Blog Categories

The term Artificial Intelligence (AI) was coined at the Dartmouth Summer Research Project in 1956 for a branch of study dedicated to finding out how to make computers perform intelligent tasks. The shared vision of the authors was to enable computers to solve real-world problems unassisted.

Artificial intelligence has transformed the digital landscape considerably, especially regarding the expansion of chatbots in the online realm. A chatbot is a computer program powered by rules and artificial intelligence that can simulate human dialogues with users via a chat interface.

Unlimited computing capacity in the cloud and real-time analytics capabilities enables manufacturers to access new insights and build systems of artificial intelligence (AI) like never before. Forward-thinking manufacturers are looking to use these capabilities to optimize their supply chain and production operations, engage their customers in powerful new ways, transform their services and products, and empower their employees through customer insights.

Artificial Intelligence has successfully permeated into most industries across the globe, be it transportation, agriculture, healthcare, manufacturing, or technical support. But now, AI also seems poised to take over human resources.

Over the past couple of years, the field of Artificial Intelligence has been expanding at a frenetic pace. It seems like every industry in the world — gaming, manufacturing, agriculture, health-care, logistics, etc — are hustling to incorporate AI solutions to enhance their customer experience and productivity.

These 3 terms Artificial Intelligence (AI), and deep learning (DL) and machine learning (ML) are related to the modern computer technology. The term “Artificial Intelligence”, is well known and very familiar and thanks to the movies like The Terminator, ex Machina etc. as they focus this term in these movies and make many people clear about AI.

It's easy to view the huge rise in popularity for Venture investment, seminars, and business-related questions for "machine learning" because of 2012 -- but many tech executives frequently have difficulty identifying where their company might really employ machine learning (ML) to company issues.