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Seven Dimensions that Help Understand Machine Learning Environments
The number of agents involved, nature of the observations, stochasticity, and dynamism, are some of the characteristics that will help us understand a machine learning problem.
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Tackling a machine learning problem might feel overwhelming at first. What model to choose? which architecture might work best? In a process that is mostly driven by trial and error experimentation, those decisions result incredibly important. One aspect that really helps to navigate that universe of decisions is to clearly understand the nature of the problem. In machine learning scenarios, an important part of understanding the problem is based on understanding its environment.
Every machine learning problem is a new universe of complexities and unique challenges. Very often, the most challenging aspect of solving an AI problem is not about finding a solution but understanding the problem itself. As paradoxically as that sounds, even the most experienced AI experts have been guilty of rushing into proposing deep learning algorithms and exotic optimization techniques without fully understanding the problem at hand. When we think about a machine learning problem, we tend to link our reasoning to two main aspects: datasets and models. However, that reasoning is ignoring what can be considered the most challenging aspect of an AI problem: the environment.
When designing machine learning solutions, we spend a lot of time focusing on aspects such as the structure of learning algorithms [ex: supervised, unsupervised, semi-supervised], the…
