Introduction
In recent years, the terms artificial intelligence and machine learning have started to appear frequently in technology news and websites. The two are often used synonymously, but many experts argue that they have subtle but real differences.
And, of course, experts sometimes disagree with each other about what those differences are.
However, in general, two things seem clear: first, the term artificial intelligence (AI) is older than the term machine learning (ML), and second, most people consider machine learning to be a subset of the artificial intelligence.
Artificial intelligence vs. Machine learning
Although AI is defined in many ways, the most widely accepted definition is “the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition.” In essence, is the idea that machines can possess intelligence.
The heart of a system based on Artificial Intelligence is its model. A model is nothing more than a program that improves your knowledge through a learning process by making observations about your environment. This type of learning-based model is grouped under Supervised Learning. There are other models that fall into the category of unsupervised learning models.
The phrase “machine learning” also dates back to the middle of the last century. In 1959, Arthur Samuel defined ML as “the ability to learn without being explicitly programmed.” And he went on to create a computer verification application that was one of the first programs that was able to learn from its own mistakes and improve its performance over time.
Like artificial intelligence research, machine learning fell out of fashion for a long time, but became popular again when the concept of data mining began to take off around the 1990s. Data mining uses algorithms to look for patterns in a given set of information. ML does the same, but then it goes a step further: it changes the behavior of your program based on what it learns.
One ML application that has become very popular recently is image recognition. These applications must first be trained; in other words, humans have to look at a bunch of images and tell the system what is in the image. After thousands and thousands of repetitions, the software learns what pixel patterns are generally associated with horses, dogs, cats, flowers, trees, houses, etc., and can guess the content of the images quite well.
Many web-based companies also use ML to boost their recommendation engines. For example, when Facebook decides what to show in your newsfeed, when Amazon highlights products you might want to buy, and when Netflix suggests movies you might want to watch, all of those recommendations are based on predictions based on patterns in your existing data.
Frontiers of Artificial Intelligence and Machine Learning: Deep Learning, Neural Networks, and Cognitive Computing
Of course, “ML” and “AI” are not the only terms associated with this field of computing. IBM frequently uses the term “cognitive computing,” which is more or less synonymous with AI.
However, some of the other terms have very unique meanings. For example, an artificial neural network or a neural network is a system that has been designed to process information in a similar way to the way biological brains work. Things can get confusing because neural networks tend to be particularly good at machine learning, so those two terms are sometimes combined.
Furthermore, neural networks provide the foundation for deep learning, which is a particular type of machine learning. Deep learning uses a certain set of machine learning algorithms that run on multiple layers. It is possible, in part, thanks to systems that use GPUs to process a large amount of data at once.
If you are confused by all these different terms, you are not alone. Computer scientists continue to debate its exact definitions and probably will for some time to come. And as companies continue to pour money into artificial intelligence and machine learning research, a few more terms are likely to emerge to add even more complexity to the problems.
