the brain, it will enable to model this on computer. According to Ringle (1983) “as a theoretical discipline of artificial intelligence, artificial intelligence attempts to define the principles of intelligent behavior, with specific emphasis on the structural and processing constraints imposed by physical realization.” From this perspective, human performance clearly seems to be relevant to artificial intelligence research. And one would expect that close cooperation between experimental psychologists and artificial intelligence model builders would be the rule. 
From nearly three decades, artificial intelligence workers have been building and reporting on systems which employ processes which they call reasoning, understanding, problem-solving, decision-making, planning, concept-formation and so on. Ringle (1983) mention that such informal use of psychological terms leads to some problems. First, it makes comparisons between different artificial intelligence projects extremely difficult. The second problem is the nature of task specification. Artificial intelligence workers are fond of using psychological terms to explicitly link their programs to aspects of human cognition and thereby to provide them with a pre-established theoretical framework. Defining a program or task in this way is misleading because it pretends to appeal to a psychological model when in fact it does not. This not only hide the inherent lack of precision in the task specification, but it generates the third problem: implicit reference to associated cognitive properties.
Today, artificial intelligence researchers are able to create computers that can perform jobs that are difficult for persons like logic, algebra problem solving, path planning, or playing chess. However, they are still struggling with developing a computer that is capable of carrying out tasks that are simple to do for humans like perceiving their environment, assessing complex situations, and taking everyday life decisions. According to Clocksin (2003) the view of the separation of intelligence and emotion pervades our thinking about computers, with the computer representing pure intelligence unaffected by emotion has challenged more recently. Artificial Intelligence has log ignored the importance of emotions and that emotions are part of the essential foundation for intelligence. On the other hand, cognition is also all about thinking. According to Crowder (2012), “Artificial Cognition refers to how the artificially intelligent machine learns, integrates, recalls, and uses the information that it receives.”. Nowadays question is about all artificial intelligence will have emotions as humans have. Human emotions have basis on how much needs are mer or not. In other words, emotions have reaction to the mind that processed at a subconscious level. According to Crowder (2012), in the case of constraint given to Artificial Intelligence Systems would operate as needs. If the goal was to meet the constraint, the Artificial Intelligence Systems begin to feel.

A look at the history of artificial intelligence shows that while program design and description has always relied on elements of human psychology, assumptions about cognitive processes have usually been drawn from the intuitions or introspective analyses of artificial intelligence workers, rather than from empirical studies. Furthermore the acknowledgement of the fundamental role of psychology for Artificial Intelligence, the role of Artificial Intelligence for psychology, as well, covers an important position in this debate. Indeed, the conceptual and practical tools developed within Artificial Intelligence offer a stimulus for an innovative approach to some psychological topics. Nowadays, the interaction of the two disciplines progressively appears as a significant cross-fertilization in the direction of the disappearance of their respective boundaries. Psychology plays a relevant role for Artificial Intelligence in clarifying its goals and methods, Artificial Intelligence offers powerful tools to psychology in answering several different questions. The question of Alan Turing transform in nowadays to “How much machines feel emotions?”.

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