What is narrow AI (weak AI)?

 Narrow AI, also known as weak AI, is an application of artificial intelligence technologies to enable a high-functioning system that replicates -- and perhaps surpasses -- human intelligence for a dedicated purpose.

Narrow AI is often contrasted with artificial general intelligence (AGI), sometimes called strong AI; a theoretical AI system that could be applied to any task or problem.

Examples of narrow AI

All forms of modern AI systems can be classified as narrow AI. They are as follows:

Image and facial recognition systems. These systems, including those used by social media companies like Facebook and Google to automatically identify people in photographs, are forms of weak AI.

Chatbots and conversational assistants. This includes popular virtual assistants Google Assistant, Siri and Alexa. Also included are simpler, customer-service chatbots, such as a bot that assists customers in returning an item to a retail store.

Self-driving vehicles. Autonomous or semiautonomous cars, such as some Tesla models and autonomous drones, boats and factory robots, are all applications of narrow AI.

Predictive maintenance models. These models rely on data from machines, often collected through sensors, to help predict when a machine part may fail and alert users ahead of time.

Recommendation engines. These systems that predict content a user might like or search for next are forms of weak AI.

Advantages and disadvantages of narrow AI

Advantages. Narrow AI systems can perform single tasks well, often better than humans. A weak AI system designed to identify cancer from X-ray or ultrasound images, for example, might be able to spot a cancerous mass in images faster and more accurately than a trained radiologist.

Meanwhile, a predictive maintenance platform could analyze incoming sensor data in real time, a feat virtually impossible for a person or group of people to do, to predict roughly when a piece of a machine will fail.

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Disadvantages. Still, narrow AI systems can only do what they are designed to do and can only make decisions based on their training data. A retailer's customer-service chatbot, for example, could answer questions regarding store hours, item prices or the store's return policy. Yet, a question about why a certain product is better than a similar product would likely stump the bot, unless its creators took the time to program the bot to respond to such questions specifically.