A new type of learning model uses far less data than conventional AIs, allowing researchers with limited resources to contribute. One such recent advance is called “less than one”–shot learning (LO-shot learning), developed by Ilia Sucholutsky and Matthias Schonlau from the University of Waterloo.
Allowing AIs to learn with less plentiful data helps to democratize the field of artificial intelligence. Not only does LO-shot learning make the barriers to entry lower by reducing training costs and lowering data requirements, but it also provides more flexibility for users to create novel data sets.
By reducing the time spent on data and architecture engineering, researchers looking to leverage AI can spend more time focusing on the practical problems they are aiming to solve.MORE