If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The First Conference on Artificial General Intelligence (AGI-08) was held on March 1-3, 2008, at the University of Memphis. The overall goal of the conference was to work toward a common understanding of the most promising paths toward creating AI systems with general intelligence at the human level and beyond, and to share interim results and ideas achieved by researchers actively working toward powerful artificial general intelligence.
We posit that, given the current state of development of cognitive science, the greatest synergies between this field and artificial intelligence arise when one adopts a high level of abstraction. On the one hand, we suggest, cognitive science embodies some interesting, potentially general principles regarding cognition under limited resources, and AI systems that violate these principles should be treated with skepticism. But on the other hand, attempts to precisely emulate human cognition in silicon are hampered by both their ineffectiveness at exploiting the power of digital computers, and the current paucity of algorithm-level knowledge as to how human cognition takes place. We advocate a focus on artificial general intelligence design. This means building systems capturing the salient high-level features of human intelligence (e.g., goal-oriented behavior, sophisticated learning, self-reflection, etc...), yet with software architectures and algorithms specifically designed for effective performance on modern computing hardware. We give several illustrations of this broad principle drawn from our work, including the adaptation of estimation of distribution algorithms in evolutionary programming for complex procedure learning.