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Big data to help government tighten screws on taxpayers


PREMIUM The tax authority is banking on big data analytics to help it further increase the number of taxpayers, as the state is already enjoying faster growth in revenue collection.The database on taxpayers has continuously grown since the Finance Ministry's Tax Directorate General offered a tax amnesty last year and cut taxes for small and medium enterprises (SMEs).The SME tax cut is expected to convin...

Conversational Artificial Intelligence (AI) Market is Expected to Hold the Largest Share by 2026 –, IBM, AffectLayer, SalesLoft, CallRail, etc – Construction News Portal


The global Conversational Artificial Intelligence (AI) market report contains regional and global market analyses, as well as business-based insights. The recent market research looks at the macroeconomic aspects that influence how individuals use the term Conversational Artificial Intelligence (AI) industry in different scenarios. The Conversational Artificial Intelligence (AI) industry study frequently considers main business characteristics, problems, and market structure. A Conversational Artificial Intelligence (AI) report provides you with a thorough market overview based on the most recent, accurate findings. Primary research also includes fine-tuning regional and global Conversational Artificial Intelligence (AI) industry databases and conducting interviews with leaders from global corporations.

AI SaaS is Here to Stay - ReadWrite


The pandemic didn't just change how we live but also changed how we work. We've seen differences in going to the office or school every day to getting up every day and sitting in front of a laptop became our reality. Artificial intelligence and cloud solutions were two leading technologies that were looking like the next big thing from the last two decades. And with the pandemic, this just became real. "A third of IT businesses around the world are now using Artificial Intelligence (AI)" – IBM Global AI Adoption Index.

Sylvester Normalizing Flows for Variational Inference Machine Learning

Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce Sylvester normalizing flows, which can be seen as a generalization of planar flows. Sylvester normalizing flows remove the well-known single-unit bottleneck from planar flows, making a single transformation much more flexible. We compare the performance of Sylvester normalizing flows against planar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets.