From the Editor's Desk

How to Build Good AI Solutions When Data Is Scarce

Conventional wisdom holds that you need large volumes of labeled training data to unlock value from powerful AI models. For the consumer internet companies where many of today’s AI models originated, this hasn’t been difficult to obtain. But for companies in other sectors — such as industrial companies, manufacturers, health care organizations, and educational institutions — curating labeled data in sufficient volume can be significantly more challenging.

There’s good news on this front, however. Over the past few years, AI practitioners and researchers have developed several techniques to significantly reduce the volume of labeled data needed to build accurate AI models. Using these approaches, it’s often possible to build a good AI model with a fraction of the labeled data that might otherwise be needed.

Assembling lots of labeled data is expensive and difficult. Imagine that you’re the CEO of a manufacturer of home-office furniture. Your customers post reviews of your products on e-commerce sites and social media, and some of these reviews provide valuable insights into potential product defects and improvements.

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