Synthetic data is an ecosystem for perfect data, showing promise in creating more capable and ethical AI models.
“Is there data, and is it of sufficient diversity and quality to address my specific need?”
This is the question that many of today’s data and technology leaders have when creating a modern data architecture to support their company’s digital and AI transformations. While data may be the foundation for any AI project, there isn’t a clear-cut answer for how much of it you need to ensure a target performance. The difficulties associated with enterprise adoption could pose significant barriers to realizing the benefits of AI.
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Facing the problem: Traditional approaches are fundamentally limiting
A single dataset may contain tens of millions of elements. With traditional approaches to AI projects, organizations are tasked with manually collecting and labeling data of this magnitude, which is time-consuming and costly, not to mention prone to human errors. This method has major disadvantages, as humans cannot label all the attributes a company may be interested in or need to power their AI project. Aside from the above limitations, real-world data presents a growing issue surrounding ethical use and privacy. The use of real-world data is only becoming more prohibitive as each country individually establishes compliance laws around data collection, data storage and more.
As we look to a world of advanced innovation in autonomous vehicles, robotics, augmented reality and virtual reality, it’s clear that we’re fundamentally limited by the traditional approaches we’ve used for training AI.
Exploring the solution: Synthetic data and its benefits
Synthetic data, or computer-generated data that serves as an alternative to real-world data, has the potential to change the current AI development paradigm and disrupt traditional data-to-insight pipelines. Synthetic data shows promise in its ability to fill the gaps with data-centric approaches and deliver comprehensive training data at a fraction of the cost and time of current practices. By merging technologies from the visual effects industry and generative neural networks, synthetic data delivers perfectly labeled, realistic datasets and simulated environments at scale — meaning data scientists can use it to overcome a massive barrier to entry.
Since synthetic data is generated artificially, it eliminates many biases and privacy concerns with traditionally collecting data sets from the real world. Additionally, information about every pixel is explicitly known, and an expanded set of labels are automatically generated. This enables systems to be built and tested virtually and allows AI developers to iterate orders of magnitude more quickly since training data can be created on-demand. As a result, synthetic data will ease the complex landscape of accelerated time-to-market schedules by providing engineers with early insights into reducing costs and risks, improving delivery schedules and bolstering competitive advantage with AI for rapid prototyping and rolling out more innovative products.
Despite being a nascent technology that’s only beginning to scratch the surface with enterprise adoption, synthetic data holds great promise in its ability to disrupt the AI paradigm as we know it. The ability to test a greater number of possible design iterations at the process’s onset allows organizations to work out any complications early on when changes are far less costly. Synthetic data also directly addresses potential privacy and regulatory concerns. Leading Fortune 50 companies are embracing synthetic data and a broader wave of adoption within the industry is expected. In other words, synthetic data’s simulation-driven design has the power to flip the AI development process on its head.
Yashar Behzadi is an experienced entrepreneur who has built transformative businesses in AI, medical technology, and IoT markets. Now the CEO at Synthesis AI, he has spent the last 14 years in Silicon Valley building and scaling data-centric technology companies.