Synthetic Data Transforms AI Training: A Look at the Methods and Benefits
The Emergence of Artificial Data and Its Role in Reinforcing, Rather Than Eliminating, Authentic Data
In the realm of Artificial Intelligence (AI), the importance of synthetic data generation is increasingly being recognised as a solution to the challenges posed by real-world data collection. Here's a breakdown of the key methods used to create synthetic data and the benefits they bring.
1. Deep Learning Techniques
Deep learning techniques, such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), are at the forefront of synthetic data generation.
- Variational Autoencoder (VAE): VAEs work by compressing data into a latent space using an encoder, and then generating synthetic data using a decoder. The aim is to optimise the similarity between the input and output data.
- Generative Adversarial Network (GAN): GANs consist of a generator that creates synthetic data and a discriminator that evaluates its realism. Through iterative training, the generator produces data that is increasingly indistinguishable from real data.
2. Diffusion Models
Diffusion models are another powerful tool for generating synthetic data, particularly effective in continuous domains like images and videos. They learn to generate data by reversing a diffusion process.
3. Simulation-Based Generation
This approach models the underlying process that generates real data, using domain-specific models, physics-based simulations, or mathematical models to create synthetic data. It's particularly useful when real data collection is expensive or impossible.
4. Procedural Generation
Procedural generation uses algorithms and predefined rules to generate data. It's widely used in video game development to create large datasets quickly.
These methods enable the creation of synthetic data that can mimic real-world data characteristics, enhance privacy, and accelerate AI model training by providing scalable and cost-effective solutions.
According to Gartner, synthetic data will make up 60% of all data used for training AI models by 2024. However, it's worth noting that AI-generated synthetic data is currently the most widespread form, involving training models on real-world data to detect patterns and correlations, then generating new data that mimics these statistical properties.
The University of South California's researchers found that companies can replace up to 90% of their real data with synthetic data without seeing a substantial drop off in performance. This finding underscores the potential of synthetic data to revolutionise AI training.
However, models trained solely on synthetic data may experience model collapse, where they start to produce less precise or less diverse outputs. To mitigate this, the most common approach is to diversify the data used by combining synthetic data with human data.
The market for synthetic data generation is expected to reach $1.15 billion by 2027 and $2.33 billion by 2030. Companies such as Gretel AI, Tonic.ai, MOSTLY AI, Synthesis AI, and Hazy have received significant investments in the synthetic data sector.
Despite the growing reliance on synthetic data, real-world data will always be needed to train AI models that then generate synthetic data and for syncing with synthetic data to ensure accuracy and avoid model collapse.
Data-and-cloud-computing solutions are essential for the efficient training of Artificial Intelligence (AI) models that generate synthetic data. These models, such as Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and diffusion models, rely on powerful computing resources to run complex algorithms.
Artificial-intelligence development is increasingly utilizing synthetic data generated via these methods, with predictions indicating that synthetic data will account for 60% of all data used for AI training by 2024. This trend is supported by significant investments in synthetic data generation companies, like Gretel AI, Tonic.ai, MOSTLY AI, Synthesis AI, and Hazy.
