Many people have developed different taxonomies for intelligence over the years, focusing on its function and its objective. Most of us have heard of the logical/mathematical intelligence, for example, which is the one responsible for handling all the operators that our math teacher seems to care about, or the operators related to solving a Sudoku puzzle. The linguistic intelligence is of a different type, as it focuses on words and the semantics of language, an intelligence that all literary writers have traditionally been adept at. Then there is also EQ and SQ, intelligence types that focus on the emotional and the spiritual aspects of our lives respectively. However, so far, there hasn’t been a taxonomy that deals with the form of intelligence, its rudimentary essence, at least not to the best of my knowledge.
According to this paradigm of intelligence, there are three main types: static, dynamic, and versatile intelligence. This may be apparent, considering the three-fold structure of the human brain. What may not be as apparent though is how these are directly related to data science, since if done properly, data science is an intelligent application of intelligence.
Static intelligence is the intelligence of the hedgehog. It involves mental structures that are established, reliable, and fool-proof. Most of science is actually done using this kind of intelligence since it aims to discover and apply knowledge that is accountable and void of surprises. This intelligence is the one that you develop reading books and watching educational videos (wink-wink!), while it is highly valued in most educational institutions. Intelligence of this type doesn’t change much over one’s life, since it is very passive, in a way.
The exact opposite of static intelligence is dynamic intelligence (duh!), the inteligence of the fox. It entails mental structures that are in constant flux and are experimental, risky, and unpredictable. Most of scientific innovation and all processes that employ creativity are based on this kind of intelligence. Dynamic intelligence aims to bring about new ideas and concepts for further evaluation and seek new processes for handling existing information streams. Without this kind of intelligence any kind of feature engineering would be impossible, while coming up with a robust and unconventional model would be a stretch, even if you have many years of experience in data science. Intlligence of this type is quite volatile, evolving, and very active, in a way.
Something between and beyond these intelligence types is versatile intelligence, aka the dolphin-like approach to things. This is basically the intelligence of all successful inventors, both the known and the unknown ones. Also, I believe that the first pioneers of data science employed this kind of intelligence when they lay the foundations of the field. Versatile intelligence is concrete, yet playful, accountable, yet experimental, formal, yet also chaotic. Most science leaders, be it researchers or practitioners, rely on this kind of intelligence in their everyday lives. Also, entrepreneurs seem to employ this sort of thinking, particularly if they are committed to their work. All major innovations in data science (and science in general) are based in one way or another on versatile intelligence.
Although there is a certain charm in correlations and finding links among different classes of people with different classes of intelligences, I will refrain from indulging into this intriguing yet futile endeavor. The reason is that we all have all three types of intelligence, even if we tend to express one over the others, because of our life requirements and goals. Intelligence is just like water, fluid and able to take whatever form we ask it to, so applying a static approach to this taxonomy, although interesting, is not realistic. So, when we need to do some ETL work with our data, we need to apply static intelligence mainly. In other parts of the pipeline (e.g. data modeling), dynamic intelligence may lend itself better. And when it comes to conveying our findings via insight deliverance or the creation of a data product, employing the versatile intelligence would make more sense.
Note that all this is my own approach to the topic and may not have any scientific literature to back it up whatsoever. However, in practice it has worked well so far. What about you? What does your intelligence tell you about the nature of intelligence?
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.