How big data helped Santa save Christmas
Every year Santa delivers millions of presents to millions of children around the world.
When that was written, around a hundred years ago, the world was a much smaller and less complex place.
Santa's systems, the Workshop and the Mail room at the North Pole were geared to maximise elven efficiency.
The elves had a well-defined system to sort through the children's mail - using their well-developed sense of smell to filter the requests from good children from those coming from naughty children. The choice of gift was much simpler - a small selection of cars or guns for boys: dolls for girls - with the occasional pony thrown in for the really well behaved.
Over the last hundred years, Santa had invested heavily in automation and mechanisation. This means that a smaller number of elves are able to deal with a higher volume.
But, around 20 years ago two trends emerged that have threatened to overwhelm his operation.
1. The world's population has grown exponentially. There are now many more children requesting gifts.
2. The Internet has created a generation of children, the millennials, that are easily bored, that are exposed to a great variety of choice, and that are comfortable with electronic media and instant gratification.
Santa set up an elven committee to define the major problems facing his organisation.
After months of research the elves were able to categorise the technical challenges into four main areas:
Santa now gets billions, rather than millions, of requests for gift from children around the world. The elven population has not exploded at the same rate - in fact the population of workers is still roughly the same size as it was in the days of a few million letters. The systems at the Pole were being overwhelmed. Santa had to find a new approach
Very few of Santa's requests come via post (although the channel cannot be ignored.) Santa receives Christmas lists via email, via WhatsApp, via Social Media, and several million still come in via traditional mail.
The elves can no longer filter this mail using smell - a new approach had to be found. Children also now have much more to choose from in the range of gifts. This puts pressure on Santa's supply chain as they cannot wait until the requests come in before ordering stock and beginning construction. Santa had to find a way to predict which gifts would be popular before the rush.
The majority of requests to Santa still come in the twenty day period before Christmas. But children are much more likely to change their minds as new toys and games are advertised in the days before Christmas. This issue compounded the challenges of variety and volume.
Some children send multiple requests, via different channels, hoping that they will receive more than one present. Multiple languages, regional slang, poor spelling and grammar - these are all challenges that Santa and his team face very day. Without quality data Santa risks making the wrong decisions - disappointing the good children, or rewarding the naughty.
In summary? Santa’s existing systems were not equipped to deal with the Information age!
The elves proposed to augment the automated processes with machine driven predictions.
This would mean using statistical models to predict which toys would be popular. By analysing solution media and advertising Santa's manufacturing team are able to identify the broad categories of toys, and even specific items, that will be in high demand in December. This allows the team to get a head start on manufacturing, without waste.
The elves had to improve the process for filtering requests from good and bad children. Once again, social media sources give a good indication of character. The elves supplement this with data from phone calls, emails and other communication sources.
The elves must analyse and aggregate the sentiment of each child's parents, teachers, friends and family in order to place each child into the category - from very good to very naughty - that will determine whether they get exactly what they asked for, or a lump of coal.
The elves must use machine learning techniques to read through the mountains of mail - paper and electronic and match gifts requested to those available in the production line. In some cases, compromises must be made. Children may be allocated gifts that are very similar, but not identical, to that which they had requested. Santa's recommendation engine combines ensure that each child gets the most appropriate gift to their request - depending on their segmentation.
Finally, Santa's team use sophisticated data cleansing and matching engines to identify and filter duplicate requests, even across channels.
Without these investments in big data Santa would be out of business!
Thankfully, Santa was able to meet the challenges posed by the information age. Have you?
For those that celebrate Christmas - have a good one! May Santa filter your requests appropriately, and give you what you asked for.
More importantly, I hope that you will have a good day, with friends and family - because Christmas is about more than gifts!
Gary Allemann is MD at Master Data Management.
Chinese Firm Taigusys Launches Emotion-Recognition System
In a detailed investigative report, the Guardian reported that Chinese tech company Taigusys can now monitor facial expressions. The company claims that it can track fake smiles, chart genuine emotions, and help police curtail security threats. ‘Ordinary people here in China aren’t happy about this technology, but they have no choice. If the police say there have to be cameras in a community, people will just have to live with it’, said Chen Wei, company founder and chairman. ‘There’s always that demand, and we’re here to fulfil it’.
Who Will Use the Data?
As of right now, the emotion-recognition market is supposed to be worth US$36bn by 2023—which hints at rapid global adoption. Taigusys counts Huawei, China Mobile, China Unicom, and PetroChina among its 36 clients, but none of them has yet revealed if they’ve purchased the new AI. In addition, Taigusys will likely implement the technology in Chinese prisons, schools, and nursing homes.
It’s not likely that emotion-recognition AI will stay within the realm of private enterprise. President Xi Jinping has promoted ‘positive energy’ among citizens and intimated that negative expressions are no good for a healthy society. If the Chinese central government continues to gain control over private companies’ tech data, national officials could use emotional data for ideological purposes—and target ‘unhappy’ or ‘suspicious’ citizens.
How Does It Work?
Taigusys’s AI will track facial muscle movements, body motions, and other biometric data to infer how a person is feeling, collecting massive amounts of personal data for machine learning purposes. If an individual displays too much negative emotion, the platform can recommend him or her for what’s termed ‘emotional support’—and what may end up being much worse.
Can We Really Detect Human Emotions?
This is still up for debate, but many critics say no. Psychologists still debate whether human emotions can be separated into basic emotions such as fear, joy, and surprise across cultures or whether something more complex is at stake. Many claim that AI emotion-reading technology is not only unethical but inaccurate since facial expressions don’t necessarily indicate someone’s true emotional state.
In addition, Taigusys’s facial tracking system could promote racial bias. One of the company’s systems classes faces as ‘yellow, white, or black’; another distinguishes between Uyghur and Han Chinese; and sometimes, the technology picks up certain ethnic features better than others.
Is China the Only One?
Not a chance. Other countries have also tried to decode and use emotions. In 2007, the U.S. Transportation Security Administration (TSA) launched a heavily contested training programme (SPOT) that taught airport personnel to monitor passengers for signs of stress, deception, and fear. But China as a nation rarely discusses bias, and as a result, its AI-based discrimination could be more dangerous.
‘That Chinese conceptions of race are going to be built into technology and exported to other parts of the world is troubling, particularly since there isn’t the kind of critical discourse [about racism and ethnicity in China] that we’re having in the United States’, said Shazeda Ahmed, an AI researcher at New York University (NYU).
Taigusys’s founder points out, on the other hand, that its system can help prevent tragic violence, citing a 2020 stabbing of 41 people in Guangxi Province. Yet top academics remain unconvinced. As Sandra Wachter, associate professor and senior research fellow at the University of Oxford’s Internet Institute, said: ‘[If this continues], we will see a clash with fundamental human rights, such as free expression and the right to privacy’.