narrow AI
In plain wordsAI built to do one specific job, or a small set of related jobs, such as playing chess or spotting spam, without being able to turn its skill to unrelated problems.
AI designed or trained for a specific task or a narrow range of tasks, such as playing chess, recognizing faces, filtering spam or recommending videos, whose competence does not transfer to unrelated problems without being rebuilt or retrained. It is defined by contrast with artificial general intelligenceartificial general intelligence, n.: AI that could learn and do almost any mental task a capable adult can, rather than excelling at one narrow job. Experts disagree on the exact bar and on when, or whether, it will arrive.. Narrow systems can be superhuman within their domain, as Deep BlueDeep Blue, prop. n.: IBM's chess computer that beat world champion Garry Kasparov in 1997, the first time a machine defeated a reigning world champion in a match under standard tournament conditions. and AlphaGoAlphaGo, prop. n.: A DeepMind program that in 2016 beat Lee Sedol, one of the world's best players of the board game Go, a decade or more before many experts expected a computer to manage it. were, while being useless outside it. Nearly all deployed AI before the 2020s was narrow. How to classify foundation modelsfoundation model, n.: A very large AI model, built once at great cost from vast amounts of text, images or code gathered mostly from the internet, that many products then reuse as their starting point, from chatbots to coding tools. and LLM-based assistants is disputed: they handle thousands of tasks yet perform unevenly and fail at some simple ones, so commentators variously call them narrow, general-purposegeneral-purpose AI, n.: AI that can do a wide range of different tasks, like writing, coding, translating and analyzing images, rather than the one job it was built for. Today's chatbots are the main example. or early general intelligence.
The fraud model is narrow AI: excellent on card transactions, useless on insurance claims.
Origin
The earliest use of 'narrow AI' has not been established. It became common in the 2000s among futurists and AGI researchers as the counterpart of 'strong AI' and 'artificial general intelligence', for example in Ray Kurzweil's The Singularity Is Near (2005) and Ben Goertzel's writing on AGI. 'Weak AI' is older but different: John Searle (1980) used it for the view that computers merely simulate minds, as opposed to strong AIstrong AI, n.: Originally, the claim that a computer running the right program would literally have a mind and understand, not just imitate understanding. Today it often simply means AI as capable as humans across the board..
Usage note
'Weak AI' is often used as a synonym, but Searle's weak/strong distinction concerns whether a machine has a mind, not how broad its abilities are. Many researchers treat narrow versus general as a spectrum rather than a binary.
Related
- artificial general intelligenceartificial general intelligence, n.: AI that could learn and do almost any mental task a capable adult can, rather than excelling at one narrow job. Experts disagree on the exact bar and on when, or whether, it will arrive.
- general-purpose AIgeneral-purpose AI, n.: AI that can do a wide range of different tasks, like writing, coding, translating and analyzing images, rather than the one job it was built for. Today's chatbots are the main example.
- strong AIstrong AI, n.: Originally, the claim that a computer running the right program would literally have a mind and understand, not just imitate understanding. Today it often simply means AI as capable as humans across the board.
- jagged intelligencejagged intelligence, n.: The uneven pattern of AI abilities: today's systems can solve very hard problems yet fail at some a child could manage, so their skills form a spiky profile rather than a smooth one.
- Deep BlueDeep Blue, prop. n.: IBM's chess computer that beat world champion Garry Kasparov in 1997, the first time a machine defeated a reigning world champion in a match under standard tournament conditions.
- transfer learningtransfer learning, n.: Reusing what a model learned on one task to get a head start on another — like a pianist picking up the organ faster than a beginner. It lets models learn new jobs from little data.
- levels of AGIlevels of AGI, n.: A scheme from Google DeepMind researchers for grading AI by how well it performs and how broad its skills are, from emerging to superhuman, much as self-driving cars are graded by levels.
- artificial general intelligenceartificial general intelligence, n.: AI that could learn and do almost any mental task a capable adult can, rather than excelling at one narrow job. Experts disagree on the exact bar and on when, or whether, it will arrive.
- general-purpose AIgeneral-purpose AI, n.: AI that can do a wide range of different tasks, like writing, coding, translating and analyzing images, rather than the one job it was built for. Today's chatbots are the main example.
Mentioned in
artificial general intelligenceartificial general intelligence, n.: AI that could learn and do almost any mental task a capable adult can, rather than excelling at one narrow job. Experts disagree on the exact bar and on when, or whether, it will arrive., artificial intelligenceartificial intelligence, n.: Computer systems that do things we would normally credit to a thinking mind, such as holding a conversation, reading an X-ray or planning a route, and the field of research that builds them., superintelligencesuperintelligence, n.: A mind, usually imagined as an AI, that would leave even the cleverest people far behind in nearly every kind of thinking, not just in one game or skill. None is known to exist., artificial superintelligenceartificial superintelligence, n.: Superintelligence in machine form: AI that would outthink the best humans at nearly every intellectual task. Often shortened to ASI and pictured as the stage after human-level AI., foundation modelfoundation model, n.: A very large AI model, built once at great cost from vast amounts of text, images or code gathered mostly from the internet, that many products then reuse as their starting point, from chatbots to coding tools., general-purpose AIgeneral-purpose AI, n.: AI that can do a wide range of different tasks, like writing, coding, translating and analyzing images, rather than the one job it was built for. Today's chatbots are the main example., human-level AIhuman-level AI, n.: AI that can match an ordinary adult across the whole range of thinking tasks, such as talking, learning, reasoning and planning. An older, more intuitive name for the goal now usually called general AI., strong AIstrong AI, n.: Originally, the claim that a computer running the right program would literally have a mind and understand, not just imitate understanding. Today it often simply means AI as capable as humans across the board., Ben GoertzelBen Goertzel, prop. n.: An American AI researcher who made 'artificial general intelligence' a widely used name and built a research community around the goal of human-like, broadly capable AI., levels of AGIlevels of AGI, n.: A scheme from Google DeepMind researchers for grading AI by how well it performs and how broad its skills are, from emerging to superhuman, much as self-driving cars are graded by levels., tool AItool AI, n.: AI built to be used like an instrument, answering, suggesting or calculating when asked, rather than pursuing goals on its own. Some argue that this is the only kind we should build.