AUGUST 20208 MANUFACTURING TECHNOLOGY INSIGHTSCultivating Fit-for-Purpose AIBy Gautam Aggarwal, Senior Vice President, Operations & Technology, Asia Pacific, Mastercard (ASX ­ DHG)Artificial Intelligence (AI) is not new. These days, it would be difficult to find a business that isn't at least attempting to use AI to improve operational efficiencies, drive innovation or enhance customer experiences. However, too often the development of AI is described in the same words commonly used to reference traditional coding: "compiled," "built," and "run." This language implies that AI is built once and then adjusted now and again as needed.However, AI is, by design, incredibly dynamic, and it requires much more than a "set it and forget it" approach.Instead, developing and implementing AI-based machine learning systems requires planting algorithms in rich data soil and cultivating them. As these systems search real-time data for patterns, their insights expand with their inputs. Likewise, as their impact on businesses grow, they slowly alter their own environments.As a result, using traditional coding language when describing AI can not only mislead businesses about the realities of developing AI, it can severely limit the benefits produced by AI and machine learning technology.To avoid this, businesses need to think about this technology less in terms of `building code', and more as `cultivating a system.'Growing AIThe core utility of AI, when combined with machine learning, is to enable technology systems to turn data into meaningful insights that lead to smarter decision-making.As human society generates and stores more data (with an expected 100-fold increase over the next five to seven years), AI's ability to make use of this information will be key to unlocking its power. It will enable a world of smart cities, smart homes, smart cards, and smart goods, and will serve as the refinery for the new data generated by those technology connections.To design fit-for-purpose AI systems in this environment, businesses need to begin by connecting the critical business opportunities AI can address--whether that be automating basic workplace tasks, creating greater customer personalization or improving a company's security defenses-- with the optimal technology solution. A company needs to both understand the data that is on hand, and design an AI and machine learning system to arrive at the desired end decisions.Having organized and well-labeled data sets is key, and companies should be vigilant about protecting against bias within their AI systems. Bias, particularly in terms of the quality of inputs provided, can negatively impact the quality of insights generated.Combating DriftAs AI systems grow in size and impact, they become increasingly vulnerable to concept drift.Concept drift happens when the relationship between the inputs a model receives and its target insights change or IN MY OPINION
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