Here is a current article by Google's Alon Halevy, Peter Norvig, and Fernando Pereira [376 kB PDF], from the IEEE's March/April issue of Intelligent Systems. It is intelligible to a lay person, and it contrasts lucidly two broad approaches to extracting meaning from information. Though the article does not use the terms, you might describe these two approaches as taxonomic and statistical. These excerpts - the second is the concluding paragraph - give you a flavour:
"So, follow the data. Choose a representation that can use unsupervised learning on unlabeled data, which is so much more plentiful than labeled data. Represent all the data with a nonparametric model rather than trying to summarize it with a parametric model, because with very large data sources, the data holds a lot of detail. For natural language applications, trust that human language has already evolved words for the important concepts. See how far you can go by tying together the words that are already there, rather than by inventing new concepts with clusters of words. Now go out and gather some data, and see what it can do."