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Synthetic Data : The gamechanger for AI

Artificial intelligence is on the road to a gradual evolution to progress. However, the road is not an easy one. Challenges line up one after the other, often shaking the foundations of a future that can be completely transformed by leveraging AI.

AI has to deal with the increasing number of restrictive data privacy regulations while also ensuring the accuracy and authenticity. With increasing number of challenges, a multitude of solutions have been proposed- programs that ensure user consent, tools that facilitate identification and reduction of bias and those designed to render the user data anonymous, thereby ensuring privacy and safety. However, these proposed solutions come with certain blank spots that need to be addressed and built upon before they can be used as full fledged solutions.

This is where synthetic data makes an entrance, wearing the cloak of a saviour. And given its features and characteristics that ensures a smooth transition of AI into a harbinger of change, it is not surprising that synthetic data is considered to be a saving grace. In a nutshell, synthetic data is artificial computer generated data, that can replace real world data. It is similar to real world data in the sense that it has the same mathematical and statistical properties. The point of difference is that it does not stand for real individuals, thereby addressing a major privacy concern. It can be thought of a digital mirror that statistically reflects the real world, enabling AI systems to train in a completely virtual realm. It also facilitates customization in accordance with the diverse use cases whether it be finance, healthcare, transportation, agriculture or retail.

With the advent of synthetic data and the solutions it offers, increasing number of people have switched to synthetic data.

Synthetic Data vs Real World Data

Bias in datasets have been a matter of concern since it can lead to AI algorithms generating systemic discrimination. If the speculations are to be believed, it is anticipated that by 2022, more than 80% of AI projects will generate erroneous data due to the inherent bias in algorithm and data. The growing concerns over privacy is yet another side of this matter. Though the consumer data privacy laws ensure protection and control of personal data, it considerably reduces the algorithm’s effectiveness. This is due to the fact that, as the laws restrict data access, the algorithm loses ground on which it can train on. This inevitably limits the scope of AI across various fields.

This is where synthetic data comes in, completely transforming the rubrics of the game. By taking out the real personal data out the equation, it ensures to deliver the advantages of AI that is free of the downsides that comes with real world data. By filtering the biases inherent in real world, synthetic data exhibits better performance and efficiency. It also enables quick dataset creation, saving time and costs.

It is not surprising that well established companies like IBM are among the ones that generate synthetic data. According to Forrester Research, synthetic data is listed under the potential “AI 2.0,” which can add radical changes in the field of AI. Though it has a lot of ground to cover, it won’t be long before synthetic data takes the lead in the field of artificial intelligence.



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