This summer I will be going for my 2nd summer internship at Google's Computer Vision Research Group in Mountain View, CA. My first real internship ever was last summer at Google -- I loved it.
There are many reasons for going back for the summer. Being in the research group and getting to address the same types of vision/recognition related problems as during my PhD is very important for me. It is not just a typical software engineering internship -- I get an better overall picture of how object recognition research can impact the world at a large scale, the Google-scale, before I finish my PhD and become set in my ways. Being in an environment where one can develop something super cool and weeks later millions of people see a difference in the way they interact with the internet (via Google's services of course) is also super exciting. Finally, the computing infrastructure that Google has set up for its researchers/engineers is unrivaled when it comes to large scale machine learning.
Many Google researchers (such as Fernando Periera) are big advocates of the data-driven mentality, where using massive amounts of data coupled with simple algorithms has more promise than complex algorithms with small amounts of training data. In earlier posts I already mentioned how my advisor at CMU is a big advocate of this approach in Computer Vision. This Unreasonable Effectiveness of Data is a powerful mentality yet difficult to embrace with the computational resources offered by one's computer science department. But this data-driven paradigm is not only viable at Google -- it is the essence of Google.