Description
Most attention has been paid to the recent and impressive achievements of deep-learning systems that require extensive data and computation for training. Other approaches to artificial intelligence, including associative memory and Bayesian statistics, can deliver contextual reasoning with far fewer resources needed.
Interfaces that learn the user’s behaviors, preferences, and workflows over time can dramatically improve the subjective experience of using such a system. By delivering a personalized user experience that anticipates the needs of the user we can move away from a “one-size-fits-all” approach and instead create dynamic user experiences that are a better match for each individual’s way of working.
