What is machine learning? In the past year, whether it was during a conference, a seminar or an interview, a lot of people have asked me to define machine learning. There is a lot of curiosity around what it is, as human nature requires us to define something before we begin to understand what its potential impact on our lives may be, what this new thing may mean for us in the future.
Similar to other disciplines that become suddenly popular, machine learning is not new. A lot of people in the scientific community have been working with algorithms to automate repetitive activities over time for several years now. An algorithm where the parameters are fixed is called static algorithm and its output is predictable and function only of the input variables. On the other hand, when the parameters of the algorithm are dynamic and function of external factors (most frequently, the previous outputs of the same algorithm), then it is called dynamic. Its output is no longer a function only of the input variables and that is the founding pillar of machine learning: a set of instructions that can learn from the data generated during the previous iterations to make a better output the following time.
The Big Data terminology, as well as the term “Digital”, covers a lot of different situations of various natures. This short paper aims at clarifying the existing processes and making some proposals for applying a Big Data approach to the Oil & Gas upstream industry.