What is Narrow AI? - Guide to Explore the Tech

Explore “What is narrow AI?” and the nuances around it to build your own solution in-house or outsource it with prior knowledge.

In September 2024, Ramesha Mallya, CTO of DBS Bank India, shared his excitement about using narrow AI within their operations. Why? The technology showed him great potential to bring cutting-edge capabilities for tasks like fraud detection and improvement of customer interfaces. Similar to him, many C-suite employees, their partners, stakeholders, etc., across industries are seeking the potential of narrow AI.

But what is narrow AI? Let’s get our answers around this lucrative new technology through this dedicated editorial.

What is Artificial Narrow Intelligence?

The clarity around “what is narrow AI?” often creates confusion among people. It is simply because there are multiple types and subsets of AI. However, to pin down its exact definition, below we have defined it based on what Gemini said:

“AI that is very good at a specific task but can't do anything else is narrow AI.”

Artificial narrow intelligence (ANI) or weak AI systems are ideally prepared for simple operations, general-purpose tasks, automation, etc. In fact, systems like SIRI, Google Assistant, etc., are all great examples of narrow AI. And, while these systems may seem overly complicated for an average person, they are simply a scratch we have made tapping the potential of artificial intelligence. However, don’t underestimate it because it is the future.

Note: We wanted to discuss the types of narrow AI. However, there are no universally accepted types. Instead, there are operational types (Reactive AI and Limited Memory) and functional types (Natural Language Processing, Computer Vision, etc.).

Capabilities of Narrow AI - Exploring What It Can Do

Here are the core capabilities of narrow AI:

1. Task-Specific Expertise: Narrow AI can precisely perform specific tasks, surpassing humans with much better speed and accuracy.
2. Data Analysis and Pattern Recognition: It has the capability to process tonnes of data, helping in identifying complex patterns, correlations, and anomalies.
3. Automation: It can automate repetitive or mundane tasks. This helps free up human workers for more complex and creative endeavors.
4. Prediction and Recommendation: It has the capability to help with data analysis and personalized recommendations.

Benefits of Narrow AI - What It is Doing

Narrow AI benefits can be a spectrum considering its affinity for integration for a wide variety of applications. However, we’ll only discuss the core ones to avoid information overload.

• Increase Efficiency and Productivity: It can free up humans for more creative and complex tasks, as discussed earlier.
• Better Accuracy and Precision: Narrow AI, being a machine-based system, is more accurate and precise because, unlike human intelligence, it is devoid of distractions and monotony that often leads to human errors.
• Improved Decision-Making: Narrow AI’s capability to process tonnes of data makes it a precise tool to derive data-enabled insights and predictions.
• Personalized Experiences: It has the capability to understand behavioral patterns considering an apt algorithm is used. This empowers narrow AI to deliver personalized recommendations and services.
• Cost Reduction: Narrow AI is capable of automating tasks that are mundane and repetitive. So, employing its system to automate those tasks would tremendously help in cost-cutting for businesses.
• Enhanced Problem-Solving: It can solve highly specific and complex problems that can often be difficult for a human to comprehend.
• 24/7 Availability: Unlike humans that require sleep and rest to work properly, Narrow AI can function 24*7 throughout the year. This unlocks continuous operations and support. 

Applications of Narrow AI - For Everyday Use and Industries

Applications of narrow AI are multifaceted. It can help businesses empower everyday applications for general users, and can help in developing custom AI solutions that cater to different industries. Covering up a few examples, we have provided real-life applications for both.

Everyday Applications

#1 Virtual Assistant

Virtual assistants have become an integral part of our life. The capability to give voice or text commands and perform functions like setting alarms, making phone calls, playing/stopping music, controlling home IoT equipment, etc., feels like magic. In fact, there are plenty of real-life systems that use it, such as:

• Google Assistant
• Siri
• Amazon Alexa
• Microsoft Cortana

#2 Recommendation Systems

Ever thought about how applications like Netflix and YouTube Music are capable of recommending movie titles and music that you end up liking? Well, narrow AI is capable of understanding the watching and listening patterns of its users. 

How do they do this? These systems continuously monitor their users for what they prefer, based on similar preferences of other users and your historical behavior on the app, these systems make recommendations. Aside from Netflix and YouTube, a few other applications that showcase these recommendation systems are:

• Amazon
• Spotify
• Facebook
• Google Maps
• Uber Eats

#3 Spam Filters

Email, at its inception, was a cutting-edge technology. However, before the worldwide boom of the internet, only corporations and select groups of researchers and scientists used it for data exchange. However, as the capability to send and receive emails was normalized, cyberattacks like phishing, malware distribution, email spoofing, botnet recruitment, etc., through spam began. 

Aside from this, many companies started to bombard customers with emails. This created greater inconvenience and threats for general users. In fact, as per AAG, in 2022, around 48.63% of emails globally were spam. This called for innovative solutions that can help mitigate this beyond manual settings on your Gmail, i.e., spam filters. 

Spam filters of today use narrow AI through AI technologies like machine learning, natural language processing, computer vision, etc., to figure out whether an email received is spam or not. Here are some products that use spam filters readily available around us:

• Microsoft Outlook
• Apple iCloud Mail
• Cloudflare Email Security

#4 Search Engines

Have you ever heard the word ‘Googling.’ Well, it is a common phrase used by people to Google stuff. Such is the prominence of search engines today. In fact, as per Internet Live Stats, Google alone processes approximately 40,000 searches per second.

How is it able to do it? By using narrow AI. Google today has indexed hundreds of billions of pages. And, based on the search term provided by the user, through its narrow AI technology it fetches all the relevant pages. A few other search engines that use narrow AI are:

• Bing
• Yandex
• DuckDuckGo

#5 Navigation Apps

All of us have used services like Google Maps to reach our destinations. But how does the tech work? Simple through narrow AI. Essentially, the routes on these systems were developed through satellite images, satellite-level photography, data from the government, and user-submitted data. However, as the integration of routes increased, Google Maps started to use narrow AI.

The AI system helped the navigation app with real-time traffic analysis, route optimization, voice navigation, predictive navigation, etc., to deliver the most optimized and accurate routes. Some navigation apps that use narrow AI are:

• Waze
• Apple Maps
• MapQuest
• Here WeGo

Industry-Specific Applications

1. Healthcare

• Surgical Precision: There are several robots, like Da Vinci Surgical System, SSI Mantra, Cyberknife, etc., that use narrow AI to conduct minimally invasive surgery, moderated radiation sources, and a lot more. These systems can also provide real-time assistance to doctors, like tissue detection, medical imaging, etc.
• Diagnostic Accuracy: These systems use narrow AI to support their clinical decisions. They help in analyzing the medical history, assessing the symptoms of the patients, and even producing lab results. A few examples of narrow AI systems that are used for diagnostic accuracy are IDx-DR, Lunit INSIGHT CXR, PathAI, etc.

2. Finance

• Risk Assessment: Narrow AI is used in finance to detect the risk of each profile. As mentioned earlier, narrow AI can process tonnes of data. This capability, when applied with the right logic, helps detect the financing risks associated with each customer. This helps with preventing credit risks, fraud detection, anti-money laundering, etc.
• Customer Experience: With the help of narrow AI, financial institutions have become capable of delivering personalized recommendations for investment options, loan offers, etc. Other than this, you can employ narrow AI for chatbots, personalized financial advice, streamlined onboarding, etc.

3. Automotive

• Advanced Drive-Assistance Systems (ADAS): Have you heard about features like adaptive cruise control (ACC), lane departure warning, blind spot monitoring, etc. Well, each of these systems utilizes narrow AI to adjust the speed of an automobile, lane marking detection, and cover blind spots for a safer driving experience.
• In-Car Experience: Narrow AI is also used inside your car. Voice assistants, personalized infotainment, and driver monitoring are great examples of narrow AI applications in automotive. They not only help take calls without distracting you from the road but are also capable of detecting fatigue.

Narrow AI vs General AI (Weak AI vs Strong AI) - Establishing Relationship and Key Differences

Though AGI still feels like a farsighted idea, the reality is narrow AI is where everything will begin. So, it is important for us to establish the relationship between them but also the differences. Here are tables to showcase the aforementioned.

Relation Between Narrow AI and AGI

Weak AI vs Strong AI

Limitations of Narrow AI

There is no doubt that the benefits associated with narrow AI outweigh its limitations. Think about it, capabilities like process automation, voice assistance, predictive analytics, etc., would have felt like Sci-fi two decades ago. However, it is a reality and it is important for us to address the limitations of narrow AI helping you with an entire spectrum that ranges from AI integration to app development. So, let’s discuss those limitations.

1. Limitation of Scope

Without data, narrow AI is nothing. In fact, in most cases, until and unless you have trained the narrow AI with labeled data, it won’t understand anything. And, this factor also defines its scope. For instance, if you have fed a narrow AI system, then it will only be able to answer questions like what comes after ‘b’ or before ‘f.’ So, to expand its horizon, you are required to feed it with relevant data.

2. Lack of Human Intellect

We created fire, we created society, and we created the technology that exists today. This didn’t happen in an instant. Instead, it took years of biological and technological evolution for us to reach this stage. But, narrow AI or any type of AI evolves based on the inputs gathered, data fed, etc., which are limited to our processing capabilities. Also, like humans, it is not capable of processing visual data, auditory data, olfactory data, etc., nor does it have hormones that dominate emotion. Therefore, it lacks the capability to make discoveries like Newton based on perception and it is not able to understand emotions or context similar to humans, as of now. 

3. Lack of Explainable AI

Once an AI system is developed, it becomes a black box. What is a black box? It is a system where you can see the input and output but cannot see the internal process, making it unexplainable. And, for us to upgrade these models to the next level, we would require explainable AI which is something we haven’t achieved as of now. Acute research is being conducted to make narrow AI systems explainable. However, it hasn’t been achieved as of yet.

4. Inherent Bias

Amazon had to scrap its AI recruiting tool because it was biased against women. Reason? It was trained over a period of 10 years where the trainers were predominantly men. 

There are other incidents like COMPAS (Case management and decision support tool) being biased against blacks, Google Photos labeling black people as ‘gorillas,’ Microsoft’s Tay chatbot making racist tweets, etc. 

To tackle these issues, concepts like ethical AI standards have been laid out and are being upgraded with the evolution of AI. However, the affinity for bias can still be seen as an unforeseen incident, as the training data for these systems are gathered from everywhere.

5. Job Displacement

Some may argue that the integration of AI won’t lead to job displacement. Rather, those positions will evolve in congruence with AI. However, the actual reality is different. 

In fact, as per a 2022 report by Sage Journal, 14% of respondents said that their jobs got replaced because of AI. Adding to it, Goldman Sachs stated in a BBC news report in 2023 that AI could replace 300 million full-time jobs creating worries amongst the masses.

Narrow AI Compliance - Essentials to Get Complied

Narrow AI as a tech initially gets its power by processing data generated by users, which can often be sensitive. So, it is necessary for narrow AI systems to be compliant depending upon the domain and niche, the solution is created for. Therefore, here are some compliances that need to be kept in mind if you intend to create a narrow AI system.

• GDPR (General Data Protection Regulation): It applies to organizations that are pursuing EU residents for business. It talks about lawful data processing, data minimization, and transparency.
• CCPA (California Consumer Privacy Act): It is similar to GDPR but for citizens of California. 
• HIPAA (Healthcare Insurance Portability and Accountability Act): It applies to healthcare organizations in the U.S., establishing standards for protecting sensitive patient data. 
• AI Act (EU): It is the first major AI legislation that largely affects companies that deploy AI systems.

Aside from this, there are industry-centric regulations that one needs to follow if one wishes to develop a solution for it. For example, for finance, you need to comply with AML (Anti-Money Laundering).

Future of Narrow AI - Understanding Its Future

Considering the pace at which narrow AI has made its way to the markets across industries, the future of it is very bright. However, as of now, there are several limitations to the technology that we learned about earlier. And, researchers and scientists are consistently trying to push the envelope in that direction. So, here are a few plausible directions that narrow AI can take in the future.

1. Better Integration with Other Technologies 

It’s not like narrow AI isn’t getting integrated with other technologies. There are tonnes of examples where technologies like blockchain, IoT, AR/VR, etc., are actively getting integrated with it. However, it isn’t its full scope of integration.

For instance, an ANI system is only developed for a well-defined task, it lacks common sense, is still biased, its capability to process different formats is limited, and so on, limiting its capabilities. Narrow AI is bound to make a huge leap in this direction becoming well-versed for better integration with other tech in the future.

2. Hyper-Specialization Across Niches

Another direction that narrow AI could take is hyper-specialization. Weak AI, as of now, is only created for a specified task, as discussed earlier. However, it can take the route of hyper-specialization in different fields. 

As of now, the usage doesn’t deliver the ultimate level of precision and accuracy. It is still bound to showcase false positives, might make mistakes, and may also show slight deviation in every inference. But, in the future, it can become hyper-specialized. Think of cancer diagnosis-based medical imaging by witnessing bodily changes, or the capability to accurately predict weather with higher precision.

3. Advancements in Natural Language Processing

NLP as a technology is truly a wonder. The capability to understand human language is actually a huge step forward. However, there are still many challenges before we fully realize its power. For instance:

• It can struggle with ambiguity for a single word that has two meanings. For instance, right (direction; correct).
• It can struggle with syntactic ambiguity. For example, “I saw the man with the telescope,” now this could mean I used the telescope, or the man had it.
• NLP systems are not able to understand slang, dialects, and regional variations in language.
• NLP systems require large amounts of high-quality annotated training data, which is expensive and takes time to develop.
• NLP lacks common sense, making it incapable of making inferences.

These are a few challenges. However, there is more, and the chance of NLP getting upgraded in these directions is very high.

4. Better Computer Vision Capabilities

As of today, we have GenAI tools that are capable of generating images and video. However, in order to achieve what we really want, we often need to optimize the prompts, and even after that, the results may not be perfect. 

Characters in the image might be merged together, it could generate flawed text even after defining it in the prompt, etc., are some common issues. Why does this happen? Well, the technology that processes this data to train the model is computer vision, and as of yet, it is ridden with issues. Here are a few limitations that are largely witnessed in computer vision today:

• Incapability to detect varying textures, colors, and object arrangements in real-world conditions.
• Changes in lighting drastically change the appearance of an object, and computer vision still struggles to process it.
• Computer vision is often not able to detect the scale of the objects within the image that shift because of camera angles.

These are a few common problems, but there are more. Computer vision, a narrow AI technology, will be progressing to mend these issues, aiding in creating better GenAI technologies and integrations for other applications. For instance, self-driving cars, facial recognition in law enforcement, identification of intruders by security cameras, etc.

5. Explainability of Narrow AI

As we know, once a weak AI model is developed, it becomes a black box. So, there is a lack of visibility if we aim to understand its internal processes. However, strides are being made in the direction of explanability of narrow AI. 

Institutions like NIST (National Institute of Standards and Technology) and companies like IBM, Google, Microsoft, etc., are actively working in that direction. And, with such huge names working on the explainability of narrow AI, we can definitely estimate success in the near future.

6. Ethical Considerations 

We have already discussed ethical considerations associated with narrow AI in bits and pieces. However, just to recap, a few considerations are bias in results, lack of accountability, lack of transparency, data privacy, etc. These are not mitigated completely as of now because AI is a growing field that will go through multiple transformations. So, we don’t fully understand the scope and issues that may arise in the future.

However, efforts are being made to mitigate the existing issues. Multiple regulations like the AI Act (EU), HIPAA, GDPR, etc., have made their way to make the ecosystem more secure for the users. And, there will be more regulations coming in the future that would establish clear ethical considerations. 

Conclusion

The moment a stakeholder, business, or startup thinks about AI integration, they are essentially talking about integrating narrow AI within their systems. Narrow AI today is omnipresent, from our smartphones to the content we watch on our smart TVs, there is no avoiding it. And, businesses understand its importance in terms of delivering operational efficiency, cost reduction, predictive analytics, automation, and whatnot.

So, with this editorial, we tried our best to share a clear answer to “What is narrow AI?” in the easiest way possible. We hope we may have been able to do it. And, if you want more similar resources, check out our AI knowledge hub to get multifaceted resources on the topic of AI.