As artificial intelligence remains at the forefront of current and future technological advancements, the breadth of applications for machine learning and computer vision algorithms continues to grow as well. With the digital world producing data at an exponential rate and “big data” being a hot topic for enterprise corporations, it is vital for these businesses to find competitive advantages in this space.
As the applications of these algorithms continue to expand, so does the need for training data (dataset examples used to teach and train algorithms). In many cases, training data can be both costly and challenging to obtain. Hence, utilizing model-generated synthetic data just might be this next major competitive advantage in the artificial intelligence space.
What Is Synthetic Data?
What exactly is synthetic data? Synthetic data is system-generated data that mimics real data in terms of essential parameters set by the user. Synthetic data is any production data not obtained by direct measurement and thus is considered anonymized. Conceptually, synthetic data may seem like a compilation of “made up” data, but there are specific algorithms that are designed to create realistic data. This synthetic data will then assist in teaching a system how to react to certain situations or criteria, replacing real-world captured training data.
How To Use Synthetic Data
So why use synthetic data? Synthetic data sets have a wide variety of applications such as image processing, IoT, AI, machine learning, defense, and natural language processing. The main reasons why synthetic data is used instead of real data are cost, privacy, and testing. Producing synthetic data through a generation model is vastly more cost-effective and efficient than collecting real-world data. This especially applies to the autonomous vehicle space where real-world data can be both time-consuming and costly to collect.
With synthetic data, it becomes cheap and fast to produce new data once the generative model is set up. The other benefit of synthetic data is anonymity. With personal information being removed, the data cannot be traced back to the original owner so copyright and privacy infringements can be avoided. This is critical in synthetic data machine learning applications where realistic user behaviors are being simulated and private information must be protected.
Synthetic data can also be used to examine existing system performances as well as train new systems on scenarios that are not represented in the authentic data. Rather than utilizing costly real-world data to test if the system is providing the desired output, you can plug in synthetic data and analyze the results. For an instance where authentic data does not represent every possible situation, synthetic data can play a vital part in system training.
Creating Synthetic Data
So how is synthetic data created? Synthetic data is typically created via a generative model from the original dataset that produces synthetic copies which closely resemble the authentic data. A generative model is a workload model that can learn from real datasets to ensure that the output produced accurately resembles the original, authentic data.
There are three types of generative models: Generative Adversarial Networks (or GANs), Variational Autoencoders (VAEs), and Autoregressive models. GAN models utilize a generative and discriminative network in a zero-sum game framework. VAEs attempt to recreate output from input using encoding and decoding methods. Autoregressive models train the network to create individual pixels based on previous pixels above and to the left of them.
What is the competitive advantage with synthetic data? Besides the aforementioned cost-effectiveness and anonymity of synthetic data, the major competitive advantage lies with its various testing applications. With organizations having more data than ever at their disposal, the key challenge becomes how to extrapolate impactful insights from these large datasets and effectively translate the learnings into action. This is where big data tools and advanced analytics applications come into play.
Organizations use these tools and application to generate value from their massive datasets. Synthetic data can play a huge part in the development and improvement of these business-critical applications. For example, it can be used for visualization purposes and to test the robustness and scalability of new algorithms. This is vital for any organization working with big data applications.
Though synthetic data’s effectiveness in research is limited due as it only replicates certain properties of the original data, it is a highly useful tool to safely share data for testing the performance of a new software or scalability of an algorithm. With the widespread use of big data tools and analytics apps, an investment in synthetic data generation is critical and companies will have to factor this into their future strategic planning.