acm-header
Sign In

Communications of the ACM

ACM Opinion

'Small Data' Is Also Crucial for Machine Learning


View as: Print Mobile App Share: Send by email Share on reddit Share on StumbleUpon Share on Hacker News Share on Tweeter Share on Facebook
Illustration of a brain rendered as small cubes representing data.

The existence of techniques such as transfer learning does not seem to have reached the awareness of policy makers and business leaders making decisions about AI funding and adoption.

Olemedia Getty Images

Some of the most prominent artificial intelligence (AI) breakthroughs in the past decade have relied on enormous data sets. But AI is not only about large data sets; research in "small data" approaches has grown extensively over the past decade—with so-called transfer learning as an especially promising example.

Small-data approaches such as transfer learning offer numerous advantages over more data-intensive methods. Enabling the use of AI with less data can bolster progress in areas where little or no data exists, such as in forecasting natural hazards that occur relatively rarely or in predicting the risk of disease for a population set that does not have digital health records.

From Scientific American
View Full Article


 

No entries found