Much has been written about the impact of artificial intelligence (AI) and machine learning (ML). Borrowing from the 2007 John McCarthy paper What is Artificial Intelligence?, AI is defined as: “… the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”

Machine learning “is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.”

The idea that computers can be taught to learn instead of programmed for all they need to know is a significant shift in strategy and opens the door for extremely innovative services. Artificial intelligence and machine learning allow us to sift through data at a speed unmatched by humans and to accurately learn from it.

Most of the coverage on AI and ML tends to focus on ways the technologies will impact our jobs and lives in the coming years. While self-driving cars are sure to be exciting and assisting doctors in making more informed decisions on patient care will certainly deliver value for all of us, AI is finding a home in places that most people wouldn’t expect: Inside enterprise applications and software.

Impacting the Enterprise with AI and ML

Artificial intelligence and machine learning are quickly finding a home inside of products that are often data-driven. This is especially true with solutions that focus on utilizing data for analytic insights. AI and ML are able to reduce highly dimensional data to important variables for visualization. Helping humans get to the root of the problem or to the “ah ha” moment in a quicker, easier fashion is critical for all companies that use analytics to drive differentiation and create competitive advantage.

While most business users are excited at the idea of a chart or visualization that communicates business outcomes or insights, not all users of analytic tools are capable of easily and quickly creating these views. AI can be used inside a visual analytic environment to suggest data shaping, identify variables to explore, and find underlying data patterns that are not easily seen by humans. These are important features for companies that are serious about delivering business insight and analytics to a wider, more diverse community of users. Automating these processes is extremely valuable for people who are not expert in authoring data analysis models or in using analytics platforms.

Companies looking to deliver value via machine learning and artificial intelligence can embed these sophisticated functions inside their products to help and assist a wider group of users. Register to join us on Thursday, January 11, 10:00am PST, for a live webinar, AI and Data Analytics: the Beauty and the Brains to learn more.

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