With the emergence of technology that automates knowledge work, an entirely new part of the labor force is worried about job security. These concerns were once isolated to people who did repetitive physical labor, but today it can seem like any work at all might be replaced by AI. The 15-to-30-year forecast of the global labor market is a giant question mark. How will we navigate the transition? How can you know how susceptible you are?
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?
If you’re a business leader with access to a technology budget, over the past decade, there are a handful of phrases that have suddenly became impossible to ignore. At this point, you have basically no choice but to act like you really understand what they mean. Here’s an inexhaustive list, in roughly the order that they blew up: Big data Predictive analytics Data science Machine learning Deep learning Artificial intelligence At every conference, you’ll find some industry leader declaring “X is dead, Y is the future”—where “X” and “Y” are both items from the list above, and where both X and Y are neither dead nor the future.
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.
Welcome to another edition of Demystifying Overused Marketing Terms. The last edition was about Big Data, which you can find here. This time, we’re talking about “predictive analytics” and “machine learning.” The reason we’re doing this is that just the other day I was walking around the expo floor of a big sales conference in town and overheard the following: Guy A: We don’t do predictive anymore, now it’s all about machine learning.
I can’t remember the last time I made it through an airport terminal without seeing a giant abstract poster up on the wall with a vague heading like “Big Data is here: are you ready?“, or “Big Data: the new natural resource”. Often, the heading is in front of a photo of clouds, or a gazelle, or a server room, or some other such thing. So, for the weary business traveler who at this point is too afraid to ask what “Big data” refers to, here’s a quick breakdown.
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.