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.
Two weeks ago was Back To The Future Day. October 21st, 2015 is the day Marty and Doc Brown travel to at the beginning of the second movie. The future is now the past. There were worldwide celebrations and jokes, from the Queensland police deploying a hoverboard unit, Universal Pictures releasing a Jaws 19 trailer and even Health Canada issuing an official recall notice of DeLorean DMC-12 because of a flux capacitor defect that could prevent the car from traveling through time.
I love the trilogy and as many people probably did that week, I rewatched the movies. I also wondered if there was any fun BTTF data science project I could do. While watching the climactic sequence at the end of the third movie, I realized that as the steam locomotive pushes the DeLorean down the tracks, we get many data points as to the speed of the DeLorean. Marty is essentially reciting a dataset, all the way from 1885.
That made me ask the 1.21 Giga Watts question: Do they really make it to 88 miles per hour before they run out of tracks?
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.