Go — a strategy game.

A Go player attains excellence through endless practice. A baby learns to avoid touching hot stoves by feeling, rather than by listening to adults. Both the baby and the Go player learn through experience, rather than rules.

Why is this important for Artificial Intelligence?

The History

Determining the correct rules for any concrete task is often near-impossible due to the huge number of possibilities that exist in real life. That means that this type of approach often falls short.

A Decision Tree (a legacy algorithm)

Therefore, throughout most of AI history, researchers have searched for ways to mimic the learning that occurs through trial-and-error. Nevertheless, for the longest time, these algorithms were too slow, leading to the proliferation of faster, if-then logic and decision tree style systems. The problem with if-then logic, however, is that it’s tedious, inflexible, and limited to the developer’s creativity.

Luckily, with the inception of the back-propagation algorithm, Big Data, and improved computational power, the situation has changed.

A Simple Feed-Forward Neural Network (a type of learning algorithm)

The AI algorithms of today are often referred to as “Machine Learning” algorithms due to their crucial differentiating factor: that they learn from examples, rather than conforming to rules. Their newfound success has led to a revolution in AI applications over the past couple years. And, these algorithms are frighteningly good — even surpassing people. The only kicker is that machines require thousands of examples. Even for a trivial MNIST classifier (an algorithm that tells apart handwritten digits and/or letters by breaking them up into “classes”), it may take over ten to fifty thousand data points to attain human-like ability.

A heat map of what makes and does not make a picture one specific digit in the view of an MNIST classifier. Blue represents the former, and red, the latter.

For some applications this can prove to be a massive hindrance, given that often, one needs humans to manually generate data. Thus, it‘s been difficult for small developers to keep up with large companies with respect to AI models.

Our Plan

We wanted to gamify crowd-sourcing data acquisition. Paid crowd-sourcing data acquisition exists, but we believed we could get data cheaper by inducing fun. The fun of playing a game creates an incentive to keep going, meaning the corpus grows sustainably. The key to success is that both parties benefit.