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. Because specification descriptions will still be huge, specification languages will use knowledge representation techniques (Subsection 3.7) to support modularity, hierarchical structure, and multiple inheritance. The workshop included a presentation by NSF of IITA program goals and a brief discussion of a report aimed at identifying important AI research thrusts that could support the development of twenty-first century computing systems. Similar applications include building intelligent agents that provide current awareness services, alerting users to new web pages of special interest, or providing "What’s New" services for digital libraries. Artificial intelligence is here, and it's fundamentally changing medicine. Cooperative: Instead of the passive-aggressive error messages that are currently given in response to incorrect or incomplete specifications, intelligent agents should collaborate with the user to build an acceptable request. Other important questions--how to select and combine appropriate interface modalities automatically, and how to model and exploit discourse structure in multimodal interfaces--have scarcely been considered. A small number of systems have demonstrated the ability to exhibit a handful or more of the key capabilities in real time. Several knowledge representation research directions have the potential for exceptional payback for NII infrastructure and applications. Because these issues prove important in virtually all parts of AI, progress on them offers great advantage to the whole field. 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. 10, No. For the NII to be accessible to all citizens, dramatic improvements must be made in the design of user interfaces (This point is elaborated in Subsection 2.1). Second, AI representations and techniques can support the development of a flexible infrastructure. Replacing HR. A general, robust, three-dimensional fax capability is beyond current technology. In addition to providing enhanced customization abilities, an intelligent agent-oriented interface must be adaptive. Research on collaboration has led to representations of the information agents must establish and specifications of the information agents must communicate in order to collaborate. Entertainment 8. Negotiation will be crucial for resolving conflicts in goals, information and results, and negotiation algorithms must take into consideration tradeoffs between the time spent searching for appropriate agents and information sources, the time to access a given service, and the information quality and timeliness of information delivery. The field of AI is positioned to make substantial contributions to the NII. Read how it has affected things like personalized care, and see what a critic has to say. However, few fundamental changes have occurred since then, and computers remain intimidating to the vast majority of the population. Techniques developed for negotiation must be extended to deal with situations in which people as well as computer agents participate in the collaborative or coordinated activity. By basing these techniques on the sound foundation of probability theory, AI researchers have been able to assemble large knowledge bases in a principled way; effectively perform both predictive and diagnostic reasoning about the system; and develop control policies or plans that could enable the system to act safely, effectively, and efficiently. Knowledge representation techniques (Subsection 3.1) could enable inheritance along multiple taxonomic dimensions during index formation and allow a broker to determine which other brokers might be relevant during query processing. Knowledge representation is important to the NII because almost every intelligent computational activity depends on it to some degree. All rights reserved. Recent advances in sensors, communication and information systems have enabled transportation systems to be more intelligent. Incorporating AI technology into the next generation of computers forming the NII can help ensure that the nation’s information infrastructure is both flexible and easy to use. The field of planning develops algorithms that automatically construct and execute sequences of primitive commands in order to achieve high-level goals. Pioneering work in the automatic design of effective graphics holds much promise, but the field is still in its infancy (Subsection 3.9). In Section 2, we described intelligent agents that act as personal assistants and software brokers that support information retrieval and other advanced services. Washington, D.C.: Office of Science and Technology Policy, February 1994. Three factors conspire to confound the task of developing flexible NII support services: scale, scope, and heterogeneity. Intelligent Transport Systems (ITS) apply information and communication technologies to transport. Automatic design of informational graphics (for example, charts, maps, and scientific visualizations) is a necessary complement to natural language generation; both modalities are needed to facilitate computer applications that can explain themselves. Third, there are unsolved problems in using large bodies of knowledge effectively, including selecting relevant subsets of knowledge, handling incomplete information, and resolving inconsistencies. After an introductory chapter celebrating 70 year of publishing the McCulloch Pitts model the book consists Doing so presents a significant challenge because the NII will contain information on a multitude of diverse subjects, and data represented in a wide variety of forms, including various natural languages, digital and video images, audio, geometric computer-aided design (CAD) models, mathematical equations, and database relations. An effective and efficient integration of all the key capabilities is still a long-term project. role of intelligent systems in providing these networks with the requisite decision-making functionality is discussed. As more intelligent systems are introduced into the marketplace, it is becoming increasingly urgent to consider usability for such systems. Intelligent interfaces should accept requests in whichever modality (e.g. Because determining which duties must be paid requires reasoning about the type of merchandise, the scope of services provided, and the relevance of tax codes, AI techniques can provide significant assistance in meeting the challenges of integration and translation. The role of intelligent systems in providing these networks with the requisite decision-making functionality is discussed. Work in decision making has also been extended to the concept of meta-rationality; in planning an effective course of action the agent must take into account not only the cost of taking action but also the cost of delaying action. Intelligent test case generation, which defines software test cases as a […] Machine learning addresses two interrelated problems: the development of software that improves automatically through experience; and the extraction of rules from a large volume of specific data. Furthermore, consistency is unattainable in a large multisourced system that is being updated continually. For example, a photograph of Bill Clinton standing in front of the White House with Al Gore is indeed a picture of Bill Clinton. It will connect people to people, and help them with their jobs and tasks. Existing management aids provide some help in this direction but do not offer much assistance in representing knowledge about plans and designs or provide mechanisms for reasoning about plans and designs in flexible ways. 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. Just as the nation’s highway system would be an unnavigable maze without such services as maps, gas stations, and signposts, the NII will be unusable without a flexible system of support services. For example, the Internet Netfind service can determine a person’s email address but only if provided with distinguishing information about the person, such as his or her city or institutional affiliation. However, specification and refinement environments must support more activities than simple construction of an executable design; they must also facilitate revision and modification of descriptions, because any specification of a massively complex system will evolve with experience. Intelligent Systems Co., Ltd. (株式会社インテリジェントシステムズ, Kabushiki-Gaisha Interijento Shisutemuzu?) In contrast, NII interfaces will need to be intelligent, adjusting automatically to a person’s skills and pattern of usage. Share on. Try. What is therefore needed now--and what the field is clearly ready to provide--are significant pushes to develop both real-time versions of a wider set of the key capabilities and larger and more varied combinations of capabilities (even if not in real time). A similar problem results from naive attempts to scale current technology for information dissemination--indiscriminate advertising causes both cognitive and network congestion. Ontologies are broad , in that they cover a wide range of phenomena and situations. Covid-19 has increased the demand for video analytics and not least for people counting applications. In the near future, some computer applications might even use a tightly coupled combination of image understanding and synthesis. For example, a main result from research on the combination of learning and planning is that the acquisition of rules intended to improve the speed of the planner usually has the inverse effect unless the combination is done just right. Finally, system support environments can support evolutionary design by acting as an intelligent project coach (Subsection 3.5) that records (Subsection 3.1) and explains (Subsection 3.8) choices behind design decisions . In the development of learning systems it is necessary to take into account both individual needs and requirements, as well as the resources of information technologies. The generation of plans of action for using NII services is a special case of automatic programming in which the programs (plans) involve loops and conditional branches, with primitive statements couched in terms of basic commands to local and remote networking and database servers. They will have to make plans with radically incomplete and possibly out-of-date information. An additional NII-related topic is social learning methods. For example, to support continuing conferences among participants with different interface capabilities, an environment must be able to translate "utterances" between different modalities (Subsection 3.9) and generate summaries to quickly bring offline participants up to date (Subsection 3.8). The field of multiagent coordination has studied the problem of endowing agents with the ability to communicate with each other to reach mutually beneficial agreements. A natural metaphor for such an interface is a software agent, an intelligent agent (i.e., an entity capable of autonomous goal-oriented behavior in some environment, often in the service of larger-scale goals external to itself) that acts as a personal assistant to the user. No foolproof algorithms exist for answering questions or extracting conclusions from natural language documents. Nonetheless, these two areas are currently among the most dynamic in the field of AI, and the state of the art is constantly changing. After an introductory chapter celebrating 70 year of publishing the McCulloch Pitts model the book consists Whereas today’s application interfaces offer, at most, a help command or menu option, NII interfaces will increase acceptance by offering customized, intelligent help and training, especially for the nonexpert user. As a result of research in the AI community, such capabilities are becoming technically feasible: given a controlled environment, existing computer vision algorithms (Subsection 3.9) can recognize eye and lip movements as well as hand gestures. More complicated agents include human beings and organizations of human beings (such as firms). An ontology for finance, for example, would provide working definitions of concepts like money, banks, and stocks. Because organizations (and virtual communities) will likely be composed of a large and diverse collection of individuals, tools could inform users of recommended policies, procedures, and processes as well as facilitate the evolution of these guidelines and agreements. The structural (or symbolic) component indicates dependencies or independencies among system components; the numeric component quantifies the extent of the dependency, the strength of belief in a relationship, or the relative likelihood of various results. Efforts are under way to create ontologies for a variety of central commonsense phenomena, including time, space, motion, process, and quantity. Data that are similar in content can vary greatly in form and in the operations that can be performed on them. However, because of the NII’s potential size, complexity, and rate of change, intelligent software systems will initiate a large fraction of network activity. Instead of forcing a user to remember how and where to access information, NII computers need to understand a user’s task, guide him or her to the correct place, and show the user what he or she wants. The polynomial-time inference and classification schemes of knowledge representation (Subsection 3.1) provide the desired functions, but multiple taxonomies must be supported, and classification schemes must allow evolution over time. (Subsections 3.1 and 3.7). Although much data-format conversion and some level of application-system interoperability are achievable through the development of standards (e.g. Populating a virtual environment with seemingly intelligent agents will require substantial advances in all areas of AI, especially the real-time issues of agent architecture (Subsection 3.5). In some cases, however, the best interface will be one that gives the impression of directly manipulable, three-dimensional space; the contribution of AI to these virtual-reality interfaces are described in Subsection 2.1.4. Furthermore, current interface capabilities that provide formatted data must be supplemented by automatic explanation systems that consider the background, abilities, and interest of the requester. Finally, the capabilities of current systems must be expanded to encompass the capability of communicating with, and reasoning about, mental attitudes, including the development of semantics and algorithms for introspective reasoning (reasoning about the agent’s own mental states) and social reasoning (reasoning about the mental states of other agents). This report recommends several basic research initiatives in AI, each of which has high potential for large payback to the NII endeavor. First, methods must be devised for automatic construction of network structures from fragmentary input, especially input involving a combination of formal specifications, natural language databases, raw statistical data, or existing databases or knowledge sources. A sampling of tasks such agents could perform illustrates the promise and challenge of multiagent coordination and collaboration. Many systems are currently deployed throughout every sector of society; machine learning can help reduce the amount of labor needed for both development and maintenance. Techniques for organizing ontologies, combining smaller ontologies to form larger systems, and using this knowledge effectively are all in their infancy. Plausible reasoning techniques are especially appropriate for National Challenge application areas such as health care, but are applicable to the information infrastructure as well. Natural language processing research has resulted in several significant achievements, including the following: techniques for parsing, semantic interpretation, and discourse modeling sufficient to process realistic database queries posed in natural language; the reuse of language-understanding techniques for generation of reports customized to context, task, and user; statistical models of speech acoustics, word pronunciation, and word sequencing that are sufficiently accurate to support usable speech understanding of restricted vocabulary utterances; speech-generation systems that can generate spoken language with intonation contours that begin to conform to and reflect intended meaning and underlying context; machine-translation systems that can improve the efficiency of human translators by providing useful first drafts; and content-based retrieval systems that can glean useful information from unstructured text documents. 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