We are excited to announce that our Data Science Blogathon 19th Edition is LIVE. The Data Science Blogathon by Analytics Vidhya began with a simple mission: To bring together a large community of data science enthusiasts to share their knowledge with the world. With 4000 articles under our belt on various topics such as Data Science, Machine Learning, Deep Learning, Data Lakes, and Data Engineering published by over 700 authors who are avid data science enthusiasts, students, professionals and researchers from across the globe. We bring to the 19th edition of the Data Science Blogathon, this time with a new reward system. Choose the Data Science content you want to create and win for each published article.

Automation fears distract from the real problem: too few blue-collar workers. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Following the Great Recession, anxiety intensified over the prospect of automation causing permanent, widespread unemployment. Feeding on public alarm, a large number of studies assessed the likely impact of future automation on jobs. Although some touted the potential for job creation, others predicted catastrophic job loss. Today, after more than a decade of continuous U.S. economic expansion, the fear of automation remains entrenched in the country's psyche, dominating public discussions and political debates.

Creating an AI startup team structure is demanding in terms of time and resources needed for building a team. This post will cover 10 top tips on startup team building. Artificial intelligence is a force for businesses to reckon with. It has enough potential to reshape the way businesses approach daily workflows and manage projects. As for consumer-facing AI applications, every existing industry will soon be introduced to projects that involve one or more applications of artificial intelligence.

So, you want to scale your computing muscle to train bigger deep learning models. Can your data center handle it? According to Nvidia, which sells more of the specialized chips used in machine learning than any other company, it most likely cannot. These systems often consume so much power, a conventional data center doesn't have the capacity to remove the amount of heat they generate. It's easy to see how customers without infrastructure that can support a piece of Nvidia hardware is a business problem for Nvidia.

The dawn of the internet at the end of the 20th century marked one of the first significant shifts seen by the recruiting industry in decades. Online job boards, resume databases, and applicant tracking systems rapidly replaced Rolodexes, newspaper classifieds, cold calling, and piles of paper resumes. The abandonment of the archaic "head hunter" model significantly broadened the candidate pool and fundamentally changed the way recruiters sourced talent prospects. The digital generation of the 2000s refined this important evolutionary step. LinkedIn, launched in 2003, brought resumes and talent profiles onto a universally searchable database.