 I think that this summit has a very interesting characteristic, which is that the participants are from very, very diverse backgrounds. So in some sense, it's a learning experience and education experience for all of us. So from actually having the government people, the academia, all the companies, and all trying to talk about this theme of AI for good has been very interesting, at least for me to listen to these different ways of interpreting and working towards this goal. So in that sense, Joy Deep is with us with this localization, PhD thesis, and then Brian Colton with his scheduling of tasks, and Stephanie Rosenthal as the PhD student, the PhD thesis on interacting with humans and the symbiotic autonomy and asking for help kind of show, demonstrate the advancement of science while also generating some engineering to get the actual robot existing and present in our environments. Yeah, so definitely robots in some sense provide a different type of help than really just the knowledge that a cell phone or a laptop or an assistant, a digital assistant may provide. You have to go beyond thinking about just a digital assistant, which will be fantastic for doctors, for professors, for everything, and go beyond to these physical assistant, an assistant that moves in the physical space that is capable of going check something for you. So it's now the ability to have these mobile robots, and maybe we have to create more of tasks that they can help us with that can actually achieve other types of tasks. So it is definitely knowledge tasks, can you check these vital somewhere, but also really the physical tasks, transport these, bring me that, go and take this person there, take care of these while I go somewhere, or carry these to that place while I go somewhere else, or go and get me the referral while I wait here, or I'm really tired, get me a cup of water. So all these assistants that is at physical space and physical level and not just the knowledge itself. The humans have to make use of this good technology, this technology, make good uses, but these technology, AI and robots and learning and all sorts of like technology for data processing and learning did not come, did not fall from the sky. I mean it's not something that was given to us. It was invented by humans. It was invented by us. So in some sense we as inventors of course need to be careful about the bad uses, but we are trying to make good uses of this technology. And I do believe that these types of meetings and exposing the scientists to the governments, to the industries, to the social needs makes people be more informed of all these multiple aspects of the discoveries that people do under development. So in some sense it's a wall that needs this interdisciplinarity to eventually move forward towards having AI for good. I really think that the AI researchers are eager in some sense to engage with a variety of different people from the social sciences all the way to the actual technical people that know how to build code and machines. I think we are very aware of that, but the thing that I think it's more interesting is that AI became so present in our lives. I mean the recommendations on Amazon and Alexa talking with you and all sorts of like data we produce that people are becoming more and more unaware that AI has to do with these data. And so the more people get attached and interested on data, the more they eventually they will be able to collaborate with AI researchers and the more they get engaged with AI because if you really have a Fitbit suppose and you really start wondering, oh, I wonder what was the average that number of steps I took over the last end times or what is the prediction if I would go down this road versus that road, what would be better for my health and all of these, you start entering thinking AI, you are thinking about some kind of artifact that would be intelligent in terms of answering these complex questions that involve using the data, reasoning about objectives and eventually making counterfactuals, alternative searches and so it's more of like almost everybody through this data is getting ready if you delve into thinking how much you would actually want to know from the data that's collected is getting ready to collaborate with AI researchers on these problems that you would like to learn how to solve.