Sure thing yeah as I'm sure Vivek will describe much more expertly than I would ever be able to. I think one of the kind of ingredients here was that in the AI field in general and in particular at Google there had been some really outstanding progress in the field of large language models and we were increasingly seeing that with scale of these models was coming I think was being published as sort of emergent properties and really kind of surprising new capabilities for AI systems that were arising from these models as these new architectures were being developed and scaled up and put a task across a really broad variety of contexts. And so as medical AI researchers I think our first question was to start just like we did in the era of CNN's and that first wave of discovery when Vivek and I originally started working together. There were very similar questions arising here which is I was always taught you know make the care of the patient your first concern. And so the immediate questions that come to mind for me around this kind of powerful technology was in the much more challenging setting of healthcare where if a language model makes a mistake or makes an error there's a sort of much more perceptible risk or harm than in some other contexts for example in creative applications and other things. So one of the scientific questions I think that arises in that moment when the technology is coming to fruition is to start asking the extent to which clinical knowledge and medically important information is actually encoded in these systems to begin with and to start asking scientific questions around how to best measure that but also how to begin to put metrics around it and maybe even then optimize and develop it. So at the outset of a research field we try to make contributions that are generally useful and thoughtful aligned with the values of the practice of medicine and of what matters to patients and people. And so that was kind of the inspiration for the first paper and I think the first thing we did was to look at question answering in the broadest sense because it seemed to be a very foundational property of these large language models in all of their kind of foundational work. And in healthcare we took a fairly pragmatic approach I mean we were very lucky that this is a space and healthcare natural language processing is a space in which there's been actually some fantastic work that precedes these large language models and it's great to be on the call with the likes of Andy who has been thought leading exactly that for many years and I think we therefore were very fortunate because there are plenty of open datasets which pose medical questions and associate them with answers as a sort of foundation for which you can start testing the ability of these models to do various things.