Source: The Morning Paper, Nov 2016
an important difference between learning systems that are fundamentally based on statistical pattern recognition, and learning systems that build some model of the world they can reason over.
The pattern recognition approach discovers features that have something in common – classification labels for example – across a large diverse set of training data.
The model building approach creates models to understand and explain the world, to imagine consequences of actions, and make plans.
People learn more than how to do pattern recognition, they learn a concept – that is, a model of the class that allows their acquired knowledge to be flexibly applied in new ways. In addition to recognising new example, people can also generate new examples, parse a character into its most important parts and relations, and generate new characters given a small set of related characters. These additional abilities come for free along with the acquisition of the underlying concept. Even for these simple visual concepts, people are still better and more sophisticated learners than the best algorithms for character recognition. People learn a lot more from a lot less, and capturing these human-level learning abilities in machines is the Characters Challenge.