 like a human expert who annotates or classifies terms. "While the prototype is in its infancy, it has demonstrated great potential for use in biomedicine by reading x-rays and CT scans, as well as in digital libraries, busi- ness, Web searches and the military," says Dr. James Z. Wang, an assistant professor at Penn State's School of Information Sciences and Technology and the project's lead researcher. Unlike other content-based retrieval systems that compare features of visually similar images, ALIP uses verbal cues that range from simple con- cepts such as "flowers" and "mushrooms" to higher-level terms such as "rural" and "European." ALIP can also classify images into a larger number of cate- gories than other systems, thereby broadening the uses of image databases. ALIP processes images the way people do. When someone sees a new kind of vehicle with two wheels-a seat and a handlebar, for example-it's recog- THE CRITICAL LAST INCH-INPUT AND OUTPUT TECHNOLOGIES nized as a bicycle from information about related images stored in the brain. ALIP has a similar bank of statistical models that "learn" from analyzing spe- cific image features. Other advantages include ALIP's ability to be trained with a relatively large number of concepts simultaneously and with images that are not necessarily visually similar. In one experiment, researchers trained ALIP with 24,000 pho- tographs found on 600 CD-ROMs, with each CD-ROM collection assigned keywords to describe its content. After "learning" these images, the computer then automatically created a dictionary of concepts such as "building," "land- scape," and "European." Statistical modeling enabled ALIP to automatically index new or unlearned images with the dictionary's linguistic terms. Future research will be aimed at improving ALIP's accuracy and speed. ALIP's reading of a beach scene with sailboats yielded the keyword annota- tions of "ocean," "paradise," "San Diego," "Thailand," "beach" and "fish." Although the computer was intelligent enough to recognize the high-