The term artificial intelligence was first coined by John McCarthy in 1956 when he held the first academicconference on the subject. But the journey to understand if machines can truly think began much before that. InVannevar Bush’s seminal work As We May Think [Bush45] he proposed a system which amplifies people’s ownknowledge and understanding. Five years later Alan Turing wrote a paper on the notion of machines being able tosimulate human beings and the ability to do intelligent things, such as play Chess [Turing50].No one can refute a computer’s ability to process logic. But to many it is unknown if a machine can think. Theprecise definition of think is important because there has been some strong opposition as to whether or not thisnotion is even possible. For example, there is the so-called ‘Chinese room’ argument [Searle80]. Imagine someoneis locked in a room, where they were passed notes in Chinese. Using an entire library of rules and look-up tablesthey would be able to produce valid responses in Chinese, but would they really ‘understand’ the language? Theargument is that since computers would always be applying rote fact lookup they could never ‘understand’ asubject.
Validation The quality of expert systems is often measured by comparing the results to those derived from human experts. However, there are no clear specifications in validation or verification techniques. How to adequately evaluate an expert system remains an open question, although attempts have been made to utilize pre-established test cases developed by independent experts to verify the performance and reliability of the systems. 13 Managerial and Organizational Challenges The success in technical or economic sense of an expert system does not guarantee a high-level of adoption rate or long-term use in business. T. Grandon Gill surveyed expert systems built during the early and mid 1980s . Of all the systems surveyed, the key results were as follows: about one-third were being actively used and maintained, about one-sixth were still available to users but were not being maintained, and about one-half had been abandoned.
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The survey also indicated that problems suffered by some of those machines that fell into disuse, had neither technical nor economic basis . Alignment of Technology and Business Strategy Building expert systems involves substantial amount of investments and efforts. Inconsistency between the organizations business strategy and the technology could ultimately ruin such money and efforts. Maintenance Cost of Expert Systems Maintenance cost could be high because expert systems are complicated and might require extensive knowledge of both application domain and development tools in people who develop and maintain the system. A loss of key personnel could result in a whole portion of the project to be re-worked. Projects can be abandoned or delayed if the vulnerabilities in staffing turnover are not resolved.
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Expert Systems Make Mistakes Legal concerns over expert system makes mistakes could drive investors and developers away. As mentioned previously, there is little consensus on what testing is necessary to evaluate an expert systems validity, reliability and performance. There are no legal authorities to certify and validate systems. The potential legal and financial liabilities if such systems should go wrong, especially in life-critical systems such as medical diagnosis and air-traffic control could be crippling and astronomical. Resistance from Users Unlike traditional computer programs, expert systems perform tasks that an expert performs. This could trigger potential strong opposition and resistance of such technology from users with concerns about expert systems taking their jobs. These managerial and organizational challenges appear to be very important for expert systems. Failure to take care of such issues could lead to system abandonment or cancellation.
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Is "Thinking" Machine Ever Possible Scientists have long dreamed of autonomous thinking systems that are free of human interference. Despite five decades of research, the intuitive intelligence of human beings seems to be still beyond the capabilities of reasoning machines. While some people deem the invention of a thinking machine a dangerous endeavor that is ultimately doomed to failure, others are more optimistic. Notably, Jeff Hawkins predicted in his award-winning book On Intelligence that the mind-machine may come into the world within 10 years . He reasoned, It took fifty years to go from room-size computers to ones that fit in your pocket. But because we are starting from an advanced technological position, the same transition for intelligent machines should go much faster . To explore the development potential of expert systems, it is necessary to compare the differences between human and machine thinking and discuss the possibilities for the future. 14
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