What is AI?

Introduction

AI is an acronym for artificial intelligence. Although some people argue that assistive intelligence or augmented intelligence is more accurate, the majority of the time, AI stands for artificial intelligence.

But what does that mean? For starters, there is no universally accepted definition of AI. You can ask different experts and they will give different answers.

There are many definitions out there that define AI as computer programmes doing things that would generally be assumed to require human intelligence. These definitions are problematic as they contribute to the anthropomorphising (attributing human characteristics to inanimate or non-human entities) of these technologies which ultimately lead to misunderstandings of what they can and cannot do, and also because what is considered to require human intelligence is constantly shifting.

Essentially, AI is a field of loosely related computer-based and statistics enabled technologies that, based on the input of data (information),instructions and parameters, can produce outputs such as predictions, recommendations and content.

Many people use the OECD’s definition of AI:

This definition is quite wordy and jargony and can be seen as being inaccessible. But many prefer it as it is purely descriptive and does not make equivalences to human traits or otherwise.

To add to the complications, there is no single “AI”. AI is more of an umbrella term for a wide range of computer and data driven technologies, including software that filters spam emails away from your inbox, computer programmes that allocate hospital appointments, chatbots giving customer service advice, smart devices in the home and, self-driving cars.

While recent years have brought heightened public interest, AI is most certainly not new. The idea of intelligent machines has been around for well over a century and can date back to ancient myths and tales of automata.

Modern AI, however, is a 20^th century phenomenon, and it goes back to the 1950s when the term artificial intelligence (AI) was coined and launched as a formal field of research at the now famous 1956 Dartmouth College conference. It was initially concocted as a marketing term which has evolved over the years to mean very different things to different people. The evolution of the field can be tracked over the decades through a series of winters (troughs) and summers (peaks) corresponding to rises and falls in computing power and funding.     

The definition of the term “intelligence” is up for debate. Current AI models cannot understand or think for themselves. Rather, they are statistical models that may give the impression of intelligence based on the outcomes they produce from patterns identified in their training data. While there is speculation about the possibility of future AI models developing true intelligence, we do not currently have actual artificial intelligence.

Categories of AI

In public and media discussions of AI there are often very different understandings of what this means, or what AI is capable of. The public image of AI has often been shaped by the ways it has been presented in movies and science fiction novels as something that can think for itself and control its own actions, but this is very different from the reality of AI today. In recognising the capacities and limitations of AI compared to visions of how this might develop in the future, AI can be classified under three broad categories:

All AI in existence now is Narrow AI.

Core AI technologies