From DL to Agent Based Modelling

Deep learning has seen a lot of recent success in tackling difficult problems that require extracting useful information from large amounts of data. Such work has shown promising results for learning difficult tasks in image recognition, natural language, time-series forecasting, etc. Traditionally, these networks have millions of parameters that are learned using an optimization algorithm. Optimization informs parameters how to update to capture features of the input relevant for learning the task at hand. While these models are often well suited for the tasks on which they are applied, they have not yet shown the ability to bootstrap a-priori knowledge for novel tasks. Even the limited approaches that show some transfer of previously learned knowledge don't scale, in terms of resources, in the same manner as seen in biological brains.