Last week, I published two posts that relate to a very interesting and socioeconomically relevant machine learning topic: human augmentation through artificial intelligence. There have been many examples that made their way in the mainstream media where we’ve seen AI push the limits of what was possible, especially since deep learning really took off. One of the big recent examples is of course, AlphaGo.
But human augmentation isn’t about what AI can do by itself. Rather, it’s about how AI can be used to assist a human so that he or she can be much more efficient at performing a given task. It can be to make humans faster at it, or allow them to produce work of higher quality. It’s about using AI as a tool, just like any one of the hundreds of tools we all use in our everyday life.
In Montréal this time of year, the city literally stops and everyone starts talking, thinking and dreaming about a single thing: the Stanley Cup Playoffs. Even most of those who don’t normally care the least bit about hockey transform into die hard fans of the Montréal Canadiens, or the Habs like we also call them.
Below is a Youtube clip of the epic goal celebration hack in action. In a single sentence, I trained a machine learning model to detect in real-time that a goal was just scored by the Habs based on the live audio feed of a game and to trigger a light show using Philips hues in my living room.
The rest of this post explains each step that was involved in putting this together. A full architecture diagram is available if you want to follow along.
At Datacratic, one of the product we offer our customers is our real-time bidding (RTB) optimisation that can plug directly into any RTBKit installation. We’re always hard at work to improve our optimisation capabilities so clients can identify valuable impressions for their advertisers. Every bid request is priced independently and real-time feedback is given to the machine learning models. They adjust immediately to changing conditions and learn about data they had not been exposed to during their initial training. This blog post covers a strange click pattern we started noticing as we were exploring optimized campaign data, and a simple way we can use to protect our clients from it. Read more…