Although AI is defined in different ways, the most widely used 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,” essentially the idea that machines can be intelligent.
The heart of an artificial intelligence system is its model. A model is nothing more than a program that improves its knowledge in the learning process by observing the environment. This type of learning-based model refers to supervised learning. Other models fall under the category of unsupervised learning models.
The expression "machine learning" also dates back to the middle of the last century. In 1959, Arthur Samuel defined ML as "the ability to learn without explicit programming." And he went on to create a computer checker application, which was one of the first programs that could learn from its mistakes and improve its performance over time.
Like research in artificial intelligence, machine learning went out of fashion for a long time but became popular again when the concept of data mining began to evolve around the 1990s. Data mining uses algorithms to find patterns in a given set of information. ML does the same, but then goes even further - it changes its program's behavior depending on what it learns.
One of the machine learning applications that has become very popular lately is image recognition. These applications first need to be trained - in other words. People have to look at a set of images and tell the system what is depicted on them. After thousands and thousands of repetitions, the program will know which pixel patterns are commonly associated with horses, dogs, cats, flowers, trees, houses, etc. It can make a pretty good guess about the content of the images.
Many web companies also use machine learning to power their recommendation systems. For example, when Facebook decides what to show in your news feed when Amazon highlights products you might want to purchase. When Netflix offers movies, you might want to watch, all of these recommendations are based on predictions that flow from patterns in their existing data.
The 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 area of computer science. IBM often uses the term "cognitive computing," which is more or less synonymous with AI.
However, some other terms have unique meanings. For example, an artificial neural network or neural network is a system that has been designed to process information in ways similar to the way a biological brain works. This can be not very clear because neural networks tend to be especially good at machine learning, so the two terms are sometimes combined.
Also, neural networks provide the foundation for deep learning, a special kind of machine learning. Deep learning uses a specific set of machine learning algorithms that operate at multiple levels. This is made possible in part by systems that use GPUs to process large amounts of data at once.
If all these different terms confuse you, you are not alone. Computer scientists continue to argue about their exact definitions and probably will be for a while. And as companies continue to invest in artificial intelligence and machine learning research, it is likely that a few more terms will emerge that will further complicate the problems.
21 ARALıK 2020 It was written on.