1.2: Tech that works

We found several promising technologies and endeavored to connect their successes to formative ideas in Cognitive Psychology. We focused a great deal on insights from instance-based view of cognition, and their connection to the many recent successes of machine learning applied to various classification problems. For example, problems that used to be identified as easy for for people and hard for machines, like face and object recognition, word and document similarity analysis, gesture, posture, and emotion recognition, voice transcription, and language translation, all have working computational solutions. In general, many of these problems were solved by the same kind of solution: gather a large database of examples, represent each example as a collection of digital features, then train a machine learning algorithm on the examples and measure whether the classifier can generalize to accurately classify new examples that were not from the training set. This big-data approach often works quite well. Why does it work? My view is that success was anticipated by instance theories.