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Artificial narrow intelligence is a weak AI version that only focuses on handling a specific subset of tasks.

Artificial narrow intelligence (ANI) is defined as 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. This article explains the fundamentals of narrow AI, its key advantages and challenges, and the top 10 best practices for narrow AI development.
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.
Narrow AI
Narrow Artificial Intelligence
Artificial narrow intelligence systems are programmed to attend one task at a time by pulling in information from a specific dataset. In other words, such systems do not go beyond the assigned tasks.
Unlike general AI, narrow AI lacks self-awareness, consciousness, emotions, and genuine intelligence that can match human intelligence. While such systems may appear sophisticated and intelligent, they operate under a predetermined and predefined set of parameters, constraints, and contexts.
The machine intelligence that surrounds us today is a part of the same narrow AI. Examples include Google Assistant, Siri, Google Translate, and other natural language processing tools. Although these tools can interact with us and process and comprehend human language, they are termed as weak AI as they lack the fluidity or flexibility to think for themselves as humans do. 
Let’s consider Siri. It is not a conscious machine. Instead, it is only a tool performing tasks. When we converse with Siri, it processes the human language, enters it into the system’s search engine like Google, and provides results.
When someone poses abstract questions such as how to handle a personal problem or deal with a traumatic experience to tools like Alexa or Google Assistant, they either give vague responses that lack sense or provide links to articles on the internet that presumably address the issue at hand. 
On the contrary, when we ask a fundamental question such as “what is the temperature outside”, we tend to get an accurate response from virtual assistants such as Siri. This is because answering such basic questions is within the range of Siri’s intelligence for which it is designed.
Moreover, even something as complex as self-driving cars falls under weak AI, as they are trained to navigate the surrounding area with the help of an annotated driving dataset. A typical self-driving vehicle comprises multiple ANI systems that are critical for its smooth movement in a highly complex urban environment.
See More: What Is Artificial Intelligence (AI) as a Service? Definition, Architecture, and Trends
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
Advantages of Narrow AI
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.
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.
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.
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. 
Challenges of Narrow AI
Challenges of Narrow 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.
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.
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.
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.
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.
See More: Top 10 Machine Learning Algorithms
AI development is contributing immensely to improving people’s lives around the world, right from business operations, aviation, manufacturing, healthcare to education. As AI systems become an integral part of every industry vertical, discussions on the best ways to incorporate fairness, interpretability, privacy, and security into these systems are being opened up.
Here are the top 10 best practices for narrow AI development. 
Best Practices for Narrow AI Development
Best Practices for Narrow AI Development
The true impact of an AI system’s predictions, recommendations, and decisions can be evaluated by factoring in how actual end-users experience the system. The following routines can be considered to keep a check on your narrow AI development:
To understand the tradeoffs between several errors and user experiences of the AI system, one should consider several essential metrics rather than opting for a single one.
Analyzing raw data can help you better understand the working of ML models as they reflect the data that they are trained on. In cases where sensitive raw data is concerned, you can instead focus on understanding the input data while respecting privacy.
Understanding the limitations of the dataset and AI model is vital to keep track of the loopholes of narrow AI.
To ensure that the designed AI system works as intended and can be trusted, you must undertake quality test practices.
Regular monitoring will ensure that the AI model considers real-world performances and incorporates user feedback to update the AI system.
Today, AI systems are used across industry sectors to perform critical tasks such as predicting the severity of a medical condition and matching profiles to jobs or marriage partners. The risk here is that any unfairness in such computerized decision-making systems can have a wide-scale impact. Hence, as AI penetrates across societies, it is crucial to design a fair and inclusive model for all.
Narrow AI systems have improved our lives as automated predictions and decision-making has become mainstream. This may relate to several examples, from music recommendations to monitoring a patient’s vital signs. Despite the proliferation of narrow AI across fields, interpretability is crucial to understand and trust AI systems. The following interpretability practices before, during, and after designing and training AI models can be considered.
ML models are programmed to learn from training data and make subsequent predictions on input data. In some cases, both training and input data can be sensitive. Take the example of a tumor detector trained on biopsy images and deployed on an individual patient’s tumor scans. Here, it is crucial to consider the privacy implications while handling sensitive data. It may include legal and regulatory requirements, social ethics, and the patient’s expectations.
Security of AI systems involves determining whether the system is behaving as intended, irrespective of how attackers try to interfere. Addressing the security of an AI system before entirely relying on it is essential for safety-critical applications.
See More: AI Job Roles: How to Become a Data Scientist, AI Developer, or Machine Learning Engineer
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.
Do you think weak AI will ever become strong AI? Comment below or let us know on LinkedIn, Twitter, or Facebook. We’d love to hear from you!

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Published On: March 27, 2022 / Categories: Uncategorized /

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