As AI becomes more widely adopted by a growing number of industries and applications, the demand for robust training data will grow in lockstep. However, with manual data collection already reaching its limits, the race for AI supremacy will only serve to widen the existing supply-demand gap. Simultaneously, companies such as Datagen are making it easier and less expensive to generate high-quality synthetic datasets for training computer vision (CV) AI models. The ability to generate tens of thousands of synthetic images that are tailored to the specific parameters of each application makes synthetic data the obvious solution to the limitations of traditional, manually-collected data.

This year, AI experienced a major paradigm shift in which traditional, model-centric approaches to AI development were reconsidered in favor of data-centrism, which means that data scientists are now emphasizing the quality of their training data as a determinant of performance rather than the quality of their model. This cultural shift, combined with the ability to rapidly iterate one’s dataset in a targeted, fine-tuned manner, will make 2022 the year when synthetic data becomes the most widely used training and testing solution in AI.

The Synthetic Data Revolution Will Give Birth to a New ‘Synthetic Data Engineer’ Job, Which Will Become One of the Most In-Demand Jobs

In 2022, a new job will be created: the synthetic data engineer,’ who will be a data scientist in charge of creating, processing, and analyzing large synthetic datasets in order to support the automation of prescriptive decision-making through visuals. This new profession, a natural evolution of the computer vision engineer, is already sprouting in larger companies with synthetic data teams. As more enterprises and startups require the skills to support their simulated data initiatives, the synthetic data engineer will become one of the most sought-after professionals in the AI market.

Data-Centric AI Development Will Accelerate Synthetic Data Adoption

After being dominated by model-centric approaches to development for nearly a decade, the field of AI is undergoing a paradigm shift — away from modeling and toward a data-centric approach to AI development. In short, rather than focusing on incremental improvements to one’s AI algorithm or model, researchers have discovered that improving the quality of one’s training data can optimize AI performance much more effectively. By 2021, data-centrism will have gained widespread acceptance in AI’s R&D and enterprise communities. This trend will undoubtedly continue well into 2022, with the increased emphasis on data quality serving as yet another impetus for the adoption of synthetic data.

The Technology Required to Make the Metaverse A Reality Will Grow Significantly

The recent announcement by Facebook about its foray into the metaverse is fueling the metaverse craze. Microsoft’s announcement of its own metaverse, as well as Apple’s key metaverse patent filing, is recent metaverse developments. Meanwhile, NVIDIA, another early metaverse entrant, has seen its stock price rise by 12% since Facebook’s announcement. These recent metaverse announcements are merely the opening salvos in what will undoubtedly be a heated competition to define the future of human interaction with the environment and how we manage social connections with people who live in other parts of the world.

In the rush to create the first practical, real-world applications, vendors will need to invest heavily in tools and technologies that will allow them to be the first to market and gain a competitive advantage. These consist of various hardware, software, and data solutions. Expect an increase in these investments over the next 12-18 months.

Edge Cases Will Drive Industry Demand for Synthetic Data

Edge cases are situations that are unlikely or improbable for a given AI to encounter during its operational lifetime. Although unlikely, engineers must consider these edge cases when developing and training AI applications, especially when applications involve significant risks, such as autonomous vehicles. However, the same risks that make edge case training so important in these applications also make gathering the data required for said training extremely difficult, if not impossible. Faced with this difficulty, many businesses will turn to synthetic data for their training needs. A growing number of automakers will use synthetic data to train and develop their in-cabin driver monitoring systems (DMS).

These artificial intelligence-enabled systems use computer vision to monitor drivers and issue alerts when they show signs of distraction or fatigue. Many other automakers will undoubtedly follow suit in the coming years as new EU regulations mandating DMS technologies take effect, and American manufacturers will inevitably do the same to compete. Along with work on driverless technologies, this will vastly increase and deepen the industry’s investment in the human-centered synthetic data required to train those systems.

The Supply Chain Crisis Will Worsen, But Digital Twins Will Save the Day

According to Federal Reserve Chair Jerome Powell and other experts, the global supply chain crisis will worsen in 2022. In fact, according to a recent Wall Street Journal poll of leading economists, supply chain bottlenecks are the biggest threat to growth in the next 12 to 18 months. Unpredictable weather patterns and labor shortages will exacerbate the global pandemic’s disruptions.

Digital twins, a machine learning-driven simulation of real-world objects that predicts disruptions and makes recommendations on how to avoid them, will be one such solution. To remain competitive, organizations that rely heavily on supply chains should consider investing in digital twins technology.

The common thread running through all of these forecasts is that the world’s demand for good data is increasing. And manual data collection and annotation will be insufficient to meet the impending surge in demand. Synthetic data, on the other hand, provides a quick, customizable, and cost-effective alternative that often outperforms its real-world counterpart.

The increasing demand for data in the world coincides with an increase in the demand for data professionals, both data scientists and computer vision engineers, who may well prove to be the true bottleneck preventing AI’s rise to universal adoption.