introduction
Over the past few years, the terms artificial intelligence and machine learning have started popping up more frequently in technology news and websites. The two are often used as synonyms, but many experts argue that they have subtle but real differences.
Of course, experts sometimes disagree among themselves about what these differences are.
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 AI.
Artificial intelligence versus 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,” at its core is the idea that machines can possess intelligence.
The core of the AI-based system is the model. A model is nothing but a program that improves its knowledge through the learning process by making observations about its environment. This type of learning-based model is grouped under supervised learning. There are other models that fall under the category of unsupervised learning models.
The phrase “machine learning” also dates back to the middle of the last century. in 1959, Arthur Samuel ML is defined as “the ability to learn without being explicitly programmed.” He went on to create a computer checker application that was one of the first programs that could learn from its mistakes and improve its performance over time.
Like AI research, ML has not been popular for a long time, but it became popular again when the concept of data mining started to emerge around the 1990s. Data mining uses algorithms to look for patterns in a particular set of information. ML does the same thing, but then it takes it a step further – it changes the behavior of its program based on what it learns.
One of the ML applications that has become very popular recently is image recognition. These applications must be trained first – in other words, humans must look at a set of images and tell the system what is in the image. After thousands and thousands of iterations, the program recognizes the pixel patterns that are generally associated with horses, dogs, cats, flowers, trees, houses, etc., and can make a good guess about the content of the pictures.
Many web based companies also use ML to power 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 see, all of these recommendations are based on predictions based on patterns in its current 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 computer science. IBM often uses the term “cognitive computing,” a term somewhat synonymous with artificial intelligence.
However, some other terms have very unique meanings. For example, an artificial neural network or neural network is a system that is designed to process information in ways similar to the ways that biological brains work. Things can get confusing because neural networks tend to be particularly good at machine learning, so these two terms are sometimes confused.
In addition, neural networks provide the basis for deep learning, which is a special type of machine learning. Deep learning uses a specific set of machine learning algorithms that work in multiple layers. It is made possible, in part, by systems that use graphics processing units (GPUs) to process large amounts of data simultaneously.
If you are confused by all these different terms, you are not alone. Computer scientists continue to debate their exact definitions and will probably do so for some time to come. And as companies continue to pour money into AI and machine learning research, it is likely that some other terms will emerge to add more complexity to the problems.