Datagen, the pioneer of domain-specific synthetic data for humans and object perception, today released its new year’s predictions for the fields of Artificial Intelligence, Machine Learning, and Computer Vision. As AI makes its way into ubiquitous adoption by a growing number of industries and applications, the demand for robust training data will expand accordingly. However, with manual data collection already at the limits of its own utility, the race for AI supremacy will only serve to widen the existing gulf between supply and demand. At the same time, companies like Datagen are making it easier and more affordable to generate high-quality synthetic datasets to train computer vision (CV) AI models. The ability to generate tens of thousands of synthetic images — customized to suit the unique parameters of each distinct application — makes synthetic data the obvious solution to the limitations of traditional, manually-collected data.

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“We’re approaching a major inflection point for the synthetic data field,” said Ofir Chakon, co-founder and CEO of Datagen. “This year, AI underwent a major paradigm shift, in which traditional, model-centric approaches to AI development were reconsidered in favor of data-centrism, which means data scientists are now placing more significance on the quality of their training data as a determinant of performance, rather than the quality of their model. This shift in the zeitgeist — combined with the ability to rapidly iterate one’s dataset in a targeted, fine-tuned way — will make 2022 the year in which synthetic data becomes the most widely used training and testing solution in AI.”

After a year of building great momentum to power the next big leap in computer vision systems, including key appointments to its executive leadership and advisory board, Datagen’s executive team have predicted the following trends to take center stage in 2022 to help organizations accelerate their AI adoption and to prepare for what comes next:

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The Synthetic Data Revolution Will Create a New ‘Synthetic Data Engineer’ Vocation to Become of the Most In-Demand Jobs
In 2022, a new position will surface — the ‘synthetic data engineer’ — data scientists who handle the creation, processing, and analysis of large synthetic datasets in an effort to support the automation of prescriptive decision-making through visuals. This new vocation, a natural evolution of the computer vision engineer, is already emerging in larger companies, where synthetic data teams have sprouted. The synthetic data engineer will become one of the most sought-after professionals in the AI market as more enterprises and startups alike will need the skills to support their simulated data initiatives. Expect to see such job postings soar and more training courses to become available, to fill the 22% rise in computer and information research scientist jobs over the next 10 years (US Bureau of Labor statistics), of which CV (and synthetic data) engineers are a subset. In addition, we will see other data-related professionals reposition themselves as synthetic data engineers to take advantage of expanding opportunities.

Data-Centric AI Development Will Fuel Widespread Adoption of Synthetic Data
After nearly a decade of being dominated by model-centric approaches to development, the field of AI is experiencing a paradigm shift — away from modeling and toward a data-centric approach to AI development. In short, rather than focusing on making incremental improvements to one’s AI algorithm or model, researchers have found that they can optimize AI performance much more effectively by improving the quality of one’s training data. Over the course of 2021, data-centrism has been rapidly gaining acceptance throughout AI’s R&D and enterprise communities. This trend will undoubtedly continue well into 2022, and the increased focus on data quality will act as yet another catalyst for the adoption of synthetic data.

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