The power of machine learning (ML) is in the details. When combined with data, ML is a convenient, context-fuelled resource that can push human efficiency and productivity to new heights.
The fuel, in this case, is synthetic data, which we define as “artificially generated machine learning training data that mimics the characteristics of real-world phenomena.” The positives of leveraging this information are not lost on decision-makers. According to Gartner, synthetic data is expected to completely overshadow real data in artificial intelligence (AI) models by 2030, with some believing that “you won’t be able to build high-quality, high-value AI models without it.” However, with empirical insights so critical to the success of ML, a lack of a coherent data strategy could stop that momentum in its tracks.
Finding the footing to overcome those challenges can be key for organizations to unlock the true value of this innovative and impactful solution. Could synthetic data be the missing piece to pushing these ML integrations over the top?
As an analogy to understand how ML works, consider its closest counterpart: the human brain. Humans learn and retain information through repeated experiences and feedback that refine our knowledge of the world around us.
ML operates in a comparable manner through a process known as supervised learning. In this instance, the human brain is replaced by a neural network. By providing the neural network with a curated input of imagery and corresponding annotations that describe the images, the network learns to recognize patterns that can then be applied to new, similar data.
In our rapidly digitizing world, computers are becoming more than passive presenters of images or video. Through computer vision, they have become insightful interpreters, providing an understanding of the visual world around us, deriving value from aspects like:
Classification: The network assesses the contents of the image and categorizes it into predefined labels or categories.
Leveraging these capabilities to solve complex problems in real-world applications is often gated by having access to the right data. Inaccuracies inherent in manually labelled data can lead to cost overages; this illustrates the importance of every industry having ground truth data that is accurate and precise, especially in areas like healthcare. Misinformed data can yield unreliable predictions that make the adoption of machine learning solutions in a production environment unrealistic.
Synthetic data can account for these inaccuracies and help to address many of the most challenging aspects of collecting data for training ML applications.
First, it is important to properly contextualize the term synthetic data because it can often get lumped in as a generative AI offshoot. With synthetic data, real-world data is augmented or replaced with imagery generated in 3D applications using techniques common to the visual effects and gaming industries. Generative approaches and models can also be factored into the process and leveraged to complement workflows within solutions, when applicable.
The composition of these photorealistic synthetic images is informed by real-world variables and the requirements of the machine learning application. These parameters capture the diversity that is encountered in the real world in an unbiased manner which can be difficult to achieve when only using real data. Having this granular control over the data that is fed into the machine learning process can address many commonly encountered issues, such as:
Rare data: Consider safety and inspection, an industry heavily reliant on data that may not exist in the quantities needed to train an ML system. Synthetically generated imagery of these rare cases can be produced to fill in these gaps in real data. This enables ML vision systems to be trained in ways that would not be possible otherwise.
Investing in reliable machine learning can represent a significant step toward a more intuitive and proficient method of production. Every industry seeks ways to increase expediency and cost-effectiveness without sacrificing quality, and ML can be a data-driven means to that end.
Want to take a deeper look into our process for creating synthetic data? Click here to watch a video of Jacob Berrier and I presenting “Beyond Visible Light: Generating Synthetic Data in Unique Spectrums” at SIGGRAPH 2023.
And if you are ready to turn data simulations into details that power real-world solutions, contact us.
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