For individuals unfamiliar with the intricacies of modern machine learning and AI, the whole domain may appear as a confusing mix. Yet, delving deeper reveals that leading scientists are adopting distinctly different methodologies to advance artificial intelligence, essentially dividing the field into five main strategies.

Pedro Domingos, in his seminal book "The Master Algorithm," highlights these distinct methodologies or "tribes" within AI research, a concept echoed across various scientific discussions.

The first methodology, known as connectionism, delves into the neural connections mimicking the human brain's structure and functionality, leveraging backpropagation to derive outcomes, essentially aiming to replicate the brain's workings.

Symbolism, the second approach, relies on logic and accumulated knowledge to construct intelligent models, reminiscent of the early days of AI research before the advent of neural networks. This method involves compiling extensive knowledge bases and manipulating them to generate AI, blending traditional and modern techniques.

Evolutionism, the third strategy, draws from evolutionary theory, genetics, biophysics, and bioinformatics, applying these disciplines to genetics and distinguishing itself as a unique venture among the AI methodologies.

The fourth, the Bayesian school, is one of the more established methods, initially used for tasks like spam detection. It employs heuristic models based on probabilities to refine models for filtering out unwanted outcomes or achieving specific goals, notably utilized in network security to identify potential threats.

Lastly, the analogizing approach makes AI more accessible to the general public, with recommendation systems from entities like Facebook and Google serving as prime examples. This method uses algorithms to match concepts or people based on similarity, aiding in personalized recommendations.

These diverse approaches collectively contribute to the advancement of AI research, with scientists striving to progress these methodologies collaboratively. However, amidst technological advancements, there's a consensus on the importance of ethical considerations and responsible technology use to mitigate potential societal issues, a principle that applies across all five AI methodologies.