Knowledge adds certainty to our doing things right and doing the right things. We are knowledge workers after all, so we should strive to become really good at learning and how it can make us more knowledgeable.
Learning is the information economy’s production function. Unlike the capital or land intensive production functions of other economies, this one requires primary inputs of data and information along with a healthy dose of human motivation, curiosity and time. People learn by doing, reading, writing, listening and observing. And as Drucker suggested, we should know which works best for us.
When we learn, we apply our existing knowledge to extract relevance and purpose from data. Data are just facts about things and our knowledge helps assemble these facts in a way that helps us know more.
Machine learning works in much the same way. In supervised machine learning, a data scientist uses her domain-specific knowledge to extract a set of features from tons of data representing an observed phenomenon. Each row of features is associated to a desired output and an algorithm generates a model to optimally map the two. The model is then capable of predicting the output value for a new row of features. The more rows come in, the more this new data can be used to retrain the model to make it more accurate. More accuracy means knowledge is gained and the model has learned.