Part 1 of a 2-part series. Read Part 2.
Is there one term to describe the fantastic machines and infinite computing power built around the cloud, innovative chips, and semiconductors already in use by a growing number of companies?
“Artificial intelligence (AI) technology,” according to SAP’s senior director of advanced analytics, Chandran Saravana, on Coffee Break with Game Changers Radio, presented by SAP on May 3, 2017.
He and SAP’s senior director of Big Data initiatives, David Jonker, joined producer/moderator Bonnie D. Graham (follow on Twitter: @SAPRadio and #SAPRadio) for a lively discussion on “Machine Learning: Man vs. Machine Or Man + Machine?”
Chandran observed, “Businesses are already relying heavily on this level of AI. We’re at the top level right now. But when it comes to machine learning, you have to bridge a big gap to teach the machines how to learn – so machines can become smarter every day.”
New to the term machine learning? It describes a computer science subfield that analyzes algorithms iteratively to find hidden insights, but without being explicitly told where to look. Examples include Google’s self-driving car, Facebook’s personalized news feeds, and Amazon’s pushing relevant purchase recommendations to customers. An upcoming use case comes from financial services, where companies are combining this technology with linguistic-rule creation to detect and prevent fraud.
Machine learning offers a range of applications across every industry and line of business, from HR and marketing to finance, with use cases reflecting who you are and your mindset.
Will these innovations result in a world of man versus machine or man plus machines?
Looking back in time
David added historical perspective. “In the 18th century, machines were seen by many skeptics as taking over the world. At that time, the focus was on man plus machine, not man versus machine, but people were worried nonetheless. Of course, we can see today the dramatic, positive, impact this advancement had on our society, economy, and personal wealth in the Western world. I am a proponent of machine learning in a person-plus-machines future.”
Chandran noted the importance of adding the human quality of empathy when designing end-user experiences of a product, service, or point of interaction. “Machines can do many things, but there are always disproportionate capabilities when it comes to people. We [humans] have the cognitive abilities to think and respond to certain types of emotions. Whether you’re selling a product or service, customers are key,” he continued.
David pointed out that human brains have difficulty correlating a particular observation when assessing massive volumes of data. “This is where the machine can help the human to become smarter. You leverage the machines to understand this data, learn from it, and help humans make a better version of it.”
Call to action: embrace and adapt to machine learning
Given the pace of business, people need a machine to perform deep analysis. David acknowledged, “Machine learning can assist the human, coming back to that whole idea of person plus machine. When we talk about enabling the intelligent enterprise, it’s that combination that’s going to be so fundamental to building the future.”
But we first need a better understanding of how to manage and leverage Big Data. Chandran said, “Teaching these machines how to learn requires a lot of skills sets, such as those of data scientists and machine-learning architects and specialists.” Universities and other educational institutions are developing programs and expertise to bridge this gap.
The future of the workforce?
David observed that much of the current workforce will need to be “retooled” to take on machine learning. “An adjustment needs to happen in the workforce. The challenge is acquiring specialized skills sets and a background in science, technology, engineering, or math (STEM) … it’s a huge opportunity.”
“Machine learning capabilities need to be extended to a variety of users in the enterprise, not just the data scientist role,” advised Chandran. “Those without a STEM background need the right tools to leverage machine learning to gain the insights to build a simple, predictive model.”
David concluded, “Machine learning isn’t about theorizing in an ivory tower. It’s about empirically looking at data, inferring insights, and reflecting on it in a way that humans typically do not. If they can’t figure out how to move things across the business, organizations will not reap the full benefits of machine learning.”
As in the Industrial Revolution, the companies and pioneers who can rethink their business in fundamentally new ways will thrive.
Listen to Coffee Break with Game-Changers Radio: “Machine Learning Trends – Part 1: Enabling the Intelligent Enterprise” on demand.
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