Artificial intelligence (AI) is the theory and development of computer systems capable of performing tasks that traditionally require human intelligence. AI is a very broad field, in which “machine learning” is a subfield. Machine learning can be described as a method of designing a sequence of actions to solve a problem, called algorithms, that optimize automatically through experience and with limited or no human arbitration. These methods can be used to find patterns in large data sets (big data analytics) from increasingly diverse and innovative sources. The figure below gives an overview. Say no to plagiarism. Get a tailor-made essay on "Why violent video games should not be banned"? Get the original essay Since an initial wave of optimism in the 1950s, smaller subsets of artificial intelligence - the first learning machine learning, then deep learning, a subset of artificial intelligence learning – created increasingly significant disruptions. The simplest way to think of their relationship is to visualize them as concentric circles with AI, the idea that came first – the biggest, then machine learning – which blossomed later, and finally the Deep learning – which is at the origin of the current explosion of AI – fits into both. Since a start of optimism in the 1950s, smaller subsets of artificial intelligence – first machine learning, then deep learning, a subset of machine learning – have created larger disruptions. and more important. Machine learning, at its most basic, is the practice of using algorithms to analyze data, learn from it, and then determine or predict something in the world. So, rather than manually coding software routines with a specific set of instructions to accomplish a particular task, the machine is "trained" using large amounts of data and algorithms that give it the ability to learn how to complete the task. Machine learning came directly from the minds of early AI adopters, and algorithmic approaches over the years have included, among others, decision tree learning, inductive logic programming, clustering, reinforcement learning and Bayesian networks. One of the best areas of application of machine learning for many years has been in computer vision, although that still required a lot of hand coding to get the job done. could identify where an object started and stopped; shape detection to determine if it had eight sides; a classifier to recognize the letters “STOP”. From all of these hand-coded classifiers, they would develop algorithms to make sense of the image and “learn” to determine whether it was a stop sign. Good, but not incredibly great. Especially in foggy weather, when the sign is not perfectly visible or a tree obscures part of it. There's a reason computer vision and image detection couldn't compete with humans until very recently: they were too fragile and too error-prone. Time and good learning algorithms made all the difference. Another algorithmic approach from the early adopters of machine learning, artificial neural networks, has appeared and mostly disappeared over the decades. Neural networks.