Adaptive software, speech synthesis/recognition, neural networks

A virtual world, or virtual reality, is a precise re-creation of a real-world environment via multisensory data and computer graphics that allows interaction between humans and synthesized objects. It consists of a set of multisensory devices employed as both actuators and effectors. "Virtual" is often used synonymously with computer-generated or synthetic.

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In identity verification tasks, users perform computation in order to gain access to some online content; an example is reCAPTCHA, which leverages millions of users who solve CAPTCHAs every day to correct words in books that optical character recognition (OCR) programs fail to recognize with certainty.

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Growth in the amount of new terminology and technical jargon always accompanies such changes and advances in technology. Recognizing the futility of attempts to craft comprehensive definitions in light of these rapid changes, the panel has opted to provide brief descriptions of four frequently used terms: artificial intelligence, human-computer interface, virtual worlds, and synthetic environments.

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The sixth topic in the terms of reference was facilities, and the related questions were ''What facilities are the highest priority to emphasize in furthering the unique strengths that a government laboratory brings to this field? Which facilities are the most appropriate at a university?" This topic is addressed in the presentation of each grand challenge area to the extent possible on the basis of the panel's collective experience.

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There is a range of connectionist systems, but a much smaller set of neural network models corresponding to biological neural networks. Neural models range in biological validity through the traditional biological Hodgkin Huxley models to Leaky Integrate and Fire models that miss much of the biological detail. From a computational perspective, many of the biological details may be unnecessary to accurately simulate psychological and psychophysical behaviour, and these unnecessary details come with a computational cost. One of the challenges is to identify the level of detail necessary or beneficial for simulating behaviour at higher levels.

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concurrent problem-solving effort involved, and in realistic representation and modeling. Software for complex systems also presents challenges in reasoning under uncertainty. Adaptive software presents challenges in machine learning and reasoning under uncertainty. Interface technology develops collaborative (with the user) problem solving and virtual reality. Speech synthesis and recognition present challenges in collaborative problem solving, machine learning/adaptive systems, reasoning under uncertainty, and virtual worlds. Neural networks present challenges in machine learning, reasoning under uncertainty, and neurophysiological models of cognition.

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The Journal of Biomedical Informatics (formerly Computers and Biomedical Research) has been redesigned to reflect a commitment to high-quality original research papers and reviews in the area of biomedical informatics. Although published articles are motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, imaging, and bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices, and formal evaluations of completed systems, including clinical trials of information technologies, would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report.