In the fast-paced world of artificial intelligence (AI), data is the key ingredient that fuels machine learning models. But getting the right data isn’t always easy. Enter synthetic data— reshaping how AI is developed and deployed. This important topic came to us from InfoWorld in their article, “Breaking through AI data bottlenecks.“
Synthetic data is artificially generated data that mimics real-world data. Instead of collecting real data from people, environments or systems, synthetic data is created using algorithms. It can replicate all the characteristics of actual data (like structure, distribution and relationships between data points) without involving real people or resources.
Gathering and cleaning real-world data can be expensive and time-consuming. With synthetic data, you can bypass the need for massive data collection efforts. You don’t need thousands of human test subjects or wait for specific events to occur to gather data—synthetic data can be generated on demand.
In AI, data is the foundation, and having access to large, varied datasets speeds up the training process. With synthetic data, AI researchers can create endless variations of datasets tailored to specific needs. This means faster iterations and more accurate models in less time.
Despite its benefits, synthetic data isn’t perfect. The biggest challenge is making sure it accurately reflects the complexities of real-world data. If synthetic data doesn’t capture real-world nuances, it could lead to AI models that perform poorly in real-world applications.
Data Harmony is a fully customizable suite of software products designed to maximize precise and efficient information management and retrieval. Our suite includes tools for taxonomy and thesaurus construction, machine aided indexing, database management, information retrieval and explainable AI.
Melody K. Smith
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.