AI Articles and Analysis about Data analysis

Although not often regarded as a technological first-mover, the insurance industry has recently seen robust, even rapid, adoption and deployment of AI capabilities, particularly in those related to natural language processing (NLP). 

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. 

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.

Actionable, decision-augmenting data can be obtained internally or externally. Of course, external data is a far richer and more diverse source, as it comprises every other piece of digital information outside of the four walls of an enterprise. 

Meta Platforms Inc. (herein, “Meta”), was known as Facebook up until 2021. Mark Zuckerberg states that the new brand embodies his strategic plan to create a “metaverse” for its customers using AI and VR technology.