The results of the 2016 US Presidential election suggest that the glass ceiling for women in politics remains unshattered. This reminded me of an interesting pattern that the Kemvi Engine recently found in the historical win/loss data of one of our enterprise customers. This customer tended to close deals with companies that have a higher than average ratio of female executives. This sparked the question: what is the gender distribution among executive titles and sectors?
Maybe you want to change careers and you’ve heard that lots of people are hiring front-end engineers, data scientists, and mobile developers. You’re tempted to try Coursera or Codecademy because you really want to get into this coding thing that’s in such high demand. Salaries are high and every company seems to need “hackers”. But the big question remains: what technology should you learn? In the end, companies look for people with specific capabilities, and you want to maximize the probability of being hired.
AWS just announced the release of their new p2.16xlarge instances, making this an especially great time to get started using a cloud service for deep learning. If you’re a developer or engineer interested in learning what all the fuss is about, the best way to learn is to spin up an instance and try to build something. We’ve written up a quick getting started guide on the best options we found for quickly creating a versatile development environment.
As I woke up recently to the news of a 1000-point drop of the Dow Jones, I thought back to a paper we recently published about predicting stock market crashes. Over the last few decades, the US stock market has had several crashes, none of which compare to the 2007-2008 financial crisis, where the market lost about 50% of its value in 6 months. Such a massive loss can only be explained with a combination of bad news and crowd behavior.
