JUL-AUG 2019

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INTECH JULY/AUGUST 2019 11 COVER STORY The takeaway is clear, says data analytics software provider SAS: "If you're deploying IoT, deploy AI with it. If you're developing AI, think about the gains you can make by com - bining it with IoT. Either one has value alone, but they offer their greatest power when com - bined. IoT provides the massive amount of data that AI needs for learning. AI transforms that data into meaningful, real-time insight on which IoT devices can act." AI and machine learning Artificial intelligence uses a variety of statistical and computational techniques and encompass- es a number of terms. Machine learning (ML), a subset of AI, identifies patterns and anomalies in data from smart sensors and devices with- out being explicitly programmed where to look. Over time, ML algorithms "learn" how to deliver more accurate results. Because of this learning, "ML outperforms traditional business intelligence tools and T here is nothing "artificial" about the in- telligence that can be gleaned from the detailed monitoring of machines, pro - cesses, and the people who interact with them. Ever since the time and motion studies of the efficiency experts of the early 1900s, industrial engineers have been turning real-time data into information and decisions that could improve productivity, efficiency, and profits. With the fourth industrial revolution upon us now, artifi - cial intelligence (AI) technology is ready to go to work in ways that are not always obvious. According to a Gartner Group forecast, The Business Value of Artificial Intelligence World - wide, 2017–2025, AI and Internet of Things (IoT) "already work together in our daily lives with - out us even noticing. Think Google Maps, Net- flix, Siri, and Alexa, for example. Organizations across industries are waking up to the potential. By 2022, more than 80 percent of enterprise IoT projects will have an AI component—up from less than 10 percent today" (2018). FAST FORWARD l If you are deploying IoT, deploy AI with it. Each has value alone, but they offer greater power when combined. l Industrial AI applications fall into three categories: AI for assets, AI for processes, or AI for operational excellence and/or business agility. l When starting a pilot project, aim for fairly soft outcomes and focus on worker augmentation, not worker replacement. Term artificial intelligence Applied artificial intelligence Deep learning Machine learning Neural network Classical Modern D a t a s i z e , v a r i e t y & c o m p u t e p o w e r Past to present 1950s 1990s 2010s Present AI, around since the 1950s, is becoming a mainstream application as a result of the explo- sion in IoT data volume, high-speed connectivity, and high-performance computing. Source: SAS AI evolution timeline Here's how to understand what industrial AI can do, how IoT feeds it, and how to start a pilot project of your own By Renee Bassett

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