Why math is easy for AI but gardening is not: Moravec's paradox

Artificial intelligence (AI) systems, powered by massive data and sophisticated algorithms -- including but not limited to -- deep neural networks and statistical machine learning (ML)(support vector machines, clustering, random forest, etc.), are having profound and transformative impact on our daily lives as they make their way into everything from finance to healthcare, from retail to transportation. Netflix movie recommender, Amazon's product prediction, Facebook's uncanny ability to show what you may like, Google's assistant, DeepMind's AlphaGo, Stanford's AI beating human doctors. Machine learning is eating software. However, one of the common features of these powerful algorithms is that they utilize sophisticated mathematics to do their job -- to classify and segment an image, to arrive at the key decisions, to make a product recommendation, to model a complex phenomenon, or to extract and visualize a hidden pattern from a deluge of data. All of these mathematical processes are, quite simply, beyond the scope of a single human (or a team) to perform manually (even on a computer) or inside their head.