We argue that, despite the spectacular achievements of machine learning techniques, we are still far from Artificial General Intelligence (AGI). A big step toward this goal would be to develop autonomous systems capable of replacing human agents working in complex organizations, as envisioned by the IoT. This requires in particular, the convergence between Computing and AI ? ?integrating data-based machine learning and model-based systems engineering. We discuss the relevance of existing criteria for comparing human and machine intelligence and show some notable ? ?analogies and differences between scientific knowledge and that produced by neural networks. Emphasizing that autonomy is an important step towards AGI, we present a characterization of ? ?autonomous systems, and show key? differences with mental systems equipped with common sense knowledge and reasoning. We conclude by advocating challenging work directions, including the ? ?development of a new foundation for systems engineering and scientific knowledge, and the joint exploration of physical and mental phenomena that embody human intelligence.