Is Deep Learning Possible Without Back-propagation?

Back-propagation algorithm in simpler terms can be typified as learning how to ride a bicycle. After a few unfortunate falls, one learns how to avoid the fall.  And every fall teaches how to ride the bike better and not lean too much on either side so as not to fall before reaching the destination. Today, back-propagation is part of almost all the neural networks that are deployed in object detection, recommender systems, chatbots and other such applications. It has become part of the de-facto industry standard and doesn’t sound strange even to an AI outsider. However, this invention is not so recent as it appears to be. This was introduced three decades ago by one of the pioneers of modern AI, University of Toronto’s Geoffrey Hinton. Back in 1986, people couldn’t grasp the significance of this technique and now the machine learning community can’t do without it. Back-propagation is an ingenious idea that also has its own set of disadvantages like vanishing or exploding gra