As AI becomes more prevalent, developers face a shortage of quality training data, making synthetic data increasingly popular.
The Decline of Available Data
With each day, the availability of high-quality data for AI training decreases, posing a threat to technological advancement. In December, Google CEO Sundar Pichai highlighted the rapid depletion of quality training data by companies in the AI space.
The Rise of Synthetic Data
Synthetic data, which mimics real-world information, is becoming a crucial resource for developers. MIT Professor Muriel Médard notes that synthetic data has been around in statistics since the 1960s as a way to generate more data. However, it must be handled with care as it can also carry biases.
Risks and Opportunities of Synthetic Data
With the growing use of synthetic data, the risk of data manipulation arises. According to Nick Sanchez, biases could be intentionally introduced into datasets for sensitive applications. Médard suggests that blockchain technology can help ensure data integrity.
Synthetic data offers new opportunities for AI but requires careful handling and addressing ethical concerns related to its mutability and security.
