One of the more popular methods of training artificial intelligence is through video games. Games can offer worlds with both the unexpected as well as specific tasks that need to be accomplished, crucial tools for any AI to learn in the real world. The games used have ranged from Minecraft to Mortal Kombat. Now you can add Mario Kart to the list.

Coded by Kevin Hughes, a developer at the company Shopify, the machine learning was done using TensorFlow. An open source software library, TensorFlow was originally developed by engineers and researchers affiliated with Google's AI efforts. On one its first races, Hughes says, "Mario drove straight into the wall and made no attempt to turn."

But things got better from there. While debugging, one thing that helped Hughes was recognizing what type of game Mario Kart is. It's "a very jerky game in that you typically don't take corners smoothly. Instead, players usually make several sharp adjustments throughout a large turn," he writes. "This jerkiness could explain why Mario didn't turn."

In a process he compares to teaching a sixteen year old how to drive, Hughes was "able to train an AI to drive a virtual vehicle using the same technique Google uses for their self-driving cars." He only had given himself a few days, and with that comes limitations: the car can only drive a select portion of the game's simplest track, Luigi Raceway.

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Given enough time, he says, "we could build a complete AI for MarioKart 64." He's released his data on Github in case anyone wants to grab a couple power boosters and continue on in his stead.

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