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more capital intensive than labor intensive.
AI is dependent upon high-performance computer systems, which fortunately are rapidly decreasing in price and operating costs. Industry competition should continue to drive these costs down.
Industrial interest in AI and HCI can be measured in terms of their use in industrial operations and the extent of external (usually government) funding of industrial programs.
AI practitioners and users would concur that AI has an apparently solid future in academia and a bright, expanding future in applications. Today AI is pervasive in academic institutions, although its applications are primarily in industry and government. Well-known and widespread examples of AI applications include the following: expert systems, multisystem analysis, natural language processing, pattern recognition, robotic control, and computer vision.
HCI is rapidly acquiring all of the attributes of a scientific specialty area of computer science and, according to many experts, will shortly be perceived as such. The hardware resource base for HCI is shared fairly equally between academia and industry.
The skill base for AI and HCI is currently shared fairly equally between academia and industry. Government is primarily an HCI customer rather than a research source. The diversity of the required skill base for AI and HCI makes it difficult for a single research organization to sustain the minimum skill requirements. One attractive option is for NRL to form long-term arrangements with other research groups to pool skill bases in order to meet programmatic needs. This pooling is a cost-effective means for obtaining more current skills than are often available in a government laboratory with a stable professional population and a low attrition rate. Fortunately, the growing success of multimedia networking has essentially negated any justification for "owning" all needed skills at one facility. NRL is encouraged to enter into such strategic alliances via multimedia networking to maintain the necessary diversity in its AI and HCI skill base. It is further encouraged to establish joint projects in as many research or application areas as is feasible. However, the laboratory is cautioned that cooperative networking programs need to be explicitly defined, with formal, agreed-on objectives.
General Recommendations Regarding AI and HCI
The panel made a number of observations and reached a number of general recommendations regarding AI and HCI:
NRL's research program in AI and HCI should be goal-oriented, with specific, substantive content, and not simply aimed at maintaining an "awareness" of the AI and HCI fields. The AI and HCI strategic planning process therefore would be more akin to a corporate model for planning than to an academic model.
In support of the Navy's many unique and varied operational requirements, NRL should support and promote AI as a strong research component. Linking the strategic planning process to known Navy needs for AI and HCI technology, particularly during this period of restructuring and downsizing when operational leverage is paramount, will allow NRL to better assess its research and programmatic strengths alongside those of other defense research centers and laboratories.
NRL management should manage and support AI as a recognized scientific specialty area within computer science. In doing so, it should be recognized that AI is still immature and is more "science" than engineering in nature.
NRL should accelerate its HCI programs and support capital investment in HCI as a key research area for a defense laboratory.