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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Szu, H. |
| Copyright Year | 2005 |
| Description | Author affiliation: Dept. ECE, Digital Media RF Lab., Washington, DC, USA (Szu, H.) |
| Abstract | Current designs of info tech (IT) are relying on a traditional backbone. The most serious bottleneck, however in users' minds, is not the technology; rather the quality of service (QoS) for IP is not intelligent. Although our software-enhanced design is likewise constrained in hardware, but a recent breakthrough in the brain-style information science (BIS) based on the physics of unsupervised learning neural nets can enhance IP-user interface. BIS requires computational intelligence (CI), similar to human brains, as opposed to the current computer-central IT approach. Such a biological inspired approach is more general than the classical Shannon info theory for a closed equilibrium system. BIS can address a public concern to have an electronic office-mate for automatic semantic Web knowledge fusion which need (i) smarter search engine than current keyword matching. In conclusion, brain-style IS & IT is the wave of the future and shall use natural language voice interface for pairs of human-like hearing & seeing sensory inputs, which enjoy much less man-machine interface difficulty. Also it gives us human-like intelligence search engine in handling fuzzy memory fault-tolerance recall, as well as trust-worthy privacy and reliability of Web information. |
| File Size | 856729 |
| File Format | |
| ISBN | 0780393031 |
| DOI | 10.1109/ICIA.2005.1635158 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-06-27 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Information science Spine Quality of service Search engines Competitive intelligence Hardware Computational intelligence Physics Unsupervised learning Biological neural networks |
| Content Type | Text |
| Resource Type | Article |
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