I will describe two contradicting lines of work. On one hand, a practical work on autonomous driving I was doing at Mobileye, in which deep learning is one of the key ingredients. On the other hand, theoretical work I was doing at the Hebrew university showing strong hardness of learning results. Bridging this gap is a great challenge. I will describe some approaches toward a solution, focusing on "practically relevant theory" and "theoretically relevant practice".