3.8.3 Research Opportunities
Much of the success of current natural language processing technology has come from a long, tedious process of incremental improvement in existing approaches. More work of this kind is needed to extract the best possible performance from known techniques. In addition, there are significant research opportunities in exploring new and combined approaches. For example, although statistical and machine-learning techniques in natural language processing offer broad (but shallow) coverage and robustness with respect to noise and errors, grammatical and logical techniques offer deeper analyses of meaning, purpose, and discourse structure. These two types of techniques could complement one another. The symbolic techniques might serve to specify a space of interpretation possibilities and the statistical techniques might serve to evaluate efficiently the evidence for alternative interpretations. The results of such integration will be of value to all natural language processing applications, from information extraction and machine translation to collaborative interfaces. Another way to exploit known technology maximally might be to determine how natural language processing technology can be combined most effectively with other AI and non-AI technologies to forge effective multimodal user interfaces.
3.9 Image Understanding and Synthesis
The speed with which people extract information from images makes vision the preferred perceptual modality for most people in the majority of tasks, thus implying that easy-to-use computers should be capable of both understanding and synthesizing images. One of the goals of computer-vision research is image understanding and classification. Depending on the application, the imagery to be understood might include a scanned document page, a mug shot, an aerial photograph, or a video of a home or office scene. In each instance, what it means to understand the image (or image sequence) and the ways this understanding is accomplished, can be very different.
In contrast, image synthesis is the task of generating artificial imagery; it is the goal of computer-graphics research. Again, the types of image vary greatly (for example, charts and maps, interior and exterior views of buildings, biomedical and scientific visualizations, and cartoon animations), as do the methods for generating them. In the near future, some computer applications might even use a tightly coupled combination of image understanding and synthesis. For example, a three-dimensional fax might scan and understand two-dimensional images of an object at one location; resolve structural ambiguities; compress and transmit a symbolic encoding of the object; and then synthesize a virtual three-dimensional replica that can be manipulated, molded, or even physically manufactured somewhere else. Such an application would, in effect, be sending a physical object through a computer network.
A general, robust, three-dimensional fax capability is beyond current technology. It is not easy to say in general what characterizes the hard or easy problems in computer graphics and computer vision. Although some vision and graphics problems have yielded to persistent research (such as optical character recognition and photorealistic image synthesis), others continue to challenge us (for example, general real-world scene interpretation and motion control for animated characters). Nonetheless, these two areas are currently among the most dynamic in the field of AI, and the state of the art is constantly changing.
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