What is Synthetic Data?
Synthetic data is a term I remember hearing a while back but I did not quite understand what it meant. The term refers to artificially generated data that mimics real-world data patterns and characteristics.
The market for synthetic data is projected to grow from $163 million dollars in worth to $3.5 billion dollars in worth which is a sizeable jump (Allied Market Research, 2022).
This growth is driven by the increasing utilization of synthetic data in diverse industries, including banking, healthcare, and more. The appeal of synthetic data lies in its accessibility and the experimental flexibility it offers to businesses and researchers. For a good read on the future of synthetic data refer to the article below.
Synthetic data could be better than real data (nature.com)
While there are various reasons for the adoption of synthetic data, the emergence of ChatGPT-based solutions in numerous industries raises an important question: What sets data generated by ChatGPT apart from carefully crafted synthetic data?
Synthetic Data Example — AI-Generated Images Trying to Mimic the Real World
An example of synthetic data would be an image generated by the likes of Stable Diffusion or DALL E 2. Whilst the images may resemble an actual place, it is not real photo that was taken. It is AI-generated! Images like the one below have uses especially for computer vision and training an image model.
Other examples of synthetic data include:
- Synthetic Text — Artificially generated text used for language model training, data privacy protection, or simulating large-scale datasets.
- Synthetic Voice Recordings — Artificially generated speech samples used for training speech recognition systems, voice assistants, or speech synthesis models.
- Synthetic Financial Data — Artificial financial transactions and market data used for testing fraud detection algorithms or simulating economic scenarios.
Does ChatGPT produce synthetic data?
In short yes but if you were to ask ChatGPT, it would say no. When asked if its responses qualified as pieces of synthetic data, this was the response:
So the ChatGPT outputs are not synthetic data then? Interesting as another prompt asking it to create such data produced the following:
You can then from there fine tune the data creation process to include common features of financial data. In addition, you could feed in a real dataset and ask it to create extra data for training a fraud detection model for instance.
Conclusions
In conclusion, the distinction between synthetic data and ChatGPT-generated data raises intriguing questions. While synthetic data has great potential, ChatGPT’s unique perspective challenges traditional categorization. By fine-tuning and combining with real datasets, ChatGPT can generate synthetic data. Understanding this nuance is vital in the evolving data generation landscape. The growth of synthetic data empowers businesses and researchers, and grasping the differences between approaches is key to unlocking their full potential in AI and data exploration.
