Pinaki Laskar on LinkedIn: #selfdrivingcars #machinelearning #deeplearning

There are two fundamental reasons: To deeply understand the world for machine intelligence and learning and human intelligence replacing statistical independence with causal world model; To create a deep understandable AI instead of the Explainable AI; To build the Meta-disciplinary AI (Meta-AI) following the structural algorithm: Transdisciplinary AI (Trans-AI) the World Hypergraph Data Ontology AI Models ML/Deep Neural Networks Human Intelligence; First, It takes a human about 20–30 hours to learn how to drive a car, while it takes tens of thousands of hours to train a neuralnetwork to achieve this same capability. Even after all of these years of training and despite of using the latest and greatest in processing and sensor technology, #selfdrivingcars are still not deemed road-safe. We can train our learning model to recognize many of these situations, but there is an infinite number of them and even after millions of miles driven, the #machinelearning model will not have experienced anywhere near all of them. Because #deeplearning models do not have an inherent understanding of how the world works. They do not know any laws of physics and neither do they know ethics or even liability laws.