Article: Breaking down Correlation
Correlation is the first step in finding relationships between quantities and deserves some attention . Correlation is defined as the association between quantities , for eg, the sales might increase when income of people increases.
Article: Creativity Is How We Evolve
According to scientific research, creativity and the ability to think on our feet is what helped us survive and evolve into the organisms we are today. The topic is analyzed in great detail by Steven Mithen in his book, ‘Creativity in Human Evolution and Prehistory.’ Creativity and pushing the mind to understand and convey thoughts led to language. Creativity is what kept our species alive and helped us reach new evolutionary heights. With everything our creative minds can produce, why does society pressure us to pursue one thing or one focus of creativity? Why can’t we create just to create or learn new things simply to learn new things? At what point did we decide we need to master every skill we decide to adopt? Why are we looked down upon if we lose interest in the things we’ve found a creative outlet in doing in the past?
Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and uncertain observations. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF is evaluated in both synthetic processes and a simulated multi-robot warehouse, where it outperformed alternative filtering methods by exploiting passivity.
After 10 years of ImageNet, AI researchers are digging into the details of test sets and some are asking just how much knowledge has really been created with machine learning.