Dr. Muckai Girish-
(MuckAI Girish is the co-founder & CEO of the Generative AI company, Rockfish Data, which is developing a state-of-the-art Synthetic Data Workbench Software for Data Scientists. The views in this column are his own)
As xAI announced a massive $6 Billion fundraising round at a whopping $24 Billion valuation, Elon Musk has spoken publicly about the need for massive amounts of data to train models that are echoed in his comments from a month ago “The two sources of unlimited data are synthetic data and real-world video,” and that “Tesla has a pretty big advantage in real-world video.”
These comments of Elon’s cannot be taken lightly. The jury is no longer out when it comes to synthetic data. The verdict is in.
We are at an inflection point in the industry today. We are witnessing a massive transformation from a model-centric world to a data-centric world. Models continue to be developed at a feverish pace by the exponentially growing cadre of Artificial Intelligence researchers, Machine Learning engineers and data scientists. However, they quickly hit a plateau when it comes to making models work well. Hyperparameter tuning and other optimization methods get us quickly to a certain level of accuracy and then we hit a wall.
The missing element is data. If we have the right data, these models can be tuned to much better accuracy levels. We are ushered into the data-centric world. Unfortunately, relevant data is often not available to the team or person working with these models. Data sparsity and governance bottlenecks stand squarely in the way.
Synthetic data can help resolve these bottlenecks and help businesses unlock the true value of their models by leveraging the available data. In addition to model building and training, synthetic data can be used for a plethora of applications such as testing, incident response, sales enablement and sharing data with collaborators.
Generative AI-based Synthetic data platforms can be used to bridge the gap between available operational data and the outcomes targeted by the domain data scientist. Coupled with their knowledge of the domain, the data scientists can avail the benefits of synthetic data solutions.
Thanks to Generative AI-based synthetic data capabilities, we can now make a smooth transition to a data-centric world.
According to Gartner, by 2026, 75% of businesses will use generative AI to create synthetic customer data, up from less than 5% in 2023. Thanks to massive advances in generative AI technologies, we can now generate synthetic data tailored to any needs.
