The race between humans and robots as teachers

(part 4: Worldwide equality of education using AI)

The race between humans and robots as teachers

‘That’s one small step for man, one huge step for humanity.’ These words by Neil Armstrong were first said when he planted a flag on the moon. The moon landing in 1969 is one of the proudest achievements of humankind.

Overcoming the impossible and breaking barriers is a human drive. The Wright brothers’ were the first to achieve a motorized airplane flight in 1903, Chuck Yaeger was the first pilot to break the sound barrier in 1947 and the team of IBM were the first to defeat a human at the cognitive challenging task chess with Deep Blue in 1997.

There are still many challenges humanity faces. One of these current impossible feats: worldwide equality of education. Artificial Intelligence(AI) is a gamechanger that could partially solve this challenge. Because chess is low hanging fruit in AI, I truly believe a leading role can be played by the chess community.

Breaking the cycle of poverty

Education and poverty are inextricably linked. According to an UNESCO paper, 263 million kids are out of school. Just 14% of young children in poor countries complete their studies at secondary level. Young children in poverty may stop going to school so they can work. This leaves them without numeracy and literacy skills. Skills they need to further their career. According to numbers of the UN Educational, Scientific and Cultural Organization , 69 million teachers are needed before 2030 to achieve universal primary and secondary education.

Taking these numbers into account, AI in education has the possibility to transform the world for the better. How does chess help with solving worldwide equality in education, you might ask yourself? Isn’t it too far reached? I beg to differ. Humans innovate by using an analogical problem solving approach: copy-adapt-paste.

Copy-Adapt-Paste

Being the first to break a barrier or ‘plant a flag’ so to speak, has a certain historical significance and marketable advantage. Think for example about IBM and Kasparov being defeated in 1997 or more recently Google DeepMind with AlphaZero becoming the best Go and chessplayer in just a couple of hours by teaching itself. Huge companies like Google and IBM look for interesting challenges, but at the same time huge markets they can penetrate to earn a lot of money. They copy the ideas learned in chess and transfer some of these ideas to other domains which are more profitable. They adapt the ideas for the new domain to a certain extent, because every domain has their own complexities. And they paste this solution. Sometimes the implications of innovations can be far reaching and goes way beyond the borders of the domain the innovation was created.

When you look at innovation, there’s often a crossover between industries which at first sight, looks like they have nothing in common. However, when you take a closer look there are staring similarities. Think for example about the equation of Arpad Elo. It was first developed to calculate rating in chess to improve on the in that time Harkness rating system. The interesting thing is, the equation was ‘copy-adapt-pasted’ to other domains. Mark Zuckerberg used the equation in one of the first versions of Facebook (Facemash) to help out with deciding the beauty of students depending on the feedback other students gave on photo’s they saw on their screen. Other companies use it for deciding which next math problem they show kids to solve with their adaptive learning model. In sports the equation of Arpad Elo is widely used. Multiplayer competition in videogames, American Football, Major league baseball, table tennis and soccer. All make use of this innovation which was originally created for the chess community.

Or think about Henry Ford and the automobile industry. He changed the way the industry built cars. He introduced the assembly line. What most people don’t know, he borrowed the idea for the assembly line from the meat packing industry in Chicago. In the meat industry they used it to efficiently ‘strip’ everything. Ford turned this process around with cars and he used it to build a car with all the different elements you need to put everything together.

The example I love the most is about the Artificial Christmas Tree. To share you a funny fact you can share during Christmas. The toilet brush industry created the artificial Christmas tree. It’s the same material. In a way, many people have a huge green toilet brush with shiny balls in the living room during Christmas.

The point I want to make is: inspiration comes from unexpected sources by using an analogical problem solving approach. Topics like math and reading are of course by far the most important topics to reach worldwide equality of education. This could take away a big disadvantage some kids have and potentially break the chains of poverty. As low hanging fruit for AI, chess can function as a frontrunner.

Someone else already solved your problem

Chess could help by inspiring other domains with how we create algorithms to detect didactical patterns (i.e. Explainable Artificial Intelligence), the entire (online) infrastructure of data, connecting online and offline communities, privacy issues, ownership of data and much more. Chess can also learn a lot by looking at other industries concerning Human Performance and incorporating these ideas in chess. It’s a cliché, but a truism: don’t reinvent the wheel. Someone else already solved your problem.

Make sure to also read part 1, read part 2 and read part 3 of this series of articles.

Written by Rick Lahaye
Rick Lahaye is the founder of ChessAnalytics. A company using Artificial Intelligence and learning analytics to create personalized training programs in chess. He is the author of 'Pushing Boundaries', a book based on scientific research about what 19 Olympic gold medal winners and world champions do to push their limits and reach the top.