Replace data obtained from the real world with synthetic data, artificial computer-generated data. You can achieve a less biased artificial intelligence (AI) and protect your users. Learn how synthetic data can enhance the efficiency of machine learning (ML) and reduce costs.
In a data-driven age, having reliable and secure data is becoming increasingly important. Possessing the same mathematical and statistical properties as real-world datasets, synthetic data protects consumer privacy by not explicitly representing as many real individuals. Moreover, incorporating synthetic data enables the machine learning of artificial intelligence (AI) systems in a completely virtual realm.
A variety of industries can implement synthetic data into their operations. Furthermore, incorporating it into machine learning and artificial intelligence allows for less biased results. Synthesis.ai found that 89% of industry leaders agree that synthetic data is innovative and will transform their industry. It also found that 59% of industry leaders believe their industry will utilise synthetic data either independently or in conjunction with ‘real-world’ data within the next 5 years.
Global research and advisory company, Gartner, predicts that through 2022, 85% of AI projects will deliver erroneous outcomes. Moreover, these errors will be the result of bias in data, algorithms, or even the teams responsible for managing them. Subsequently, these potential issues with real data have led to the increasing adoption of synthetic data to facilitate machine learning with less bias.
Gartner forecasts that by 2024, synthetic data will account for 60% of the data used for the development of AI and analytics projects. The resource-intensive nature of supervised learning and increasing concern on user privacy and personal data attribute to this forecast.
Synthetic data can help expand and fill out issues or gaps within data sets. This can be useful in cases when:
- Estimation or forecast models based on historical data no longer work
- Assumptions based on past experiences fail
- Algorithms cannot reliably model all possible events due to gains in real-world data sets
Benefits of synthetic data
Machine learning requires a lot of data. Often, the amount of real data we possess is not enough for machine learning. For complex applications and AI projects, collecting this large quantity of high-quality training data can be a challenge. Real training data also is collected in a linear way. Meanwhile, synthetic data can be generated in massive quantities, the main limits are the need for more processing power for even more examples. Along with the high quantity of data, there is also high-quality synthetic data. Rare events can occur more often in synthetic data to accurately train the AI model. Each object in a scene can also have a variety of annotations automatically generated which reduces efforts for data labelling.
Synthetic data can reduce the resources used for supervised learning. This learning is a subcategory of machine learning and artificial intelligence that labels datasets by matching input and outputs based on example input-output pairs. Conducting supervised learning on average for new projects costs $2.3 million annually and can take 16 weeks. Improvements in productivity, data quality and more efficient model development and labelling are the expected results of incorporating synthetic data.
Another goal of synthetic data is to correct any bias found in real-world data. Bias can not only be offensive to consumers who use its applications, but it can render AI ineffective. One of the main reasons that bias finds it way into AI is primarily the training data being used containing historical biases. By being artificial, it can help deal with the scarcity or insufficient amounts of real data and the fact that actual data is biased and unfair towards certain groups of people. Since some people are underrepresented in datasets for different areas, it creates a bias within the data. Synthetic AI data can create a fairness definition that will attempt to transform biased data into something deemed fair or AI-generated data can fill in the holes in a dataset that isn’t diverse or large enough to form an unbiased dataset.
Protecting consumer data is becoming increasingly important. Anonymised personal data is insufficient in protecting consumers. With a decreased reliance on real personal data, synthetic data protects consumers. Through its generative adversarial networks (GANs), one neural network generates synthetic data and another attempts to detect if it is real. Artificial intelligence learns over time, with the generator network improving the quality of data until it cannot tell the difference between real and synthetic.
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