Strong Machine Learning: a Way Towards Human-Level Intelligence

Machine learning has achieved remarkable success with deep learning technologies. However, these methods are often inefficient in terms of resources; they require large datasets, many parameters and consume much computational power. In this paper, I define a general strategy for machine learning, named _strong machine learning_, which aims to create resource-effective machine learning models. Under strong machine learning fall all the approaches that learn inductive biases during an initial phase and later apply those inductive biases to make models more effective learners. Several strong machine learning methods already exist and some are very popular exactly due to their effectiveness. However, strong machine learning is in its infancy and a lot more can be done. In order to further advance AI, we need to direct our effort toward developing even better, more powerful strong machine learning methods.