What is Artificial Intelligence and how is it used? These are just some of the questions you might be asking. You might be surprised to learn that there are more applications of AI than you think. From driving to health care, AI has already transformed our lives in more ways than one. If you haven’t heard about AI yet, it may be time to start reading about its benefits. This article will give you a basic overview of AI and its applications.
AI is already making inroads into previously inaccessible areas. You’ve probably noticed short news articles on Yahoo! or Associated Press that were written by AI. The current state of AI technology has made it possible for a robot to write these. But it’s still not ready for in-depth articles or creative stories, yet. It can handle simple articles, such as product reviews. AI is also making its way into everyday life, such as in self-driving cars made by Tesla and smart home devices like Google’s NEST. AI is also becoming increasingly used in games, including Alien: Isolation.
The origins of AI go as far back as the ancient Greek myths. The Greek philosopher Aristotle is credited with the development of deductive reasoning and syllogism, both of which were foundational to understanding the human mind. Today, artificial intelligence has barely been more than a century old. The first mathematical model of neural networks was published by Warren McCullough and Walter Pitts in 1923, but the field is only beginning to make a difference in the lives of humans and animals.
In 1950s, a team of scientists led by computer scientist John McCarthy defined artificial intelligence as “any task that a computer could perform.” That’s a very broad definition of AI. Modern definitions are far more specific, such as the one proposed by Google AI researcher Francois Chollet, creator of the machine learning software library Keras. Chollet explains that artificial intelligence is tied to intelligence, generalisation, and application.
AI systems use a combination of machine learning and other forms of data analytics. Machine learning algorithms glean patterns from training data. For example, an algorithm that is trained to identify spam messages must be taught with examples of both spam and not-spam emails. After the algorithm has learned to recognize patterns, it can be trained for other tasks. AI systems can even learn chess, learn how to recommend products, and perform complex tasks.