“Narrow AI” refers to artificial intelligence that is designed for a specific task and lacks a broader understanding or capability. It excels within its specific domain but cannot perform duties outside it, nor does it possess general cognitive abilities.


Imagine you have a super-smart robot that’s the best pizza maker in the world. It can make any kind of pizza you want perfectly, but if you ask it to make sushi or drive a car, it can’t do it. That’s like Narrow AI - really good at one specific thing, but can’t do things outside its special job.

In-depth explanation

“Narrow AI”, also referred to as weak AI, denotes systems designed and trained for a particular task. These AI systems excel in the tasks they are created for, but they lack the understanding or capability to perform anything outside their trained sphere.

For instance, recommendation algorithms, such as the one implemented by Netflix to suggest movies or series based on a user’s watch history, are examples of Narrow AI. They excel in predicting which media content a user may like, but they are incapable of applying that prediction capability outside the domain of recommendation.

The main driving factor behind Narrow AI is machine learning, where a model is trained on a vast quantity of data related to a specific task, and the model learns to make predictions or decisions based on that data. The data required for training these models are task-specific. For example, a model trained on movie reviews (for sentiment analysis) cannot be used to predict the stock market.

Narrow AI developers use several techniques in machine learning, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning. However, regardless of the machine learning technique used, the resultant AI is always narrow, as it can only perform tasks it has been trained to do.

It’s important to note that Narrow AI doesn’t understand or comprehend tasks the same way a human might. Instead, they use pattern recognition and statistical analysis to generate their results. They don’t have a consciousness or a general intelligence.

Machine Learning, Artificial General Intelligence (AGI),, Supervised Learning, Unsupervised Learning, Reinforcement Learning (RL),, Deep Learning, Recommendation Systems, Sentiment Analysis.