Game-playing AI Vs. Predictive AI
What’s the difference between the creative power of game-playing AIs and the predictive AIs mostly used in business?
It’s how they learn!
The AI that thrives at games like Go uses an approach called reinforcement learning: AlphaGo (the Go champion AI algorithm) plays in a way that seems to go beyond just pattern recognition in data, it self-learns being creative with never before seen strategies!
AlphaGo was trained by having it play many millions of matches against itself. During these matches, the system had the chance to explore new moves and strategies, and then evaluate if they improved performance. Through all this trial and error, it discovered a way to play the game that surprised even the best players in the world!
Now the opportunities are to bring reinforcement learning to the business world. Any time you have to make decisions in sequence there is a chance to deploy it.
Let’s see an example in Retail:
To retain a customer and maximize long-term profit, sometimes it is necessary to sacrifice short-term profit, a non-natural approach for several algorithms. When introducing a new promotion, no data is available to understand the best correlations with the different types of customer and their last purchase. Reinforcement learning immediately begins to take decisions, sometimes of an “exploratory” nature, and improves them day after day