A New Object-Recognition Algorithm Could Change the Face of Machine Learning
The basic principle of machine learning is training. As humans, we can learn very profound things from single examples—spoiled milk tastes bad, fire is hot—but machines need more because they learn statistically. Machines depend upon data. Or this is the current state of things, anyhow. It may prove to be less fundamental than is usually assumed, according to a study published this week in Science. The report, which comes courtesy of researchers at NYU and MIT, introduces the Bayesian program learning (BPL) framework, a new machine learning model capable of mimicking the human mind’s capacity for generalizing from single examples. It’s a model that “learns to learn.” “People learn richer representations than machines do,” the paper notes, “even for simple concepts, using them for a wider range of functions, including…
Link to Full Article: A New Object-Recognition Algorithm Could Change the Face of Machine Learning
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