Data and knowledge management services address two common NII needs: (1) finding information that is relevant to your task or goals; (2) finding the right audience for a piece of information you have produced. These services allow information consumers to quickly locate useful facts and software resources in a huge morass of heterogeneous, distributed data.
Data that are similar in content can vary greatly in form and in the operations that can be performed on them. Integration and translation services might convert information from one format to another subject to semantic constraints. For example, a financial translation service would not just perform the unit conversion from Japanese yen into U.S. dollars, but could convert from raw cost to total cost, including import duties, taxes, and fees.
Because the NII’s information repositories will be huge and because many will evolve rapidly, it will be impossible for people, unassisted, to check the repositories for consistency. Instead, knowledge discovery services could track the creation of new databases and updates to existing repositories. These services could cross-index related topics to discover new correlations and produce summaries.
1.1.3 Powerful Development Tools
Today’s tools and programming languages make the construction of software systems tedious and error prone. Current technology provides less support than the development of the NII and ambitious National Challenge applications require. Better support would be useful at all points of a project’s life cycle: specification, design, adaptation, construction, evaluation, and maintenance. Many software development problems could be ameliorated by devising a set of powerful tools and environments (Subsection 2.3).
We envision at least three distinct kinds of tools to which AI techniques could contribute: (1) rapid prototyping systems that combine services for specifying and refining designs with modular libraries of previously developed software and world knowledge; (2) intelligent project management aids that include software to promote collaboration and distributed decision making as well as next-generation project management software capable of checking resource utilization and assisting group leaders in replanning when unexpected conditions occur; (3) distributed simulation and synthetic environments to be used by applications for education, training, and computational prototyping of products.
A substantial body of AI research has addressed both the underlying nature of intelligence and the development of engineering algorithms necessary to reproduce rudimentary machine intelligence. This research has placed the field of AI in position to make enormous contributions to NII interfaces, flexible infrastructure, and development tools as well as to National Challenge applications. However, a concerted attack on several fundamental scientific problems is required to fully realize this promise. Here, we briefly present several key subareas within AI that we believe to be especially relevant to the development of a flexible and adaptive NII; in Section 3 we describe the state of the art of each of these AI subfields and suggest promising directions for research.
Research in knowledge representation (Subsection 3.1) seeks to discover expressive and efficient methods for representing information about all aspects of the world. Knowledge representation is important to the NII because almost every intelligent computational activity depends on it to some degree. Knowledge representation systems offer the benefits of object-oriented databases and the structuring capabilities of hypertext-based libraries; they also provide increased expressiveness and more powerful algorithms for information retrieval and update.
Machine learning methods (Subsection 3.2) extend statistical techniques in order to enable systems to identify a wide range of general trends from specific training data. These methods can be used to construct interface systems that adapt to the needs of individual users, programs that discover important regularities in the content of distributed databases, and systems that automatically acquire models of the capability of new network services.
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