Seven Key Dimensions to Help You Understand Artificial Intelligence Environments
Understanding an AI environment is an incredibly complex task but there are several key dimensions that provide clarity on that reasoning.
Every artificial intelligence(AI) problem is a new universe of complexities and unique challenges. Very often, the most challenging aspects 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 exoteric optimization techniques without fully understanding the problem at hand. When we think about an AI 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 artificial intelligence(AI) solutions, we spend a lot of time focusing on aspects such as the structure of learning algorithms [ex: supervised, unsupervised, semi-supervised], the architecture of a neural network [ex: convolutional, recurrent…] or the characteristics of the data [ex: labeled, unlabeled…]. However, little attention is often provided to the nature of the environment on which the AI solution operates. As it turns out, the characteristics of the environment are the number one element that can make or break an AI model.
There are several aspects that distinguish AI environments. The shape and frequency of the data, the nature of the problem , the volume of knowledge available at any given time are some of the elements that differentiate one type of AI environment from another. Deep diving into those characteristics will guide the strategies of AI experts in areas such as algorithm selections, neural network architectures, optimization techniques and many other relevant aspects of the lifecycle of AI applications. Understanding an AI environment is an incredibly complex task but there are several key dimensions that provide clarity on that reasoning.
One of the most effective methodologies for understanding an AI environment is to classify it across a series of well-known dimensions that are often only segmented in two or three classifications. Among the different characteristics that can be used to classify an AI environment, there are seven key exclusive dynamics that provide a rapid understanding of the challenges and capabilities needed by AI agents.
One of the most obvious dimensions to classify and AI environment is based on the number of agents involved. The vast majority of AI models today focus on environments involving a single agent but there is an increasing expansion in multi-agent settings. The introduction of multiple agents in an AI problem raises challenges such as collaborative or competitive dynamics which are not present in single-agent environments.
Complete AI environments are those on which, at any given time, the agents have enough information to complete a branch of the problem. Chess is a classic example of a complete AI environment.
