What Does Narrow AI Mean?
The Quick Answer
Narrow AI are limited artificial intelligence (AI) systems designed to perform specific tasks, operating under a limited set of constraints.In Detail
The term "narrow AI" refers to systems designed to perform specific tasks, operating under a limited set of constraints. Examples of Narrow AI include recommendation systems, image recognition software, voice assistants, and automated driving technologies. These systems may seem intelligent within their specific task domain, but they operate under a narrow set of parameters and constraints. They can't demonstrate an understanding or consciousness outside of their designated functions. For instance, a chess-playing AI can defeat grandmasters but cannot understand or perform tasks outside of playing chess. Currently, all existing real-world applications of AI are examples of narrow AI. They've shown impressive capabilities, such as diagnosing diseases or translating languages, but are fundamentally limited. They don't possess the ability to generalize learning or knowledge across different contexts, unlike what we expect from "Artificial General Intelligence" (AGI). Despite its current limitations, Narrow AI continues to have profound impacts across various sectors, transforming industries and creating new possibilities for technological advancement. Narrow AI is also known as "weak AI."Learn More
Check out our comprehensive glossary of AI terms.Image for Narrow AI
When I write Narrow AI, I mean this:
"Limited Systems Designed to Perform Specific Tasks"
Summary of Key Points
We have summarized the key points in the table below:Narrow AI | |
---|---|
Definition: | Limited Systems Designed to Perform Specific Tasks |
Type: | Word |
Guessability: | 3: Guessable |
Typical Users: | Adults and Teenagers |
Example of Narrow AI Used in a Text
Narrow AI
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AI/MLAAS (artificial intelligence/machine learning as a service)
ANN (artificial neural networks)
AUTOML (automated machine learning)
CNN (convolutional neural networks)
DL (deep learning)
DNN (deep neural network)
GAN (generative adversarial network)
GRU (gated recurrent unit)
IOT (internet of things)
LSTM (long short-term memory)
ML (machine learning)
NLP (natural language processing)
RL (reinforcement learning)
RNN (recurrent neural networks)
SUPERINTELLIGENT AI (AI systems that surpass human intelligence in practically every field)
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