Rumour has it that OpenAI is using over 50 Trillion! tokens of ‘synthetic’ data to pre-train GPT5. May or may not be true, but it is worth exploring why this is a fascinating development frees scaling from the bottleneck of existing web-based data and has many benefits in terms of cost, scaling, privacy and efficacy
Synthetic data is artificially generated data that simulates real-world data. Rather than scraping and buying data that is getting scare and expensive, you create it on computers. It can be used to augment even replace real datasets. Put simply; computers, not people, create the data.
Advantages of synthetic data?
The costs efficiencies are obvious, as you can generate huge amounts quickly, at very low cost, avoiding the need to buy ever-diminishing real datasets. Privacy problems then disappear, as synthetic data is not derived from actual users, making it particularly useful in domains such as healthcare and finance. Using synthetic data also eliminates the risk of accidentally exposing information, addressing ethical and legal concerns associated with real data, especially around the use of sensitive or copyrighted material.
It can also be targeted to specific needs, cases and conditions that may not be well-represented in real data. For example, it allows researchers and developers to simulate various scenarios and analyse potential outcomes in fields like autonomous driving, robotics, healthcare and financial modelling. It can also extend the reach of cases beyond the common cases in existing real-world data, out towards unusual or edge cases. This matters when models start to reason and create new solutions.
But its main advantage is that it can generate gargantuan quantities, making it easier to create huge datasets for training and scaling deep learning models. One also has complete control over the characteristics and distribution of the data, which allows the creation of balanced datasets, the elimination of bias or introduction of specific needs, practical and ethical.
It may not capture all the complexities and nuances of real-world data, potentially leading to models that do not generalise well to actual scenarios. This is because it is tricky to validate that it accurately represents the target problem, If the generation process is flawed or biased, the synthetic data can also inherit these issues, leading to skewed model performance. However, at least one can test and amend to solve the problems as you have complete control over the production process and what data to use.
Note that this is different from recursively generated data, where models scrape data from the web that now included model generated data. The recent paper in Nature, showing model collapse, was about indiscriminate data, not carefully generated and selected synthetic data. was taken by some to be a critique of synthetic data. It is not,
How is it generated?
As always, synthetic data has a long history. Early 20th century statistical simulations where researchers created artificial datasets to test statistical methods. This approach was used to validate statistical models. In the 1940s, Monte Carlo Methods were used in applications in physics and mathematics to simulate complex systems and processes.
More recently in the 1990s synthetic data started to be used to preserve privacy. The statistician, Donald Rubin, statistician, came up with the idea of generating multiple synthetic datasets to handle missing values.
One breakthrough was the development of Generative Adversarial Networks (GANs) by Ian Goodfellow and others in 2014. Generative Adversarial Networks (GANs) can used to create highly realistic synthetic data by training two neural networks in opposition to each other. Another group of generative models are autoencoders that have been widely used to create synthetic data, especially for image and text data.
Physics-based or agent-based simulations can also generate data that mimics complex real-world processes. Then there are techniques like image rotation, flipping, and scaling can produce variations of real data, effectively creating synthetic examples. When you expose the model to various transformations of the same image, data augmentation helps the model generalise to new, unseen data.
Augmentation techniques like rotation, flipping, and scaling help the model learn to recognise objects and patterns coping with these variations. You can rotate the image by a certain angle (e.g. 90, 180 or 270 degrees) to help the model recognise objects regardless of their orientation. Similarly with horizontal and vertical flipping, translation, cropping, varying brightness, contrast and scaling of images to simulate viewing the object from different distances and perspectives. You can even add random noise to the image making the model more immune to imperfections in the data.
The advantages are obvious in medical imaging, where obtaining a large labelled dataset is difficult. This type of data was used, for example, in the development of autonomous vehicles. Companies like Waymo and Tesla generate huge amounts of synthetic data to train and test their self-driving algorithms, simulating diverse driving scenarios and conditions. In healthcare, synthetic data has been used to create realistic medical records for research and training purposes, while preserving patient privacy as well as tumour detection. You can also generate synthetic health records for population health research.
Another use is to train conversational models to provide more examples for the model to learn from, especially in cases where real conversational data is limited or biased. By generating synthetic conversations, developers can introduce a wide range of scenarios, dialects, and linguistic nuances that the model may not encounter in real data, to improve its robustness and adaptability. This may be conversations involving rare or unusual situations, to allow the model can handle a broader spectrum of queries and responses. Also, if certain types of conversations are underrepresented in real data, synthetic data can be generated to balance the dataset, reducing bias and improving model performance across different types of interactions. Again, using synthetic data helps mitigate privacy concerns, as the data does not contain any real user information, say facebook or X posts . You see how this also helps in customer support chatbots that can be trained with synthetic conversations tailored to the specific products and services of a company.
Open source models, like Llama, are powerful tools for synthetic data generation. It is open source and allows customers to create high-quality task- and domain-specific synthetic data for training other language models. You can answers for datasets, then use them in to fine-tune smaller models. This has already been done in a number of different domains.
Conclusion
Synthetic data is a powerful tool for training and validating machine learning models with massive benefits in terms of scalability, control, and privacy. While it is not without problems but these are known and solvable. Ongoing advancements in data generation techniques continue to improve the realism and utility of synthetic data, making it an increasingly valuable resource in AI and machine learning. GPT5 – bring it on!
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