Understanding in Artificial Intelligence

However, this progress is largely driven by increased computational power, namely GPU's, and bigger data sets but not due to radically new algorithms or knowledge representations. Artificial Neural Networks and Stochastic Gradient Descent, popularized in the 80's [3], remain the fundamental building blocks for most modern AI systems. While very successful for many applications, especially in vision, the purely deep-learning based approach has significant weaknesses. For instance, CNN's struggle with same-different relations [4], fail when long-chained reasoning is needed [5], are non-decomposable, cannot easily incorporate symbolic knowledge, and are hampered by a lack of model interpretability. Many current methods essentially compute higher order statistics over basic elements such as pixels, phonemes, letters or words to process inputs but do not explicitly model the building blocks and their relations in a (de)composable and interpretable way.