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Intelligent systems engineering (ISE) is a blanket term used to refer to a variety of Artificial Intelligence (AI) approaches, including neural networks, evolutionary algorithms, model-based prediction and control, case-based diagnostic systems, conventional control theory, and symbolic AI. The term intelligent systems engineering is most frequently used in the context of AI applied to specific industrial challenges such as optimizing a process sequence in a sugar factory. This type of engineering tends to refer to the creation of short-term, narrow-task, marketable AI, rather than long-term, flexible, generally intelligent AI.
There exist university departments in a number of countries focusing on intelligent systems engineering. Both the terminology and general philosophy of ISE derive from a blend of mechanical and electrical engineering and computer science. ISE programs frequently exist within mechanical engineering departments.
Intelligent systems are usually meant to be coupled with robotics in industrial process settings, though they may be diagnostic systems connected only to passive sensors. Intelligent systems are meant to be adaptive, to solve problems as creatively as possible with minimal human input. The field has received substantial investment from both private sectors and the military.
Intelligent systems generally follow a sequence of events in diagnosing and addressing a potential problem. First, the system identifies and defines the problem. Then it identifies evaluation criteria to apply to the situation, which it uses to generate a set of alternatives to the problem.
There is an iterative search for a solution and evaluation of potential solutions, until a choice and recommendation is made. Then, sometimes with human go-ahead required, the solution is implemented. Intelligent systems take some of the stress off humans, automatically solving the simplest of the many thousands of problems that come up in industrial process settings.
Intelligent systems engineering seeks to create sensor networks that not only take numerical readings, but also act as virtual observers, integrating sense data and making generalizations. As our technological infrastructure becomes continuously more complex, many workers welcome artificial assistance in diagnosing and solving problems.
In a way because information technology is interesting in the way that it mimics the way human beings have been gathering, storing, sharing and manipulating information since about 3000 BC. As it says above, it’s a form of problem-solving.
Of course, there still needs to be a human catalyst to get things moving, and that brings to mind downsides of IT, like copyright breaches, hackers, spam emails and spyware.
Is this a way of having a computer “think” for itself?
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