Machine learning is multi-faceted. Learning mechanisms are proving incredibly useful to many disciplines. When machine learning gets applied to a discipline, like Biology or Psychology let’s say, the methodology usually takes on a form in line with that discipline.
In Pedro Domingos’ book, The Master Algorithm: How The Quest for the Ultimate Learning Machine Will Remake Our World, he categorizes the types of machine learning algorithms into five classes, which he calls the tribes of machine learning. Each group supports a set of principles, and, from them, stem different machine learning models.
From many to one, his approach ends by saying that all tribes will center upon a single, master algorithm to know everything. This final machine learning algorithm, The Master Algorithm, would be the artificial general intelligence many predict is to come. In order to get there, Domingos wants to take a little from each of these fives tribes and mix them together.
The 5 Tribes of Machine Learning:
- Symbolists
- Connectionists
- Bayesians
- Evolutionaries
- Analogizers
Symbolists
Symbolists focus on logic. They develop formal systems to create an AI. From the 1950s to the 1980s, symbolic AI was the dominant paradigm. Most computers were based on this kind of thinking, and, for decades, it was the most accessible to code. Symbolists will use technologies that follow a rigid path to make a decision based on logic. Their hard-coded rules would look something like, “Always go left when you encounter a stop sign”.
Systems that produce this kind of decision-making include:
- Decision trees
- Random decision forests
- Production rule systems
- Inductive logic programming
Connectionists (Neuroscience)
Connectionists are the group that create models based on the brain. They love to point out that neural networks, a key part of their internal machine learning architecture, were modeled after the way neurons work in the brain. They use models such as:
- Artificial neural nets
- Reinforcement learning
- Deep learning
When they say connections, they do not refer to connections like analogies, like a kitten is to a cat as a puppy is to a dog. For Connectionists, they use connections to term the signals that pass from one neuron to another. There are strengths of signals and numbers of signals.
A large problem people have with the Connectionist framework is that the way that decisions are made is hidden from view—the proverbial black box. Every step of a decision made in a decision tree is known. In Connectionism, an input may have an output, but the path that input took to get to that output goes unseen. A person cannot ask, “How did the model come to that conclusion?”
