Bugs are prevalent in software systems. Needless to say, these bugs need to be identified, managed and fixed to improve software quality and increase customer satisfaction. Unfortunately, these tasks are non-trivial; many bugs remain hidden and unresolved for weeks (or even years!). Can AI help? Of course! AI can be trained on rich historical data to allow it to mimic developers in squashing bugs (and more!). For AI to work well, it often needs to be trained on a sizable amount of data. Fortunately, many projects maintain large historical data in various repositories that are publicly available. Although full automation is not feasible yet (at least in the general sense), AI-infused solutions can support developers in their quest to identify, manage, and fix bugs (and thus remaining successful despite living with bugs). This talk will provide an overview and reflection of the large body of work that builds automated tools that leverage the power of AI, trained on rich data in various repositories, for various tasks in the bug identification and resolution process. Some open challenges will also be presented, with the goal of encouraging more research in this exciting area in the intersection of Software Engineering and AI.