I work in the intersection of these, though primarily from SLAM background. The overlap between these communities has been increasing in the past two years.
While I think this is great, I also see that a lot of people working in the field try to take Deep Learning as a 'one-stop' approach to solving SLAM. This is worrying. Any particular methodology, if embraced without understanding pros/cons, can lead to stagnation and local maxima. It was CRFs before that, and Factor Graphs, Particle Filters, or EKF Slam before that.
I speak four languages on a daily basis (work, work, family, friends). Many of my colleagues do the same. Some don't. I can't say I have noticed any cognitive difference between the two sets that can be explained by multilingualism.
Purely subjective - I have noticed that people in my workplace who exercise more or play music more have a bit of an edge over those who don't.
Thank you. I work in a very old-fashioned research lab that we managed to drag to git recently. I haven't been able to convince the team to read the git book. This could be more helpful.
I use C++ in my work - robotics/Comp-vision. We need it for the performance and the cross-platform characteristics. It is a highly powerful language once you get used to its complexity. In my experience, it is the most popular language in the robotics community, though not for prototyping (Matlab, python are more common for prototyping)