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Computers can do many wonderful things. They can perform calculations millions or billions of times faster than human beings. Yet, is this all that computers can do? Just crunch numbers? Certainly present digital computers are capable of much more. In the past, computer scientists have created a great many programs that could perform tasks that people wouldn't have otherwise believed a computer could do. This is not limited to only playing chess, or proving theorems, but also programs that can hold a regular conversation with humans, understand stories and perform many other human-like tasks. Yet, there have also been very many legitimate questions whether or not the intelligence that these programs exhibit can be comparable to human intelligence. The problem lies with architecture, the way our programs are structured. Alan Turing, one of the fathers of AI, once created a theorem that stipulated that all computers (he uses the term turing machines, which can be likened to digital computers) can compute anything that is computable. If creating a human being via a digital computer was ever possible, we must first answer some philosophical questions concerning whether emotion or consciousness is computable.

There were some scientists that questioned the capability of digital computers. Citing that the architecture of digital computers would be a terrible approach to emulating human intelligence, they strove to create artificial neurons. This was based on the architecture of our own biological brains. The idea of course, failed for the mean time, since very little was known about the subject (the same is true today). The digital computer proved to be the only sufficient medium to carry out artificial intelligence (AI).

There then came another question. People doubted whether or not symbolic AI programs, which encompassed just about every program in the 1950s, could exhibit true intelligence. Some examples of Symbolic AI programs are chess-playing programs, expert systems such as theorem provers, ELIZA, STUDENT (which solves calculus problems) etc. Just about any task-center AI program is a symbolic AI program. These programs operate by manipulating symbols. The critics claimed that the architecture of these programs were not sufficient enough for intelligence. In essence, they have no "common sense" and can rarely perform tasks other than the tasks that were assigned to them. One of the early natural language programs could translate Russian to English, and vice versa. When it converted the English phrase "The spirit is willing but the flesh is weak" to Russian, then back to English, it came up with "The vodka is good but the meat is rotten". Of course, the machine translation technology that we have today has certainly improved, but many "common sense" mistakes like this are common. You may actually want to try this with the SYSTRAN translator. These critics of Symbolic AI systems were the connectionists. They in turn created the neural network architecture. Neural networks are able to draw links between meanings and thus exhibit some form of "common sense" in some situations. More generally they are based on the architecture of neurons, synapses and dendrites in brains. As much as these systems have been hyped, they have not nearly been able to replace symbolic AI systems. On the other hand, they have been very useful for things such as image recognition.

Adaptability and learning has since been almost essential to many AI programs. This follows the goal that one day our machines will function completely free of their masters, able to learn and adapt freely from the environment that they live in. Humans, and animals can adapt to their environments, so why not machines? For example, Sam Hsiung's program, IQATS (Intelligent Question and Answer Test Summarizer) is a program that asks questions and provides answers given an article or essay (for test-making purposes), it can learn to ask new questions and make new answers by memorizing the pattern of other questions and answers that could be asked (but is not already in its knowledge base). We cannot expect, for example, for a program to innately understand that a falling glass of water will break, neither can we expect to teach a program every single detail in this universe. The only plausible solution is for our machines to learn.

Whatever the approach, we are sure that although our programs may not exhibit exactly human-like intelligence, they are nevertheless intelligent. By simply looking at what our computers can accomplish today, there is no question in this fact. If our machines are getting smarter and smarter, will there once be a day that they'll take over the earth? Marvin Minsky, a greatly respected scientist believes so. According to Minsky, one day, our nanotechnology may even make us immortal. We'll be able to store our human brain's composition inside of artificial brains. Many other scientists share this view. Will there once be a day where our robots will become so evolved that our earth will be inhabited by nothing but robots. Or is it just a science fiction fantasy?