Rosemary J Thomas, Senior Technical Consultant, AI Labs, Version 1, on synthetic data fast becoming a critical enabler for next-generation Artificial Intelligence, offering scalable, diverse and privacy-safe datasets that can outperform traditional data sources in both quality and applicability.

Yet it must not be forgotten that behind every impressive model lies a more fundamental force: data. Not just any data, but data that is high quality, diverse and available in sufficient quantity. Trillions of tokens are needed to train large language models, and the limits of what real-world data can offer are increasingly apparent, whether due to privacy concerns, cost or simple scarcity.

As we reach those limits, a quiet revolution is gaining pace. Synthetic data is emerging not just as a workaround but as a cornerstone of the next generation of AI. Those of us behind AI technology are seeing firsthand how synthetic data is reshaping the way models are trained, refined and deployed. Whether for automation, large language models or AI applications in tightly regulated sectors, synthetic data is solving problems that traditional data cannot.

Synthetic data may not be what the public thinks of when they imagine breakthroughs in AI. Yet behind every sophisticated chatbot, every automated decision system and every model making millions of predictions per second, there is a dataset that trained it. Increasingly, synthetic data is the invisible thread weaving through these systems, enabling their creation, evolution and accountability. As AI becomes more powerful and apparent both in business and day-to-day life, the importance of the data that fuels it will only grow.

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