Researchers have designed an
algorithm that learns directly from human instructions, rather than an existing
set of examples, and outperformed conventional methods of training neural
networks by 160 per cent. But more surprisingly, their algorithm also outperformed
its own training by nine per cent -- it learned to recognize hair in pictures
with greater reliability than that enabled by the training, marking a
significant leap forward for artificial intelligence. This algorithm learns
directly from human trainers.
With this model, called heuristic
training, humans provide direct instructions that are used to pre-classify
training samples rather than a set of fixed examples. The heuristic training
approach holds considerable promise for addressing one of the biggest
challenges for neural networks: making correct classifications of previously
unknown or unlabeled data. This is crucial for applying machine learning to new
situations, such as correctly identifying cancerous tissues for medical
diagnostics, or classifying all the objects surrounding and approaching a
self-driving car.
More information: