Here are simple explanations of artificial intelligence, machine, and deep learning and how they are different from each other.
Most of us are already conversant with the term artificial intelligence since it has been around for some time now. AI has been part of our lives for a long time, and all of us can see the various AI application around us. However, you may have recently heard about the other terms that are related to artificial intelligence such as machine learning and deep learning. Some people tend to use these terms interchangeably with artificial intelligence. However, fundamental AI differences exist between these three terms. Let us look at all three words and how they differ from each other.
The term artificial intelligence was coined by John McCarthy in mid-1956. In simple terms, AI refers to devices that can perform various tasks that usually require human intelligence. The whole concept of AI involves things such as understanding human language, planning, recognizing different objects and various sounds, continued learning, and problem-solving.
AI can be represented as a pile of if-then statements or as a sophisticated model that maps raw sensory data to various symbolic categories. The collection of the if-then statements are sometimes referred to as expert systems, symbolic artificial intelligence, knowledge graphs or rules engines. AI is a general category that encompasses both machine learning and deep learning.
If we were to come up with a diagrammatic representation of the three aspects, then AI will be at the center of the diagram. Agents that fall under artificial intelligence and lack machine learning capabilities only utilize decision trees to make decisions.
In very simple terms, machine learning is simply a way of implementing artificial intelligence. As mentioned earlier, AI is a general term that encompasses both machine and deep learning. This term was coined by Arthur Samuel in 1959 to refer to the ability of a device to learn and understand without being explicitly programmed.
Typically, you can implement AI without machine learning, but you will need to build millions of lines of code with sophisticated rules and large decision trees. Therefore, instead of writing unending lines of code with complicated instructions to perform a specific task, you can use machine learning to train your algorithm to learn how to perform particular functions on its own.
However, this training usually involves feeding your algorithm with numerous amounts of data and allowing it to adjust accordingly and improve over time. Machine learning can either be supervised or unsupervised. There is a wide range of machine learning application that surrounds us such as personalized health monitoring.
Deep learning is one of the most common approaches to machine learning. The other approaches include inductive logic programming, decision tree learning, reinforcement learning, Bayesian Networks, and clustering. Deep learning is mostly inspired by the structure and functioning of the human brain. Deep learning experts have developed the artificial neural networks (ANNs) which are basically computer algorithms that resemble the biological structure and functioning of the human brain.
The artificial neural networks consist of neurons which are made up of millions of simple processing nodes that are highly interconnected. These networks are carefully organized into layers of nodes which provide multiple layers to process data. Each segment of the neural network picks out one specific feature of the brain to learn. It is this unique layering and deep interconnections that give deep learning its name.
Regardless of the fundamental differences that exist between these three terms, all of them are making profound impacts on the society today. In fact, scientists believe that one day, the various artificial intelligence, machine, and deep learning application will lead humanity into a robotic world.