Demystifying Ten Big Ideas and Rules Every Fire Scientist Engineer Should Know About Blackbox, Whitebox Causal Artificial Intelligence
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Artificial intelligence (AI) is paving the way towards the fourth industrial revolution with the fire domain (Fire 4.0). As a matter of fact, the next few years will be elemental to how this technology will shape our academia, practice, and entrepreneurship. Despite the growing interest between fire research groups, AI remains absent of our curriculum, and we continue to lack a methodical framework to adopt, apply and create AI solutions suitable for our problems. The above is also true for parallel engineering domains (i.e., civil/mechanical engineering), and in order to negate the notion of history repeats itself (e.g., look at the continued debate with regard to modernizing standardized fire testing, etc.), it is the motivation behind this letter to the Editor to demystify some of the big ideas behind AI to jump-start prolific and strategic discussions on the front of AI Fire. In addition, this letter intends to explain some of the most fundamental concepts and clear common misconceptions specific to the adoption of AI in fire engineering. This short letter is a companion to the Smart Systems in Fire Engineering special issue sponsored by Fire Technology. An in-depth review of AI algorithms [1] and success stories to the proper implementations of such algorithms can be found in the aforenoted special issue and collection of papers. This letter comprises two sections. The first section outlines big ideas pertaining to AI, and answers some of the burning questions with regard to the merit of adopting AI in our domain. The second section presents a set of rules or technical recommendations an AI user may deem helpful to practice whenever AI is used as an investigation methodology. The presented set of rules are complementary to the big ideas.
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