June 2021CIOAPPLICATIONS.COM8CIO InsightsCXO InsightsIn My ViewRecent Trends in Machine LearningSEARS MERRITT, HEAD OF TECHNOLOGY STRATEGY, ENTERPRISE ARCHITECTURE AND DATA SCIENCE, MASSMUTUALears is a senior leader with expertise in the areas of data science and analytics as well as enterprise and internet technology. Over the past 15 years, Sears has spent time leading and innovating in numerous industries, including healthcare, telecommunications, and financial services. Sears currently leads MassMutual's technology strategy, enterprise architecture, and data science & advanced analytics functions. Sears is a frequent speaker at industry conferences and has numerous patents and publications in the areas of machine learning, technology and financial services. Sears has been recognized as one of the life insurance industry's top 25 innovators under 40 by LIMRA and a top 100 innovator by Corinium Intelligence.Machine learning and artificial intelligence have experienced an explosion in both scientific advancement and commercial investment. As a result, any firm with a need and a desire to deploy machine learning for the purposes of business optimization or growth can easily do so. In this article, we briefly review trends in the fundamental building blocks of machine learning: technology, data collection, and algorithmic methods. We then touch on emerging social risks and emerging regulatory responses.Machine learning technology has become increasingly more specialized and has co-evolved with data collection and methods building blocks. Originally, machine learning technology leveraged the basic components of computers; memory, storage, and processors. As data sets grew in size and algorithms grew in complexity, machine learning technology became more complex. Today, machine learning systems are optimized for large data by using distributed computing architectures, and for the newest complex algorithms by using banks of graphical processing S
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