Building an indoor NFT hydroponics system with Raspberry Pi monitoring

I love plants. I’m not quite sure why, but over the recent years, it’s been a growing love affair; pun intended.

I’m lucky to have room on my rooftop to grow lots of veggies in the summer. I’ve been building up capacity and I’m now up to 7 irrigated containers. I even managed to grow corn that was 9+ feet tall during my first summer! I’m now engaged in a ruthless battle with our beloved squirrels over domination of the garden’s bounty.

However, each year as our Montréal winter drew closer, I watched, powerless, as my once strong crops wrinkled away. This got me interested in finding out what my options were when it came to growing veggies indoors, and that lead me to hydroponics.

There is a sustainable development aspect to all of this which I find quite interesting. Growing most of the food we need locally seems like something we will have to do as a society pretty soon. I’ve been a big fan of Lufa Farms, who have been pioneers in that area, ever since they started. There are similar initiatives in most major cities. This technology is even being put in containers that are then shipped to the arctic to grow fresh food in the most arid climate on the planet!

This post isn’t an exact step-by-step guide. There is lots to know and learn, and great resources already exist online. Here, I’m going to take you through my journey and point you to relevant resources along the way.

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Human augmentation through artificial intelligence

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.

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Is your Chrome bigger than mine? How Chrome ate 21GB of storage

I’ve been playing with the macOS Sierra Golden Master for a few days and ran into the new storage optimization app that is now included in macOS. It prompted me that I was running low on disk space, and displayed the following dialog. The first item on the list was a bit shocking:

chrome-21gb

How could Chrome be using 21GB of my precious SSD space?!

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Actually, Marty didn’t go Back To The Future: Graphing the train sequence of BTTF3

In a hurry? Go straight to the graphs.

The dataset and notebook detailing how this was done are available in the companion repository.

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?

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Hacking an epic NHL goal celebration with a hue light show and real-time machine learning

See media coverage of this blog post.

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.

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