DBTB INT Gary Kazantsev

sure my name is Gary kazantsev and I run the machine learning engineering group at Bloomberg so I didn't actually have any expectations coming here is the first time a time at this conference the first time I'm presenting here and I have been very pleasantly surprised by the quality of the submissions and the caliber of the audience so it is also always very nice to be in a collegiate atmosphere with people who are working on the same problems as you are so that has been a very unexpectedly Pleasant experienced well I think we are at a very interesting point in terms of development of machine learning and data science we are at an intersection of kind of two curves one of which is the availability of computational resources which keeps growing in a roughly exponential way and on the other hand similar al-bayt slower rate of growth for the availability of machine readable data so things are possible today which were strictly impossible even 10 years ago it's just problems can be solved today which could not have been done really at any point previously so I think there is a lot of potential in terms of the kinds of problems we can solve going forward witness for instance things like self-driving cars right then the alpha go winning the go tournament and then I finger is going to be a lot more where that came from I so that one is easy Bloomberg is a technology company and one of the key things that I wanted to communicate is just that fact that we are a tech company at a large and complicated tech company that solves a very diverse and very exciting array of different problems and I wanted to tell the audience a little bit about the problems that we find most interesting I think it's going to be very generic advice because the only way to become good at something as to practice you do need a lot of mathematical background you can never learn enough enough so I think my advice would be pick the hardest problem you can solve I solve that one then pick the next one that will make you a great data scientist or a great mathematician or a great piano player really great anything you