ArtICle SummarY: Discussion on the Advantages and Disadvantages of Using Artificial Data in Artificial Intelligence
In the realm of artificial intelligence (AI), a new player is making waves - synthetic data. These artificially generated datasets, mimicking the statistical properties of real data, are becoming increasingly popular, particularly for testing software applications with data-driven logic.
The use of synthetic data offers several advantages. For one, it provides data augmentation, offering additional data examples similar to real data. This is particularly useful when real data for a specific event is scarce. Generative models, used to create realistic synthetic data from a little bit of real data, automate what was once a manual process.
One such platform helping users generate and test synthetic data is the Synthetic Data Vault (SDV), an open-core platform developed by Data to AI Lab at MIT and first released in 2017. Platforms like SDV provide software to build generative models for sensitive or private tabular data, preserving customer privacy. Users can create specific synthetic data for application testing, such as mimicking real customers and transactions.
However, the use of synthetic data isn't without its challenges. Bias can be an issue, as it can carry over from the real data. Careful planning is necessary to remove bias in synthetic data through different sampling techniques. Additionally, the use of synthetic data adds a new dimension to the problem of ensuring models can generalize to new situations.
To address these concerns, the Synthetic Data Metrics Library was created. This library ensures checks and balances in the use of synthetic data, helping to prevent loss of performance when AI models are deployed with synthetic data. New efficacy metrics are emerging, with emphasis on efficacy for a particular task.
As generative models become more sophisticated, the old systems of working with data are expected to change significantly. Estimates suggest that more than 60% of data used for AI applications in 2024 will be synthetic, with this figure expected to grow.
MIT News recently spoke with Kalyan Veeramachaneni, a principal research scientist at the Laboratory for Information and Decision Systems and co-founder of DataCebo, about the future of synthetic data. Veeramachaneni highlighted the importance of careful evaluation, planning, and checks and balances to ensure the trustworthiness of synthetic data.
In conclusion, synthetic data is transforming the way AI models are developed, offering potential for privacy protection and cost reduction. As we move forward, it is crucial to approach its use with thoughtful planning and rigorous evaluation to ensure the best possible outcomes.
The article covers four different data modalities: language, video or images, audio, and tabular data. Each has slightly different ways of building generative models, opening up a world of possibilities for AI research and development.
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