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If all content (subject matter) are alike, how can we expect innovation?

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Problems with alike content: 1) Homogenization of skills When a finished product reaches the market, the product development cycle goes through the touch of many divergent skilled peoples. A product can have many components which require different skill sets of different people. For example, Website Development: When you begin to develop a website, you start with designing a prototype of how it will look, which requires the expertise of graphic designers, you need the skill of programmer to code for the website, you need to be a webmaster to host your website. If you look into the depth, there is much more complexity to it. Each website has its own requirements. A prototype can be designed by using different software, such as Inkscape, illustrator and many others. Each software requires a different kind of learning and experience. Similarly, from programmer side, which language you will be using, which framework, based upon your website requirements such as speed of development, scalab
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Future of Generation Z and millennials

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The Competitive Collaboration Algorithm

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Competitive Collaboration Algorithm: It's a decentralized network where you compete to collaborate. Edge weight represents difficulty to get connected. Difficulty depends on factors such as distance, location, quality, price. We need to choose subgraph containing all the required nodes (e.g. nodes containing a,b,c,d ) that has a minimum weight or optimal weight. a,b,c,d can represent different services, whereas a1, a2 represents the same services from a different service provider. For example, 'a' can represent a student, 'b' can represent a teacher, 'c' can represent content provider such as a biology textbook and 'd' can represent a classroom or building. Similarly  'b1' represents teacher1, 'b2' represents teacher2, etc. So, For student1 (a1), the optimum subgraph or subgraph with minimum weight containing all the required services are a1,b1,c2, d2 with weight 0.5+2.3+4.2 . Usefulness of the model: 1) Prediction: One can make a pr
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