Data is the oxygen to modern technology. Processing the right data and getting important information from it is the most challenging part. Limited access to data, availability, and reliability always has been a barrier to building the most sophisticated AI and machine learning applications. Synthetic data and its role in the world of AI is not a new concept. Its been here for years, but many organizations fail to recognize its importance.
Many business problems that AI/ML models could solve today require huge volumes of data. Even today, it is challenging to collect large amounts of high-quality data required to build the most reliable AI and ML models. In this article, we discussed synthetic data and how it is transforming the world of AI.
What is Synthetic Data?
Synthetic data is data that is generated artificially by using machine learning algorithms to act close to real-world data. It is used for a wide range of activities, from testing to model validation to AI model training. What makes synthetic data important is that it can be generated to meet specific needs or conditions that are not available in existing data.
What is Synthetic Data in Artificial Intelligence?
A large number of organizations that work with AI/ML require access to sensitive customer data. Collecting and decoding sensitive data raises privacy concerns and leaves businesses vulnerable to data breaches. Whereas some types of data are costly to acquire and time-consuming. In some cases, there isn’t the availability of sufficient data to develop the ML models that you achieve. Synthetic data can be a handful in resolving these concerns and building more futuristic sophisticated applications of AI.
Benefits of Synthetic Data
Synthetic data has several advantages over real data:
- Synthetic data can be the perfect alternative to overcome the limitations of real data usage restrictions. For instance, real data may have usage constraints due to privacy rules and regulations, whereas synthetic data can be leveraged to eliminate these challenges.
- Where real data does not exist for model building, synthetic data can be generated to simulate not yet encountered conditions.
- Synthetic data aims to preserve the multivariate relationships between variables instead of specific statistics alone, which makes it easy to work in complex data environments.
Challenges in generating Synthetic Data
The data that mimics the real scenarios of testing and development may seem to provide limitless opportunities. But it is important to understand that any synthetic model deriving from data can only replicate specific properties of data, which possibly describes it has a few limitations.
- As mentioned earlier synthetic data can only mimic real-world data, which means it is not an exact replica of it. Therefore the synthetic data may not be able to outline every detail like real data.
- The quality of the model depends on the input data and the data generation technique used. Sometimes synthetic data may reflect the biases in source data.
- User acceptance is more challenging than expected, as many of us are still not aware of its benefits.
- Synthetic data requires time and effort, eventhough it is easier to create than actual data.
- Achieving accurate output as original or human-annotated data while working with synthetic data may not be possible. Especially when working with complex data sets.
The role of Synthetic Data in
According to Gartner’s study, 60% of all data used in the development of AI will be synthetic rather than real by 2024. The rise of synthetic data is completely transforming the economics, ownership, strategic dynamics, and even (geo)politics of data. The applications of synthetic data to upgrade AI applications today are limited. From robotics to physical security models synthetic data has its roots everywhere.
The importance of synthetic data for ML model development and testing is increasing rapidly. Machine learning models need to be trained with incredible amounts of data which could be difficult to obtain or generate without synthetic data. With ease in data production, accuracy in labeling, the flexibility of the synthetic environment, and usability as a substitute for data that contains sensitive information, synthetic data use cases are gaining widespread adoption. Here are a few applications that synthetic data is transforming:
1. Automotive and Robotics
Researchers are building synthetic data that can be used to train deep machine learning algorithms for use in a wide range of applications, from self-driving cars to robotics. Building models that can act on self-driving simulation needs to be trained with real-life experiments. Which can be expensive, whereas using synthetic can reduce data generation costs, and helps in training algorithms better. For instance, Synthetic data is already being used by companies like Tesla to train their safety systems and self-driving software.
2. Agile Development and DevOps
Artificial intelligence systems rely on training data, which are human-generated examples of the system being used. This means that even if an artificial intelligence system is capable of performing a task better than a human, the data it’s trained on will be biased toward human behavior. For instance, if an AI system is trained on data in which humans only interact with each other, it will have a difficult time learning how to interact with humans in other situations. The solution to this problem is to create synthetic data that is as unbiased as possible. Using synthetic data in such situations reduces the model’s development, testing, and validation time without affecting the accuracy of the application.
3. Data Security
Data security is one of our greatest challenges as a society, your email, your social media posts, your online shopping history, the articles you’ve read, the music you’ve listened to – all of these things are pieces of data that have been collected, stored, and used to make decisions about you. Synthetic data generated using machine learning algorithms can mimic real-world sensitive data without affecting data privacy and regulations to build next-gen applications for marketing, social media, and business operations.
4. Business Operations
The applications of machine learning for enhancing business operations like creating to managing large data sets, optimizing product operations, improving customer service, financial decision-making, and resource management require constant data that is generated and updated constantly. Synthetic data can be used to generate responses using a set of known facts and context that can regulate the decision-making time. From scaling business models to operations synthetic data can transform AI applications in daily business operations to improve productivity.
Synthetic data is potentially changing the way we generate and operate with data. It has helped small and mid-sized enterprises to work with AI applications that never dreamed to achieve with the resources they have. It is providing an opportunity to compete and scale at the same time. The rise of synthetic data is taking AI innovation to a whole new level and mitigating all the data barriers in building AI applications like never before. SoulPage, a data science company can help with a better understanding of synthetic data and how to generate it based on your needs to achieve your objectives in a shorter period. To know more about us or how you can leverage synthetic data to transform your AI journey, connect with us today.