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Its basic premise is that these "most interesting computational topics today" are deeply interrelated, and in some heretofore undescribed ways. The text is well crafted, and the scholarship is both broad and deep. The author is clearly a renaissance man as well as a wonderful teacher. He is equally good at succinct summaries and painting the big picture, and he makes particularly effective use of examples. Best of all is his infectious joy about his subject: The text is full of percolations of delight at the beauty of some concept or equation or at the sheer fun of hacking code.

This is the first presidential address of AAAI, the American Association for Artificial Intelligence. In the grand scheme of history, even the history of artificial intelligence (AI), this is surely a minor event. The field this scientific society represents has been thriving for quite some time. No doubt the society itself will make solid contributions to the health of our field But it is too much to expect a presidential address to have a major impact. So what is the role of the presidential address and what is the significance of the first one? 1 believe its role is to set a tone, to provide an emphasis.

The Objects and Artificial Intelligence Workshop was held on 25 August 1991 in conjunction with the 1991 International Joint Conference on Artificial Intelligence. The workshop brought together researchers in AI and objectoriented programming to exchange ideas and investigate possible avenues of cooperation between AI and objectoriented programming. The workshop dealt with both the theoretical and the practical aspects of this cooperation. AI, however, is looking for knowledge representation and programming techniques for developing complex applications and uses constructs (for example, frames) and notions (for example, classification hierarchy) that have similarities to constructs (for example, classes) and notions (for example, class inheritance) in object-oriented programming. The one-day workshop entitled Objects and AI, held in Sydney, Australia, on 25 August 1991 in conjunction with the 1991 International Joint Conference on Artificial Intelligence, was designed to investigate the differences and possible avenues of cooperation between AI and object-oriented programming.

Many people make many confusing claims about the aims and potential for success of work in AI. Much of this arises from a misunderstanding of the nature of work in the field. In this article, I examine the field and make some observations that I think would help alleviate some of the problems. I also argue that the field is at a major turning point, and that this will substantially change the work done and the way it is evaluated. I has always been a strange field.

It is generally accepted that knowledge has a contextual component. However, even if its importance is acknowledged, this contextual component is rarely represented explicitly in available knowledge representation systems and is not used in subsequent processing of knowledge. Thus, there is a gap between what is known and what is done. Acquisition, representation, and exploitation of knowledge in context would have a major contribution in knowledge representation, knowledge acquisition, explanation, maintenance, documentation, learning, human-computer communication, and validation or verification. A computational capability to understand, represent, and reason about context will be valuable for, and of immense benefit to, many AI problems.

With recent advancement in data collection and storage, we have large amounts of information at our disposal, especially with respect to the number of explanatory variables or'features'. When these features contain redundant or noisy information, estimating the functional connection between the response and these features can become quite challenging, and that often hampers the quality of learning. One way to overcome this is by finding a smaller set of features or explanatory variables that can perform the learning task sufficiently well. In this paper, we discuss feature elimination in statistical learning with kernel machines. Kernel machines (KM) are a class of learning methods for pattern analysis and regression, under transformations of the input feature space, of which the linear support vector machine (SVM) is the simplest case. In general, the term'kernel machine' is reserved for the more general version of the SVM problem with nonlinear transformation of the feature space. The popularity of these algorithms is motivated by the fact that these are easyto-compute techniques that enable estimation under weak or no assumptions on the distribution [see Steinwart and Chirstmann, 2008].