GitHub is testing a machine learning algorithm to identify the vast array of programming languages across the platform. Why does this even matter? Won’t developers checking in their code know what language they wrote said code in? True, but think beyond the current use case and how GitHub can add value with AI.

But announcing the project this week, GitHub machine learning engineer, Kavita Ganesan wrote, “When some code is pushed to a repository, it’s important to recognize the type of code that was added for the purposes of search, security vulnerability alerting, and syntax highlighting—and to show the repository’s content distribution to users.”

I’ve always wondered that with advances in material sciences and production methods how will the design of buildings change. Bjarke Ingels talks about the future of architecture, floating cities, and LEGOs in this TED talk.

Here’s an interesting article on building AI solutions for board games and where it works well and does not work quite so well.

Impressed by DeepMind’s AlphaZero achievement with game of Go, we tried to use a similar approach to implement AI for the highly acclaimed board game, Azul. We discovered that reinforcement learning is not a necessary ingredient of successful solution – and we also learned that using your favourite tools can sometimes lead you astray.

Each day around a third of all food harvested or produced around the globe is wasted. This means that about 1.3 billion tons of food feeds no one.  IKEA’s restaurants serve 680 million people each year and the company takes food waste seriously.  IKEA has enlisted AI in its sustainability efforts.

IKEA is a proponent of the “circular economy”, which is an economic system based on minimizing waste and making the most of resources. It is basically the opposite of taking materials, manufacturing products (or food), and then disposing of the end product. Instead it is a regenerative approach that reduces waste.