This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEO's to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results. My last two blogs focused on the importance of AI professionals having some foundation in science discipline as a cornerstone for designing and developing AI models and production processes, and explored value of computing science, the richness of complexity sciences and the value of physics to appreciate the importance of integrating diverse disciplines into complex AI programs - key for successful returns on investments (ROI). This blog discusses key AI and machine learning (ML) terms that every board director and CEO must know to stay relevant and advance their duty of care. If you want a good starter on the responsibility and duty of care, I recommend you read my earlier blog here. In the Brain Trust Series, I have identified over 50 skills required to help evolve talent in organizations committed to advancing AI literacy.

In my last blog on board director and CEO leadership needs, I identified a series of AI leadership questions to advance AI successfully and introduced basic AI concepts such as defining basic terms like: AI, algorithm, AI model. I also described different AI model methods like: unsupervised learning versus supervised learning to provide some foundational concepts that every board director or CEO should understand. If you want a good starter on the responsibility and duty of care of C suite leadership on AI, I recommend you read an earlier blog here. Over the past six months in the AI Leadership Brain Trust Series, I have identified over 50 skills required to help evolve talent in organizations committed to advancing AI literacy. The last few blogs have been discussing the technical skills relevancy.

This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEO's to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results. My blog yesterday discussed three key basic concepts to answer: what is AI?, what is an algorithm?, and what is an AI Model? I will continue in the next two blogs to define other key AI concepts and definitions that I believe every CEO or board director must master at the basic AI proficiency levels. After all, how can you lead if you don't know your basics in one of the most significant disruptors of our lifetime. If you want a good starter on the responsibility and duty of care of C suite leadership on AI, I recommend you read my earlier blog here.

What is the relevance of complexity science to AI as a discipline? You do not see enough mention of complexity science in relationship to AI competency development. This really surprises me as complexity science is all about the traversing of disciplinary boundaries and occurs both within and between multiple systems. Complexity sciences have emerged through interdependent and overlapping influences from diverse fields, including concepts from: physics, economics, biology, sociology and computer science. Complexity sciences strive to understand relevant "system" phenomenon that is characterized by changes, and unpredictability. A "system" is a set of connected or interdependent things or agents (such as a person, a molecule, a species, or an organization).