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Take for instance a computer that records a video and can recognize objects in the video, sure it has a data warehouse somewhere of what each object is, and if it doesn't it could add one once it "learns" what it is. But realistically how is that different from humans? Humans don't know what a color is until they learn what it is, I didn't know red was red until someone told me, and red is only red because it is generally agreed upon what the word red represents. If I see a color and tell you it's red, and an a.i.
PoolParty Semantic Suite is integrating improved machine learning capabilities to support the most critical knowledge engineering tasks. PoolParty is a semantic technology platform developed, owned and licensed by the Semantic Web Company GmbH (SWC), a provider of graph-based metadata, search, and analytic solutions. PoolParty 5.5 now represents a complete stack of standards-based technologies to be used as an enterprise middleware for cognitive computing platforms, according to Andreas Blumauer, PoolParty CEO. The product is focused entirely on supporting organizations to enter the era of semantic and cognitive computing as smoothly as possible, says Blumauer. New features include semi-automatic creation of knowledge graphs; options for integration with SharePoint, Elasticsearch, and Stardog; and the ability to maintain context aware knowledge models.
Governor Inslee's op-ed for CNBC published July 12, 2016 America's advantage in the knowledge-based economy is our human capital. Well-educated, highly-trained, creative-thinking people are essential to the most innovative companies and successful entrepreneurs in the world today. Skilled people are the currency of economic development for states in the 21st century. Why are nearly 95 percent of all the commercial aircraft in North America built in Washington state? Why is our Puget Sound region the cloud computing capital?
The purpose of this paper is to propose a method to analyze creative music expressions of a performer in South Indian Classical Music (Carnatic Music). The method performs a qualitative classification of the extracted note sequences in a Carnatic music exposition by defining pattern classes. The classification attributes the note sequences in varying degrees to each of the classes. These pattern classes, based on the formalized Carnatic music theory, help in capturing the dynamics of a performer. This provides a framework for style-based music generation.