I have been in the space of artificial intelligence for a while and am aware that multiple classifications, distinctions, landscapes, and infographics exist to represent and track the different ways to think about AI. However, I am not a big fan of those categorization exercises, mainly because I tend to think that the effort of classifying dynamic data points into predetermined fixed boxes is often not worth the benefits of having such a “clear” framework (this is a generalization of course as sometimes they are extremely useful).

I also believe this landscape is useful for people new to the space to grasp at-a-glance the complexity and depth of this topic, as well as for those more experienced to have a reference point and to create new conversations around specific technologies.

What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI. I call it the AI Knowledge Map (AIKM).

The AI Knowledge Map, below, was developed by Francesco Corea with strategic innovation consultancy Axilo for activities on their Chôra platform.


The AI Knowledge Map. I developed the AIKM with strategic innovation consultancy Axilo, for activities on their Chôra platform

On the axes, you will find two macro-groups, i.e., the AI Paradigms and the AI Problem Domains. The AI Paradigms (X-axis) are the approaches used by AI researchers to solve specific AI-related problems (it includes up to date approaches). On the other side, the AI Problem Domains (Y-axis) are historically the type of problems AI can solve. In some sense, it also indicates the potential capabilities of an AI technology.

Read more: Forbes