| |JULY 20228THEMATIC TRENDS IN ARTIFICIAL INTELLIGENCE FOR 2022By Gurbans Chatwal, VP-Technology, Fiserv Global ServicesGurbans is involved in the design of IT strategy and supervises the development of Contract Life-cycle Management software that includes machine learning methods.he broad spectrum of Artificial In-telligence (AI) technologies stretch-es from experimentation stage to es-tablished business applications. We have seen AI apps spiraling upwards in the last two years, with several companies pursuing intelligent automation to en-hance competitiveness. With the rapid adoption of AI and an increasing body of work exposing AI bias, there has also been a growing concern about the lack of accountability and explainability with AI-based decisions. Here are a few trends of growth and gov-ernance that will lead 2022.1.DataAI models in the last decade have primarily relied on large computation resources and Machine Learn-ing (ML) model optimization to train and build new AI applications. But we know how difficult it is to get `good' data to train AI models. Real world data is often disordered, inaccurate, riddled with privacy issues, and completely missing for some industries and/or demographic profiles. This leads us to three strong observations:a. We will see more companies, startups, and uni-versities offering synthetic data services. While the idea is not new - self-driving cars rely on synthetic data, for example -generation of synthetic data is easier now due to Generative Adversarial Networks (GANs) and variational encoders.b. A large body of work is being focused on mod-els that are optimized to learn through smaller data sets (e.g., transfer learning, unsupervised learning).c. `Data-centric' AI with MLOps tools will bring more discipline in collating, cleaning, and analyz-ing data before making it available through all stag-es of the AI project lifecycle.TExpert OpinionGurbans Chatwal,VP-Technology,
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