What is the definition of AI
AI stands for Artificial Intelligence. This is a part of the broader field of computer science that involves making computers do things which humans can’t do. In particular, it refers to systems in which a computer system performs tasks considered to be those typically requiring human intelligence, such as visual perception, speech recognition, decision-making and translation between languages. In most of these cases, the system is also able to learn from experience, and to make new adaptations depending on the outcome of past experience.Some of the very earliest artificial intelligence systems were based on simple game playing programs: examples include Checkers and Chess. For example, a computer program for playing checkers was invented by Alan Turing in 1950; this was one of the first general-purpose AI systems – it was capable of doing a wide range of tasks that would have been considered “intelligent” by most people.
The term “artificial intelligence” was introduced in 1956 by John McCarthy at the Dartmouth Summer Research Project on Artificial Intelligence, to describe a class of human-like intelligences. Later, the term “intelligence” was used interchangeably with “intelligence test”, but this has now become discouraged as the two are not necessarily identical. The definition also differs between different disciplines. In psychology, one common definition of intelligence was given by Hans J. Eysenck: “the ability to learn and apply knowledge. It is normally considered to be a matter of cognition, involving the ability to reason, plan, solve problems, think abstractly and relate to one’s environment.
Some researchers (e.g., Goleman) claim that emotional intelligence was an important aspect of human intelligence that is distinct from the type of reasoning featured in classical theories of intelligence.
How has AI been used in text
AI is used in a broad range of ways within online text. This can involve the use of speech recognition, where a computer system listens to what you say and then responds in an appropriate way. It can involve the use of functionality like opinion mining, which tries to estimate how people will respond to proposed changes to a website. It can involve content analysis, which uses data from large text collections to predict aspects of human behaviour – for example, it might be used by major media outlets such as Reuters and The Guardian in deciding whether to publish certain information.. A related technique, called natural language processing, uses computer programs to analyse documents in order to find patterns, such as that all the French “m” words begin with an “m” sound and are never followed by another consonant (in English this is called “y”). In common with all text-related techniques, the most important thing is to identify why you are doing something; to ensure that you record evidence of user intent. The evidence of user intent is, in turn, reflected in data about the way in which content and functionality are used by individuals. The most important purposes for AI and other text-related techniques are to predict behaviour based on pattern recognition, to encourage editorial decisions based on evidence of user intent (such as whether a story will be a ‘hit’ or a ‘miss’) and to track the evolving use of content. For example, a journalist might use an AI application to check whether a new story has been published, and if it hasn’t then to prompt them to do so, rather than to ask them to go through a complex and time-consuming workflow.
Examples of how AI has enhanced text
AI is a technique which has been used to create mobile apps and other text services. Examples include natural language processing, where a program looks at all the documents in a collection and identifies patterns in the language (often in order to provide answers to queries). Another example is sentiment analysis, which uses a computer program to identify words which are associated with a particular opinion (e.g. “love”, “hate”, and “dislike”). A further example is speech recognition, which allows a computer program to listen to what you say, and then use this input as the starting point for generating an appropriate response.
The main advantages of AI are its speed and accuracy. A human being might read a large collection of documents, looking for the best matches for particular questions, but it would take a long time to do so. An AI program can often complete such analysis in seconds. Another advantage is that an AI program can work around the clock, whereas a human being may not be readily available.
At the moment, however, AI is still relatively new and its use is limited. But as a technology it will no doubt grow as it becomes more sophisticated.
(AI has enhanced text by allowing computers to process information more quickly and accurately than human beings can do.) [ARTICLE END]
The future of AI and text
AI is a rapidly developing field; new techniques are constantly being developed and tested. Most of these have not been applied to text; it would be reasonable to assume that this will occur in the future, although the nature and purpose of the application is not clear. While AI is used extensively in online services, many users do not understand how it works. This is one reason why it is important to communicate your decisions when using AI techniques in text systems; give people the opportunity to provide feedback, and explain how they can do this (for example, by making sure that users can contact you through the system). This applies in general – while AI generates useful information, don’t be afraid to be explicit and make users understand what is happening.
Goals: The purpose of this article is to provide information about how AI techniques work, explain how they can be applied in text systems, and provide information on how to create effective feedback loops.
The field of artificial intelligence (AI) has been developing rapidly over the past few years; new techniques are regularly being developed, tested, and applied to text processing problems. While AI is used extensively online, most users have little or no understanding of how it works. Because of this, some might assume that applications which use AI are “magic”; they may be less likely to trust system decisions if they don’t understand what is happening.