MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.
Copyright: news.mit.edu – “Using generative AI to improve software testing”
Generative AI is getting plenty of attention for its ability to create text and images. But those media represent only a fraction of the data that proliferate in our society today. Data are generated every time a patient goes through a medical system, a storm impacts a flight, or a person interacts with a software application.
Using generative AI to create realistic synthetic data around those scenarios can help organizations more effectively treat patients, reroute planes, or improve software platforms — especially in scenarios where real-world data are limited or sensitive.
For the last three years, the MIT spinout DataCebo has offered a generative software system called the Synthetic Data Vault to help organizations create synthetic data to do things like test software applications and train machine learning models.
The Synthetic Data Vault, or SDV, has been downloaded more than 1 million times, with more than 10,000 data scientists using the open-source library for generating synthetic tabular data. The founders — Principal Research Scientist Kalyan Veeramachaneni and alumna Neha Patki ’15, SM ’16 — believe the company’s success is due to SDV’s ability to revolutionize software testing.
SDV goes viral
In 2016, Veeramachaneni’s group in the Data to AI Lab unveiled a suite of open-source generative AI tools to help organizations create synthetic data that matched the statistical properties of real data.
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Companies can use synthetic data instead of sensitive information in programs while still preserving the statistical relationships between datapoints. Companies can also use synthetic data to run new software through simulations to see how it performs before releasing it to the public.
Veeramachaneni’s group came across the problem because it was working with companies that wanted to share their data for research.[…]
Read more: www.news.mit.edu
MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.
Copyright: news.mit.edu – “Using generative AI to improve software testing”
Using generative AI to create realistic synthetic data around those scenarios can help organizations more effectively treat patients, reroute planes, or improve software platforms — especially in scenarios where real-world data are limited or sensitive.
For the last three years, the MIT spinout DataCebo has offered a generative software system called the Synthetic Data Vault to help organizations create synthetic data to do things like test software applications and train machine learning models.
The Synthetic Data Vault, or SDV, has been downloaded more than 1 million times, with more than 10,000 data scientists using the open-source library for generating synthetic tabular data. The founders — Principal Research Scientist Kalyan Veeramachaneni and alumna Neha Patki ’15, SM ’16 — believe the company’s success is due to SDV’s ability to revolutionize software testing.
SDV goes viral
In 2016, Veeramachaneni’s group in the Data to AI Lab unveiled a suite of open-source generative AI tools to help organizations create synthetic data that matched the statistical properties of real data.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Companies can use synthetic data instead of sensitive information in programs while still preserving the statistical relationships between datapoints. Companies can also use synthetic data to run new software through simulations to see how it performs before releasing it to the public.
Veeramachaneni’s group came across the problem because it was working with companies that wanted to share their data for research.[…]
Read more: www.news.mit.edu
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