![]() However, this intelligence is specific to a certain domain bees can’t build a nest and ants cannot build a hive. The distinction between narrow and general intelligence is already apparent in the natural world: for instance, bees know how to build beehives, and ants know how to build a nest – both of which are examples of intelligence in a narrow sense. However, while Deep Blue exhibits an above-human ability in chess, its intelligence ends there.Ĭonversely, the concept of artificial general intelligence ( AGI) refers to a level of intelligence across multiple fields. For example, a computer developed by IBM in the 1980s called Deep Blue can play chess at a level superior to human beings a feat of huge importance in the timeline of AI development. It is sometimes also referred to as augmented intelligence to highlight its ability to enhance (but not necessarily replace) human intelligence. This means that it has been deliberately programmed to be competent in one specific area. Most AI that we experience today is ‘narrow’. Narrow, general and super artificial intelligence This list is neither exhaustive, nor intended to be technologically in-depth. Below is a simple explanation of key terms designed to assist the everyday reader understand some of the terminology surrounding AI, and the discussion within this document. There is a significant amount of terminology and technical jargon surrounding AI that is often used interchangeably and can cause confusion, especially for those without a technical background. But, as with any new technology, the opportunities of AI come with an array of challenges for society and the law. Increased efficiency and lower costs, huge improvements in healthcare and research, increased safety of vehicles, and general convenience, are just some of the promises of AI. The ways that AI can enrich our lives are immense. ![]() ![]() Now that these systems are an established element in our lives, the fact that AI techniques – including speech recognition, natural language processing and predictive analytics – are at work is often forgotten. 2 For example, being greeted by an automated voice on the other end of the phone, or being suggested a movie based on your preferences, are examples of mainstream AI technology. One of the characteristics of AI is that once the technology works, it stops being referred to as AI and transforms into mainstream computing. Real-life applications of AI technologies are already established in our everyday lives, although many people are not conscious of this. 1 As well as technological advancements, the very way of thinking about intelligent machines has shifted significantly since the 1960s, which has enabled many of the developments we are seeing today. More recently, we are experiencing a period of rapid development in the field of AI as a result of three factors: improved algorithms, increased networked computing power, and increased ability to capture and store an unprecedented amount of data. While the philosophy of Artificial Intelligence has been argued since at least Leibnitz in the early 18 th Century, the concept of AI as we use it has existed since the early 1940s and made famous with the development of the “Turing test” in 1950. ‘AI’ is used as an umbrella term to describe a collection of related techniques and technologies including machine learning, predictive analytics, natural language processing and robotics. These tasks can be considered intelligent, and include visual and audio perception, learning and adapting, reasoning, pattern recognition and decision-making.
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