The 3 Most Promising AI Learning Approaches

By John Paul Mueller, Luca Massaron

Bayesians, symbolists, and connectionists represent the present and future frontier of learning from data because any progress toward a human-like artificial intelligence (AI) derives from them, at least until a new breakthrough with new and more incredible and powerful learning algorithms occurs. The machine learning scenery is certainly much larger than these three algorithms, but the focus here is on these three tribes because of their current role in AI.

  • Naïve Bayes: This algorithm can be more accurate than a doctor in diagnosing certain diseases. In addition, the same algorithm can detect spam and predict sentiment from text. It’s also widely used in the Internet industry to easily treat large amounts of data.
  • Bayesian networks (graph form): This graph offers a representation of the complexity of the world in terms of probability.
  • Decision trees: The decision tree type of algorithm represents the symbolists best. The decision tree has a long history and indicates how an AI can make decisions because it resembles a series of nested decisions, which you can draw as a tree (hence the name).

These algorithm types are further divided into subcategories. For example, decisions trees come categorized as regression trees, classification trees, boosted trees, bootstrap aggregated, and rotation forest. You can even drill down into subtypes of the subcategories. A random forest classifier is a kind of bootstrap aggregating, and there are even more levels from there. After you get past the levels, you begin to see the actual algorithms, which number into the thousands.