At present we are witnessing a tremendous interest in Artificial Intelligence (AI), particularly in Deep Learning (DL)/Deep Neural Networks (DNNs). One of the reasons appears to be the unmatched performance achieved by such systems. This has resulted in an enormous hope on such techniques and often these are viewed as all—cure solutions. But most of these systems cannot explain why a particular decision is made (black box) and sometimes miserably fail in cases where other systems would not. Consequently, in critical applications such as healthcare and defense practitioners do not like to trust such systems. Although an AI system is often designed taking inspiration from the brain, there is not much attempt to exploit cues from the brain in true sense. In our opinion, to realize intelligent systems with human like reasoning ability, we need to exploit knowledge from the brain science. Here we discuss a few findings in brain science that may help designing intelligent systems. We explain the relevance of transparency, explainability, learning from a few examples, and the trustworthiness of an AI system. We also discuss a few ways that may help to achieve these attributes in a learning system.