Introduction: A Simple Complex in Artificial Intelligence and Machine Learning

Many people learn from other people. But, many wise people also learn from data and almanacs because they are more objective and because they leave the inference of truths to those who are able to analyze them. This direct dependency on data seems to draw a parallel in the development of artificial intelligence in the past two decades. Statistical methods, which operate on data rather than rules, have become indispensable in the field of artificial intelligence. Statistics is undoubtedly being recognized as a form of knowledge. Statistical methods have played and continue to play an essential role in pattern recognition. Automatic learning from data, a large amount of data, as opposed to human experts, has proven to produce results that are more consistent and effective in many practical applications, particularly, when the performance of the pattern recognition system is also judged statistically (e.g. in terms of the error rate). The ever-increasing computing power at our disposal also leads to efficiency in system design and revision. We have clusters of computers — dubbed “training camps” — that churn to optimize millions of parameters needed in a large recognition system. While the need of human expertise remains strong for other reasons, automated acquisition of knowledge from a large pool of data is a task that is commensurate with the preeminence of computers. Most pattern recognition systems deal with observations of events that display randomness. Natural randomness or uncertainty that interests us is rarely stationary (as in speech) or homogeneous (as in image). Sources of uncertainty can be broadly divided into four categories: structure, production, ambience and measurement. We have the habit of assuming a particular kind of uncertainty when it comes to ambience or measurement, but we often feel a bit at a loss in dealing with the uncertainty in structure and in production. This is best illustrated by way of example in speech communication. We speak to express a notion in our mind. The exact expression in terms of the words we choose may be influenced by the mood or context of the conversation. When the sequence of words is spoken, our