At the core of Artificial Intelligence, two major pathways of knowledge extraction and representation have been the cornerstone for many decades: Deductive Learning, based on sets of "rules" from Predicate Calculus and Horn clauses that represent the domain experts' knowledge; and Inductive Learning, based on 'generalization by examples' by more or less 'black box' algorithms.In this lecture, AI is explored under the scope of "inherited" versus "learnt" knowledge, i.e., Genetic versus Adaptive Intelligence. In general, the first is usually associated with Genetic Algorithms (GA), using gene-like embeddings of system parameters and an evolutionary process, in order to drive some iterative optimization scheme. In contrast, Adaptive Intelligence like Reinforcement Learning (RL) or Temporal Difference Learning (TDL) employ action-gain associations in the form of trial-and-error for a small population of agents, in order to ensure adaptation in continuously changing environments. Both approaches are equally important and complementary in real-world AI designs. Keywords: Machine Learning, Data Analytics, AI, Artificial Intelligence, lecture, Reinforcement Learning, Genetic AlgorithmsVideo: https://youtu.be/UArPofuoVU8