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AI, Machine Learning Rising In The Enterprise

AI and machine learning are graduating from science fiction to reality. It's estimated that about half of large enterprises are currently experimenting with AI projects. Several vendors, including Facebook, Google, IBM, and Microsoft, have donated machine learning development projects to open source.

What Is AI?

"The definition of AI is very broad, and what goes into AI varies a lot," Ram Sriharsha, senior architect and machine learning expert at big data Hadoop distribution company Hortonworks, told InformationWeek in an interview. "Machine learning is a subset of AI. AI is being able to communicate, being able to plan and reason and take actions. It's also being able to learn, and that's where machine learning comes in."

AI has more broadly come to refer to computer systems that implement and encompass multiple building blocks depending upon the goal of the system. Those building blocks can be made up of machine learning projects, natural language processing, search, and other technologies.

Michael Schmidt, CEO at Nutonian, a company that bills itself as "the Robotic Data Scientist," concurs with those definitions.

"Machine learning is a small part of AI," he said. "We actually call it machine intelligence. We don't call it AI because that is such a broad term."

Nutonian applies this machine intelligence to its consulting clients' problems, often with material science. For instance, an aluminum mining and production company needed to understand the factors causing grain-sized material produced in mass quantities to lose physical strength.

The material, when used in auto or aircraft manufacturing, would break down in a year. Forensically determining the cause of the failure and how to fix it was a task that humans could perform. But it would take them a long time.

"Not one single person understands the whole process on their own," Schmidt said. "But they do a great job of collecting the data." Yet the problem was too big for humans to solve in a time-effective manner.

Big tech vendors' moves to place some of this machine learning development into open source will spur development even more and will accelerate adoption.

Open source machine learning startup H2O.ai's CEO and cofounder SriSatish Ambati told InformationWeek in an interview that the goal of open source in this instance is to get machine learning tools to a wider audience.

"We want them to feel that they can take advantage of the data they are capturing and not miss a beat," he said. "The ultimate vision is for prediction to become as powerful as search. Companies will no longer just search for what has happened, but will turn to predicting what's going to happen."

Open source also enables companies to leverage the power of the community.

"What open source is doing for them is simply to recruit the same kind of talent that a software company is able to recruit," he said. That's the pivot that will change all businesses, including the ones that are H2O.ai's clients, such as Capital One and Progressive Insurance. These companies leverage machine learning for the applications and services they offer to their customers.

"How do I build a software maker culture within a bigger business?" Ambati said. Trailblazing organizations across non-technology vertical industries are turning to apps to deliver their services and information -- information built with AI -- to customers. To do that effectively they must nurture a development culture and development talent within the organization. "That's the bigger transformation we are seeing."