For many, December is a time of great joy. But for one man, it is also a time of great burden. While millions eagerly await the big day, Santa toils in the near total darkness of Lapland, evaluating children and preparing gifts to disperse on an epic all-night voyage across the Earth. But a growing population has led to Santa encountering some scalability issues that threaten his entire operation.
Fortunately for Santa, technology caught up to the enormity of his task in the nick of time. Earlier this year, Santa’s elves reached out to us at Canotic looking for some data labeling expertise in order to help train machine learning (ML) models to reduce the village workload.
At Canotic, we believe AI should be for everyone, and that includes Santa, so let’s explore some of the ML techniques he and the elves are starting to employ in and around the workshop.
Naughty or nice?
There are an estimated 2.2 billion children on Earth. The fact that Santa has been able to manually conduct such an enormous analysis almost defies belief. Thanks to categorization algorithms, Santa is now able to simply define the parameters for what he deems “naughty” and “nice” and feed the data he has amassed on all the Earth’s children through the algorithm. The result? Almost instantaneous categorization of every naughty and nice child on Earth.
Along with the information provided by his trusty elves on shelves, Santa’s dataset has also recently come to include publicly accessible information, such as social media posts (evaluated through sentiment analysis algorithms), so online trolls should expect little more than a lump of coal this Christmas.
All I want for Christmas is… to know what people really want
Optical character recognition (OCR) is a real time saver for Santa. The Finnish Posti Group struggles to handle the avalanche of post from children all over the world earmarked for Santa’s Lapland hideout. Now imagine the time Santa and his elves spend having to examine each letter, decipher the often illegible handwriting, and determine what gifts in what numbers need to go into production. Scaling this kind of work has proven to be a nightmare. But no longer. Now, Santa simply passes the letters under a scanner and feeds the images through our image text transcription data program, providing machine-readable text identifying the sender alongside the desired presents.
Furthermore, Santa uses intent recognition on the OCR output to understand what the letters really mean. For those children who perhaps are not very good at articulating their wishes or are too embarrassed or modest to ask for their most-desired gift, Santa’s intent recognition algorithm can parse wish lists to see past what is said to what is meant.
What of those children that have come out the “nice” side of the naughty-or-nice algorithm but have not had the time to send a letter to Santa due to a demanding school play rehearsal schedule? Well, this was a real sore spot for Santa for a long time, demanding time-consuming research to arrive at the perfect, most thoughtful gift in each instance. Thankfully, recommendation engines make this troublesome process a thing of the past. Santa can use existing historical data—such as age, previous gifts, interests, and the like—as an input. The recommendation engine draws on a vast collection of toy and product images—tagged with metadata labels by an image tagging algorithm—to automatically output the best possible present option.
Avoiding Christmas catastrophe
Given the intensity of work conducted over the Christmas period, hardware failures are inevitable. But only recently has Santa been able to deploy computer vision techniques throughout his village to automatically spot faults and failures in critical machinery. This allows Santa to address damage to assembly line conveyor belts and his trusty sleigh, which undergoes extensive testing and improvements in the lead up to Christmas, before it causes real trouble.
Giving the reindeers a rest
On the subject of that sleigh, Santa’s elves have spent much of the year sending Canotic images to be labeled through semantic segmentation, bounding boxes, and line annotations. Santa has used this labeled data to prepare a state-of-the-art computer vision model that transforms his sleigh into a largely automated vehicle, capable of navigating the most ferocious snowstorms and the densest of urban areas. This allows the reindeer to take it easy this year, fulfilling a largely ceremonial role at the head of the sleigh.
Santa is still kept busy, even with ML about the village, but the reindeer, in their new role as figureheads, have plenty of time to kill, getting into mischief. As we explored in our AI in agriculture post, facial recognition has expanded beyond people into the animal kingdom. Santa makes use of the tech to monitor his reindeer’s well being, including spotting any signs of unpleasant behavior towards Rudolph because of his glorious nose.
All in all, this year is set to be Santa’s most relaxed Christmas yet. Thanks to Canotic, machine learning is now carrying much of Santa’s burden, allowing him more time to socialize with the elves and plan more elaborate ways to spread some Christmas cheer.
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