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.
By Michael Byrne|MOTHERBOARD
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 creating new exemplars, parsing objects into parts and relations, and creating new abstract categories of objects based on existing categories. The best machine classifiers do not perform these additional functions, which are rarely studied and usually require specialized algorithms.“
„A central challenge is to explain these two aspects of human-level concept learning,“ the authors continue. „How do people learn new concepts from just one or a few examples? And how do people learn such abstract, rich, and flexible representations?“