With the advancement of technology, there have been shortage of data used by ML models. To fill this gap lot of synthetic data / artificial data is generated or simulated to train ML models. Primary data collection even though highly reliable, is often costly and time-consuming and hence there is a growing demand for simulated data which may or may not be accurate and imitate real-world experiences. The article below just tries to explore the pros and cons.
What is the promise of synthetic data, and when to use it?
Synthetic data is algorithmically generated instead of being produced by real-world incidents. Real data, is directly observed from the real world. It is used to derive the best insights. Although real data is valuable, it is usually pricey, time-consuming to collect, and unfeasible due to privacy issues. Synthetic data hence becomes a secondary/alternative to real data and can be used to develop accurate and advanced AI models. This artificially generated data is used along with real data to build an enhanced dataset that is not riddled with the inherent faults of real data.
Synthetic data is best used to test a newly developed system where real data is unavailable or biased. Synthetic data can also supplement real data, which is small, unsharable, unusable, and unmovable.
Is synthetic data a must-have and essential for the future of AI?
Data science professionals introduce information to the AI model to develop synthetic data which can be used for product demonstrations and internal prototyping. For example, financial institutions can use synthetic data to simulate market fluctuations and behavior to identify fraud and make better decisions.
Synthetic data is also used to boost the accuracy and efficiency of machine learning models. Real-world data cannot account for all the combinations in events plausible or likely to happen in the real world. Synthetic data can be used to generate insights for edge cases and events that haven’t yet happened in the real world.
What are the risks of synthetic data?
One of the major advantages of synthetic data is undoubtedly cost-effectiveness and the lack of privacy concerns. However, it comes with its set of limitations and risks.
First, the quality of the synthetic data is often dependent on the model that helped create and develop it. Furthermore, before using synthetic data, it has to undergo a variety of verification steps to ensure the veracity of its results by comparing it to human-annotated, real-world data models.
Synthetic data can also be misleading, and not entirely immune to privacy issues. Additionally, there could be fewer takers for synthetic data as it could be perceived as fake or sub-standard.
Finally, questions regarding the methods used to create synthetic data could also arise. Issues regarding the transparency of the data generation techniques also need to be answered.
Why Use Synthetic Data?
Acquiring large amounts of quality data to train a model within the pre-set time frame is challenging for many businesses. Additionally, manually labeling data is a slow and expensive process. That’s why generating synthetic data can help businesses overcome these challenges and develop credible models quickly.
Synthetic data reduces the dependence on original data and limits the need to capture it. It is an easier, cost-effective, and time-saving method of generating datasets. Large quantities of quality data can be developed in a much shorter time when compared with real-world data. It is especially useful for generating data based on edge events – events that rarely occur. Additionally, synthetic data can automatically be labeled and annotated as it is being generated, reducing the time taken for data labeling.
When privacy concerns and data security are primary concerns, synthetic datasets can be used to minimize the risks. Real-world data needs to be anonymized to deem usable as training data. Even with anonymization such as the removal of identifiers from the dataset, it is still possible for another variable to act as an identifying variable. Fortunately, it is never the case with synthetic data as it was never based on a real person or a real event.
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Advantages of Synthetic Data Over Real Data
The major advantages of synthetic datasets over original datasets are
- With synthetic data, it is possible to generate a limitless amount of data as per the model requirement.
- With synthetic data, it is possible to build a quality dataset that can be risky and expensive to collect.
- With synthetic data, it is possible to acquire high-quality data that is automatically labeled and annotated.
- Data generation and annotation are not as time-consuming as it is with real data.
Why use synthetic data (synthetic vs real data)
Real Data Can be Dangerous To Procure
Most importantly, real data can sometimes be dangerous to procure. If you take autonomous vehicles, for example, the AI cannot be expected to only rely on real-world data to test the model. The AI running the autonomous vehicle need to test the model on avoiding crashes, but getting your hands on crashes can be risky, expensive, and unreliable – making simulations the only option for testing.
Real Data Could be based On Rare Events
If the real data is hard to procure because of the rarity of the event, then synthetic data is the only solution. Synthetic data can be used to generate data based on rare events to train the models.
Synthetic Data Can be Customized
Synthetic data can be customized and controlled by the user. To make sure the synthetic data doesn’t miss edge cases, it can be supplemented with real data. Additionally, the event frequency, distribution, and diversity can be controlled by the user.
Synthetic data comes with auto-annotation
One of the reasons why synthetic data is preferred over real data is it comes with perfect annotation. Instead of hand-annotating the data, synthetic data comes with automated annotations for each object. You don’t have to pay extra for data labeling which makes synthetic data a more cost-effective choice.
Synthetic data allows for non-visible data annotation
There are some elements in visual data that humans are inherently incapable of interpreting, and thereby annotating. It is one of the major reasons for the push by the industry towards synthetic data. For example, applications developed based on infrared imagery or radar vision can only work on synthetic data annotation because the human eye cannot comprehend the imagery.
Where can you apply synthetic data?
With new tools and products being released, synthetic data may play a major role in the development of Artificial intelligence and machine learning models.
Right now, synthetic data is being leveraged extensively by – computer vision and tabular data.
With computer vision, AI models detect patterns in images. Cameras, equipped with computer vision applications, are being used in many industries such as drones, automotive, and medicine. Tabular data is getting a lot of traction from researchers. Synthetic data is opening the doors to developing applications for health that were hitherto restricted due to privacy violation concerns.
Synthetic Data Challenges
There are three major challenges to using synthetic data. They are:
Should Reflect Reality
Synthetic data should reflect reality as accurately as possible. However, it is sometimes impossible to generate synthetic data that doesn’t contain elements of personal data. On the flip side, if the synthetic data doesn’t reflect reality, it won’t be able to exhibit patterns necessary for model training and testing. Training your models on unrealistic data doesn’t produce credible insights.
Should be devoid of bias
Similar to real data, synthetic data could also be susceptible to historical bias. Synthetic data might reproduce biases if it is generated too accurately from the real data. Data scientists need to account for bias when developing ML models to make sure the newly generated synthetic data is more representative of reality.
Should be free from privacy concerns
If the synthetic data generated from the real-world data is too similar to each other, then it too can create the same privacy issues. When real-world data contains personal identifiers, then the synthetic data generated by it can also be subject to privacy regulations.
Final thoughts: synthetic data unlocks new possibilities
When you pit synthetic data and real-world data against each other, the synthetic data is not far behind on three counts- faster data collection, flexibility, and scalability. By tweaking the parameters, it is possible to generate a new dataset that may be dangerous to collect or may not be available in reality.
Synthetic data helps in forecasting, anticipating market trends, and devising robust plans for the future. Moreover, synthetic data can be used to test the veracity of models, their premise, and various outcomes.
Finally, synthetic data can do much more innovative things than real data can achieve. With synthetic data, it is possible to feed models with scenarios that will give us a glimpse into our future.