2.5 How Does AI Learn?

Now that know what AI is and have reviewed of the most prominent technologies, you may be wondering how the learning process actually takes place. Machine learning is accomplished when an analytical model is integrated into an information system and continually (and automatically) "retrained" with the latest consumer data and behaviors. When that happens, the machine "learns" the new behaviors of consumers automatically as those behaviors are captured in an information system. See the image below:

We will dive into greater detail on this process in later chapters. For now, it visually outlines the steps that data scientists go through to genreate machine learnin pipeliness. It begins by identifying a relevant business problem or opportunity to address with data. Then, we proceed by extracting the data we need to generate some type of machine learning feedback or prediction. Next, we clean and prepare the data to get it into and ideal format for fast and efficient statistical processing. Then, we segregate the into those used for "training" a predictive model versus those used to test the model. Next, we apply a statistical formula to generate a set of weights for each type of data indicating how important each is in predicting some outcome. You might think of this in terms of the classic function from High School algebra: f(x) = m₁x₁ + m₂x₂ m₃x₃ + ... + m_nx_n + b. Then, we evaluate the quality of this predictive model and iteratively refine it until we get the best predictive accuracy possible. Once the best model is determined, we deploy the prediction in some way (e.g. through website or mobile app). Finally, we observe the results over time to determine how well our prediction is accurate. Eventually, our models "drift"--meaning performance may get worse--and we start the process again by importing new data. The visualization below indicates the consumer's perspective of the ML process--i.e. the person who receives the prediction and uses it to make a decision:

To understand the consumer perspective, consider the Amazon case again where a consumer is looking to buy some moving boxes: 1) A consumer—"Homer"—visits Amazon.com in search of boxes, 2) Amazon recommends alternative boxes that are often purchased by other customers viewing the box he is currently viewing, 3) Homer makes the decision to purchase a particular box which is then recorded in Amazon's operational database, 3) this data is analyzed/processed using a predictive statistical algorithm (referred to as "modeling"), 4) the results of this statistical model are then used to generate new recommendations based on Homer's last decision, 5) These recommendations are given back to Homer and other customers when they come back to Amazon.