Narrow artificial intellegence (narrow AI)

 Narrow artificial intelligence (narrow AI) is a specific type of artificial intelligence in which a learning algorithm is designed to perform a single task, and any knowledge gained from performing that task will not automatically be applied to other tasks.

What Is Narrow Artificial Intelligence? 

Artificial narrow intelligence (ANI) refers to the goal-oriented version of AI designed to better perform a single task such as tracking weather updates, generating data science reports by analyzing raw data, or playing games such as poker, chess, etc.

Advantages and Challenges of Narrow AI

Current AI and intelligent machines come under the ‘weak AI’ category. However, this does not discount the benefits of narrow AI, as it is one of the most significant human innovations and intellectual accomplishments.

First, let’s understand the advantages of narrow AI.

                                                      Advantages of Narrow AI

1. Facilitates faster decision making

Artificial narrow intelligence systems facilitate faster decision-making as they process data and complete tasks significantly quicker than humans. As a result, they allow us to boost overall productivity & efficiency and thereby improve the quality of life. For example, artificial narrow intelligence systems such as IBM’s Watson assist doctors in making quick data-driven decisions by harnessing the power of AI. This has made healthcare better, faster, and safer than ever before.

2. Relieves humans from mundane tasks

Developments in narrow AI have ensured that humans are relieved from several dull, routine, and mundane tasks. It has made our day-to-day lives easier, right from ordering food online with the help of Siri to reducing the effort of analyzing volumes of data to produce results.

Additionally, technologies such as self-driving cars have relieved us from the stress and burden of being stuck in traffic for long and instead provided us with more leisure time to carry out activities or tasks of our interests.

3. Serves as a building block for the development of more intelligent AI

Artificial narrow intelligence systems serve as the foundation for the eventual development of more intelligent AI versions such as general AI and super AI. Speech recognition allows computers to convert sounds to text with significant accuracy, while computer vision enables the recognition and classification of objects in video streams. Currently, Google is using AI to caption millions of YouTube videos.

Today, AI-powered computer vision is already used to unlock screens and tag friends online. Concurrently, the autonomous vehicle sector is exploring the field of ‘affective AI’ where the system can learn non-verbal nuances (feelings, emotions), and prompt sleepy truck drivers to stay alert and pay attention while driving. All these foundational technologies are only paving the way for future self-aware and conscious versions of AI.

4. Performs single tasks better than humans

Narrow AI systems can perform single tasks far better than humans. For example, a narrow AI system programmed to detect cancer from X-ray or ultrasound images might be able to quickly spot a cancerous mass in a set of images with substantially higher accuracy as compared to a trained radiologist.

Another example is that of a predictive maintenance system used at manufacturing plants. The system collects and analyzes incoming sensor data in real-time to predict whether a machine is about to fail. Narrow AI automates this task. The entire process is much quicker and is virtually impossible for an individual or group of individuals to match as far as speed and accuracy are concerned.

The overall performance, speed, and accuracy of narrow AI supersede that of human beings. That being said, the AI community faces several critical challenges in broadening the scope of narrow AI.

Now, let’s go over the challenges that narrow AI faces. 

1. Absence of explainable AI

One of the essential requirements for the progress of artificial intelligence is the practice of creating AI that is less of a black box. This implies that we must be better positioned to understand what’s happening in neural networks. Today’s AI systems, such as one recommending books to read, employ the black-box approach effectively. The deep learning algorithm used in such cases considers millions of data points as inputs and correlates specific features to provide a result. The underlying process is self-directed and challenging for programmers and experts in the domain to interpret.

However, when people are making high-stake business decisions that involve huge investments by relying on AI models, such a black-box approach can be detrimental as the inputs and operations of the system are not visible to the concerned parties. Thus, one of the key challenges is creating more explainable AI devoid of the black-box approach.

2. Need for impenetrable security

Neural networks are exploited extensively by narrow AI. However, it is vital to understand that AI is quite fragile– it is possible to inject noise and fool the system. For example, an attacker can hack into the software system of autonomous cars and change the AI program code so that the program may mistake a bus on the road for an elephant. This can have serious implications and ramifications. A hacker can also hijack the entire network of autonomous vehicles operating in an area and eventually wipe out a billion-dollar investment.

Moreover, a single intrusion into a neural network can disrupt the operations of several systems reliant on that same network. Additionally, as neural networks are subject to attacks, providing impenetrable security remains a crucial challenge.

3. Need to learn from small data

AI models are trained on data derived from examples–implying that examples are the real currency to today’s AI. For AI to evolve further, it must be prepared to learn more from less data. AI should be able to transfer its learning from one neural network to other networks by leveraging prior knowledge.

AI blends learning and reasoning. Although today’s AI has made significant progress in learning and accumulating knowledge, applying reason to that knowledge remains a challenge. For example, a retailer’s customer service chatbot could answer questions related to store hours, product prices, and the store’s cancellation policies. 

However, a tricky question about why product X is better than a similar product Y may freeze the bot. Although creators can program bots to answer such questions, teaching an AI to apply reasoning by itself remains a problem for most scientists and experts.

4. Prone to bias

Today’s AI systems are prone to bias as they often give incorrect results without a plausible explanation. Complex AI models are continually trained on vast amounts of data that contain biases or inaccurate information. As a result, a model trained on such a biased dataset could consider the incorrect information trustworthy and make skewed predictions.

As AI systems learn from past examples, consider a system responsible for making credit decisions. The system might consider ‘not offering credit to women or minorities’ as appropriate based on previous patterns. Thus, verifying and inspecting that the examples used by the system are free of biases remains a critical challenge.

Moreover, as narrow AI lacks the ‘common sense’ aspect, or a sense of fairness and equity, handling training bias requires substantial planning and design work.

5. Subject to human failings

Narrow AI largely relies on humans to put to task. Hence, it is prone to human failings, such as people setting overly ambitious business targets or prioritizing tasks incorrectly.

Consider a situation where a human wrongly defines a task. In this case, irrespective of how long a machine works or the number of computations it performs, the end result will still be a false conclusion. Therefore, narrow AI’s reliance on fallible humans is a huge challenge for experts in the domain.


Today, almost every industry has embraced narrow AI as it achieves superhuman accuracy and performance when accomplishing specific tasks. Factors such as robust IoT connectivity, the proliferation of connected devices, and faster computing realms have propelled the progression of AI systems. While current AI outperforms humans, the challenge now is how narrow AI can evolve into a broader form of general and super AI. Only time will tell how AI masters cross-domain tasks by building new neural networks from scratch while it switches from one domain to another.

Updated :10 Feb 2024



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