For years, IBM’s Watson was the “face” of Artificial Intelligence (AI). “He” could play chess and Scrabble; compete on Jeopardy … and win. Watson was the personification of a future where AI would change our world for the better. AI would solve problems that mere humans could not solve. We could almost forget that it was mere humans who had created Watson, mere humans who had created his vast database of knowledge. He was and still is a giant among men.
However, Watson is literally the million dollar man. The cost of a server that meets the minimum system requirements to operate Watson is $1 million, putting ownership of Watson-level AI beyond the reach of all but the largest enterprises. Fortunately, IBM has provided access to Watson via a set of open application program interfaces (APIs) and Software as a Service (SaaS) products. Also, the seeds of AI had been sown and many entrepreneurial spirits, realizing the potential of AI, took up the AI torch and ran with it. The general concept of AI has since been developed and incorporated in many different formats and sectors.
Manufacturing is one of them.
First, let’s not confuse AI with robotics. In many factories, industrial robots have taken over back-breaking and repetitive jobs. These are physical improvements that have increased efficiency. AI is focused on teaching computers to learn in the same ways as the human brain. In a 2015 Forbes article on Artificial Intelligence and Manufacturing, Mike Collins, author of the book Saving American Manufacturing, wrote:
“Unlike digital computers with fixed architecture, the brain can constantly re-wire its neurons to learn and adapt. Instead of programs, neural networks learn by doing and remembering and this vast network of connected neurons gives the brain excellent pattern recognition.”
In the two years since that article was written, advances have indeed been made in computer software programs that can learn and adapt. For example, Microsoft has introduced Azure Machine Learning (ML) in its newest ERP software, Microsoft Dynamics 365. Azure ML is Microsoft’s newest tool for predictive and AI capabilities. ML can be used to generate product cross-sell recommendations, auto-suggesting relevant knowledgebase articles and case/ticket analysis.
In addition, Versium Predict, a predictive data intelligence modelling tool, is included in Dynamics 365. It pulls the power of Machine Learning into the hands of Dynamics 365 users and is part of what has become known as Digital Transformation.
According to Versium, Digital Transformation means re-envisioning how you:
- Engage your customers
- Empower your employees
- Optimize your operations
- Transform your products
It turns big data into actionable plans. Dynamics 365 takes advantage of the foundational capabilities of the cloud ecosystem built on Azure, including the intelligence and data tools that make knowledge available and actionable throughout a company’s business process workflows.
So, what would Watson way about the role of Artificial Intelligence (AI) in manufacturing? We think he would say: “Three cheers for American ingenuity!”
Strategic Systems Group (SSG) has been implementing and supporting manufacturing software solutions for more than 25 years. Let’s talk about where you’re at today and where you’d like to be tomorrow.
Visit us at www.ssgnet.com. Call us at 310.539.3635 or email us at firstname.lastname@example.org.