Can synthetic data make artificial intelligence better?

Although artificial intelligence (AI) has become more advanced due to exponential advances, the limitations of this modern technology still exist.

So, can synthetic data be the solution to all problems related to artificial intelligence?

In the fourth industrial revolution, every industry has discovered the potential of modern technologies; such as artificial intelligence (AI) and machine learning (ML).

Almost every other organization is deploying AI to create more efficient business processes and ensure better customer satisfaction. However, startups, SOHOs, and small and medium-sized businesses (SMBs) face a major problem when adopting AI – it’s known as the cold start problem. While startups and SMEs generally do not have the resources to collect big data, the cold start problem is essentially a lack of such relevant data.

On the other hand, industry giants already have the resources to collect real-world data and apply it to train their AI systems. Therefore, the odds of winning for small and medium-sized enterprises are great. In this case, synthetic data may be the necessary enabler.

Synthetic data can be the driving force behind data-driven business models. Furthermore, studies have shown that synthetic data produces the same results as real data. Synthetic data is considered cheaper and takes less time to process than real data. Therefore, the emergence of synthetic data can level the playing field currently dominated by large companies in favor of SMEs and startups.

Discover the Benefits of Synthetic Data

Synthetic data is computer-generated artificial data based on user-specified parameters to ensure the data is as close as possible to real-world historical data. Typically, game engines such as Unreal Engine and Unity are often used as simulation environments for testing and training AI-based applications such as self-driving cars. There are many advantages to developing AI-driven applications based on synthetic data. Some of the advantages include:

#1. Develop PrototypesFinding, aggregating, and modeling large amounts of relevant real-world data is a tedious process. Therefore, generating synthetic data may be the best solution. Such data will enable building prototypes and testing such prototypes to obtain the desired results before mass production. Building prototypes using synthetic data is more efficient and cost-effective than real data.

Open AI, a non-profit artificial intelligence research company, is developing a number of artificial intelligence-based applications. Among these applications, researchers have developed robots trained with synthetic data that can learn a new task after seeing an action performed just once. A California tech startup is developing an artificial intelligence platform with a vision similar to Amazon Go. The startup aims to provide checkout-free solutions for convenience stores and retailers with the help of synthetic data. They have also introduced AI-powered smart systems to monitor every shopper in the store to identify and analyze their learning patterns.

2. Ensure data privacy

In November 2018, 500 million Marriott customers were affected in a high-profile data breach. Of those 500 million people, 327 million had their data including passport information, email addresses, mailing addresses and credit card information stolen. Due to such incidents, people are worried about the security and privacy of their data.

Synthetic data can effectively solve such privacy issues. Synthetic data does not include any personal data. Therefore, data privacy can be easily ensured. Synthetic data is extremely useful in training AI systems for healthcare applications. AI systems often require real patient data. This threatens patient privacy. Synthetic data allows the development of advanced artificial intelligence applications in healthcare while maintaining patient confidentiality.

For example, researchers from Nvidia, working with the Mayo Clinic in Minnesota and the MGH and BWH Clinical Data Science Center in Boston, are using generative adversarial networks to generate synthetic data for training neural networks. The generated synthetic data contains 3,400 MRIs from the Alzheimer's Disease Neuroimaging Initiative dataset and 200 4D brain MRIs and tumors from the Multimodal Brain Tumor Image Segmentation Benchmark dataset. Likewise, simulated X-rays can be used alongside actual X-rays to train AI systems to recognize multiple health conditions.

3. Unprecedented Scenario Testing and Training

One of the most important processes in developing AI-driven applications is testing system performance. If the system is not producing the desired output, it needs to be retrained. In this case, synthetic data can prove beneficial. Synthetic data can generate scenarios to test AI systems instead of using real data or testing the system in a real environment. This method is cheaper and less time-consuming than obtaining real data.

Similarly, synthetic data can also train new or existing systems for scenarios that may arise in the future that lack real data or events. With this approach, researchers can develop more futuristic AI applications. Additionally, retraining AI systems using synthetic data is simpler because generating synthetic data is simpler than collecting accurate real-world data.

Due to these benefits, synthetic data has become an accessible alternative for testing and training autonomous vehicles. Many self-driving car developers are using simulated gaming environments like GTA V to train their AI-based systems. Likewise, May Mobility is building a self-driving micromobility service by training their vehicles using synthetic data.

Another self-driving car developer called Waymo has already tested its self-driving cars by driving 5 billion miles on simulated roads and another 8 million miles on real roads. The synthetic data approach allows developers to test their self-driving cars on simulated roads, which is much safer than direct testing on actual roads.

4. Improve data flexibility

Getting real data is a tedious process that involves paying for annotation and ensuring that any copyright infringement is avoided. Furthermore, real data can only be used in specific scenarios with sufficient historical data in a specific domain. Unlike real data, synthetic data can instantly represent any combination of objects, scenes, events, and people. Synthetic data can generate general datasets that can discover niche applications. As a result, researchers can explore endless possibilities with synthetic data. Several startups are creating an open data economy by developing training data sets that meet customer requirements.

5. Exploring the Limitations of Synthetic Data

While synthetic data can help AI reach undiscovered territories, its limitations may become a major obstacle to its mainstream deployment. For starters, synthetic data simulates several properties of real-world data, but it doesn't exactly replicate the original data. When modeling such synthetic data, AI systems will only look for common trends and situations in the real data. Therefore, rare scenarios contained in corner cases in real-world data may never be included in synthetic data.

In addition, researchers have not yet developed a mechanism to check whether the data is accurate. Finding flaws in real data and reducing them is simpler than using synthetic data. AI-driven systems already have a “dark side” that promotes unintentional bias. Using synthetic data, it may be premature to predict the scope and impact of this bias.

6. Overcoming the Challenge

The need for organizations to understand that synthetic data is a fairly new discovery. The efficiency and accuracy of such data has not been evaluated against current industry standards. Therefore, synthetic data should not be considered a stand-alone data source. Especially in applications facing safety concerns, such as healthcare applications and self-driving cars, synthetic data must be combined with real-world data to develop AI systems. But applications in retail have a lower risk factor and can easily rely on synthetic data.

For testing purposes, synthetic data is a viable and inexpensive solution. However, for other purposes, the results of an AI system need to be thoroughly studied and analyzed before employing synthetic data as a stand-alone solution. With further research, synthetic data may become more reliable for a variety of operations.

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