AI Articles and Analysis about Data collection

Critical to the definition of robotic process automation (RPA) is the notion that the tasks a 'robotic' software automates are repetitive by nature, with exceptions in rare instances. While RPA cannot independently learn from and adapt to new contexts and workflow problems, it can if the RPA system is imbued with the correct AI capabilities. 

A paradigm shift is happening in the manufacturing industry. Advancement in big data and machine learning is changing traditional manufacturing processes into the era of intelligent manufacturing. The concept of what gets called "industry 4.0" encourages the use of smart sensors, devices, and machines – going beyond the motives of collecting data about production. 

The manufacturing industry has changed in recent years. Humans on the shop floor have always used their senses and experience to anticipate machine failure before it occurs. Now, AI can be used in conjunction with human expertise.

It is exceptionally difficult to get started, much less succeed, in AI adoption if one does not have at least a foundational education, particularly in those “modules” pertaining to AI capabilities, requirements, and organizational readiness. A business wanting to implement AI must understand the current state of adoption and how this current state matches up against AI readiness requirements.

While the steel industry may not be synonymous with AI adoption, steel manufacturers are not different from any other business in ensuring efficiency in their output and throughput. There are almost certainly opportunities for AI implementation across an industry worth hundreds of billions of dollars that currently lags behind in adopting AI technology.