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Introduction
During the past few years, the terms artificial intelligence and machine learning have started to appear 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 differ 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 secondly, most people consider machine learning to be a subset of artificial intelligence.
Artificial intelligence vs. Machine learning
Although AI is defined in several ways, the most widely accepted definition is "the field of computer science devoted to solving cognitive problems usually associated with human intelligence, such as learning, problem solving, and pattern recognition", in essence, it is the idea that machines can possess intelligence.
The heart of the AI-based system is a paradigm. The model is only a program that improves its knowledge through an educational process by taking notes 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 term "machine learning" 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 checkers application that was one of the first programs that could learn from its own mistakes and improve its performance over time.
Like the research of AI, ML has fallen from vogue for a long time, but it became popular again when the concept of data mining began in the 1990s. Data mining uses algorithms to search for patterns in a given set of information. ML does the same thing, but then takes a step forward – it changes the behavior of its program based on what it learns.
One of the ML apps that has become popular recently is image recognition. These applications must be trained first – in other words, humans should look at a set of images and tell the system what is in the image. After thousands and thousands of repetitions, the program recognizes pixel patterns generally associated with horses, dogs, cats, flowers, trees, houses, etc., and can well guess the content of the images.
Many web-based companies also use ML to power their recommendation engines. For example, when Facebook decides what to display in the newsfeed, when Amazon stands out which products you might want to buy, and when Netflix suggests movies that you might like to watch, all of these recommendations are based on forecasts based on them that arise from patterns in their current data .
The limits 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 frequently uses the term "cognitive computing", which is 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 designed to process information in ways similar to the way biological minds work. Things can get confusing because neural networks tend to be particularly good at machine learning, so these two terms are sometimes mixed.
Additionally, neural networks provide the basis for deep learning, a specific type of machine learning. Deep Learning uses a specific set of machine learning algorithms that operate in multiple layers. It is made possible, in part, by systems that use GPUs to simultaneously process a large set of data.
If you are confused by all these different terms, you are not alone. Computer scientists continue to discuss their exact definitions and may do so for some time. As companies continue to pour money into AI research and machine learning, some terminology is likely to arise to add more sophistication to issues.
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