Narrow AI vs. General AI vs. Super AI: Key Comparisons


Artificial intelligence (AI) is defined as machine intelligence that mimics a human mind’s problem-solving and decision-making capabilities to perform various tasks. All AI types use machine learning, deep learning, and neural networks to evolve to higher levels. This article deep-dives into the key similarities and differences between narrow AI, general AI, and super AI.

What Is Narrow AI, General AI, and Super AI?

Artificial Intelligence (AI) is machine intelligence that mimics a human mind’s problem-solving and decision-making capabilities to perform various tasks.

How AI Works

AI uses algorithms and techniques such as machine learning and deep learning to learn, evolve, and get progressively better at assigned tasks. AI is categorized into three types based on the human characteristics it can replicate, its real-world applications, and the theory of mind prerequisites:

  1. Artificial narrow intelligence (ANI): AI with a narrow range of abilities
  2. Artificial general intelligence (AGI): AI on par with human capabilities
  3. Artificial superintelligence (ASI): AI that surpasses human intelligence

Let’s have a look at each AI type in more detail.

1. Artificial narrow intelligence (ANI)

Artificial narrow intelligence (ANI), also referred to as weak AI or narrow AI, is application- or task-specific AI. It is programmed to perform singular tasks such as facial recognition, speech recognition in voice assistants, or driving a car. Narrow AI simulates human behavior based on a limited set of parameters, constraints, and contexts.

Some of the common examples of ANI include speech and language recognition demonstrated by Siri on iPhones, the vision recognition feature showcased by self-driving cars and recommendation systems such as Netflix’s recommendations that suggest shows based on users’ online activity. Google’s RankBrain is another example of narrow AI that Google uses to sort results. Such systems only learn or are trained to complete specific tasks.

2. Artificial general intelligence (AGI)

Artificial general intelligence (AGI), also referred to as strong AI or deep AI, is the ability of machines to think, comprehend, learn, and apply their intelligence to solve complex problems, much like humans. Strong AI uses a theory of mind AI framework to recognize other intelligent systems’ emotions, beliefs, and thought processes. A theory of mind-level AI refers to teaching machines to truly understand all human aspects, rather than only replicating or simulating the human mind.

Although AGI hasn’t been realized yet, it has drawn the attention of top tech companies such as Microsoft, which invested $1 billion in AGI through the venture OpenAI. Also, in an attempt to achieve strong AI, Fujitsu has built the K computer, which is recognized as one of the fastest supercomputers in the world. Similarly, China’s National University of Defense Technology has built Tianhe-2, a 33.86-petaflops supercomputer.

3. Artificial superintelligence (ASI)

Artificial superintelligence (ASI) is a type of AI that surpasses human intelligence and can perform any task better than a human. ASI systems not only understand human sentiments and experiences but can also evoke emotions, beliefs, and desires of their own, similar to humans.

Although the existence of ASI is still hypothetical, the decision-making and problem-solving capabilities of such systems are expected to be far more superior to those of human beings. Typically, an ASI system can think, solve puzzles, make judgments, and take decisions independently.

How do narrow, general, and super AI work?

Irrespective of the type, AI typically has three basic abilities:

1. Perceive the surrounding environment

Here, the AI model gathers surrounding data about relevant subject matter.

2. Detect patterns in the environment

Upon collecting relevant data, the AI model looks for common data patterns.

3. Learn from these patterns and update understanding for future decisions

The AI model then learns from data patterns and updates its understanding over time. For AGI, this can include becoming more self-aware, creative, and improving cognitive capabilities comparable to humans. Similarly, for ASI, this stage can entail developing emotions, beliefs, and experiences of its own and further enhancing the cognitive abilities that eclipse human intelligence. 

See More: What Is the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?

Key Similarities Between Narrow,  General, and Super AI

Artificial intelligence (AI) allows machines to learn from previous experiences, adjust to new inputs or stimuli, and perform human-like tasks with better efficiency. AI has been adopted across diverse industry verticals ranging from finance, healthcare, and aviation to manufacturing and supply chains.

While narrow AI has become part of our day-to-day lives, AGI is still in its early stages, and the hype around ASI is tremendous and unwavering. However, all these types of AI have evolved from the same roots and have certain undeniable similarities.

Key Similarities Between Narrow AI, General AI, and Super AI

Let’s understand the key similarities between ANI, AGI, and ASI.

1. Capability to predict and adapt

All types of AI use algorithms to identify and discover patterns in data. The identified patterns are then used by AI solutions to learn and adapt, much like how humans learn from repetitive tasks. All the analyzed data is further used to make future decisions and derive meaningful predictions.

Various software systems used today, such as systems used to correct spelling errors, predict what the user is going to type, or to give the user the shortest route to their destinations, have machine intelligence functioning at their core.

The same principle applies to autonomous vehicles that make navigational decisions based on traffic and surrounding data. The field of law presents asimilar application, where an AI solution reviews several documents and flags the relevant ones to a particular case. Upon flagging the relevant document, algorithms can then easily find similar documents. Such AI applications can also be used for litigation prediction, as they can evaluate the risk factors of cases based on document content and predict judgment outcomes.

2. Make decisions on their own

AI augments human intelligence, delivers insights from gathered data, and also learns to enhance overall productivity. AI’s ability to analyze data, self-learn, gain insights, and make improved decisions makes it a powerful tool, irrespective of the application.

Current narrow AI solutions make their own decisions based on the data that has been programmed into them. On similar lines, with in-built human-like cognitive abilities in AGI and ASI, future AI solutions may invariably make more thorough decisions that are optimal and faster than their human counterparts.

3. Imitate human cognition

All kinds of AI systems essentially imitate the human mind and solve complex issues. They effectively comprehend the world and respond appropriately, much like humans studying their surroundings, deriving inferences, and then interacting with them properly.

Narrow AI’s machine intelligence uses natural language processing (NLP) to understand speech and text in natural language. This is evident in chatbots and virtual assistants such as Cortana. Current AI is thus programmed to imitate human cognition to interact with humans in a natural and personalized manner. As AGI and ASI systems are advanced versions of ANI, this imitation of human cognition can only be expected to expand and progress further going ahead.

4. Continually learn and evolve

AI refers to a machine’s ability to learn from common data patterns. AI systems use deep learning, which is a subset of machine learning, to continually learn and evolve. Most deep learning methods, often referred to as deep neural networks, use neural network architectures and are trained by using large sets of labeled data. Here, ‘deep’ represents the number of hidden layers in the neural network. Compared to traditional neural nets with two to three hidden layers, deep neural nets have close to 150 layers.

Currently, the most familiar examples of deep learning include Amazon’s Alexa, Google Assistant, and Samsung Bixby. Such AI systems use algorithms to develop analytical models and perform tasks by undertaking numerous rounds of trial and error.

Illustration of Deep Learning-based Conversational AI Chatbot

5. Eliminate mundane tasks through automation

All types of AI replace routine and mundane tasks in an organization by offering automated solutions. This allows employees to focus on critical tasks.. Smart warehouses are fitted with all necessary automation and AI systems that free employees from the worry of moving heavy objects. Rather, automated robots are equipped to perform these activities. Moreover, inventory is also automatically calculated, leaving employees to handle tasks requiring human attention, such as ensuring that automated systems operate properly. This boosts efficiency and saves the time and effort of employees while reducing overhead expenses.

With AGI and ASI along the horizon, activities that require careful human monitoring will also be taken up by intelligent agents that showcase superior intelligence as compared to humans in the future.

6. Facilitate human augmentation

A combination of AI and people can bring different abilities and strengths together. Augmented intelligence takes any form of AI to higher levels. Consider the example of IBM’s Deep Blue, a computer that defeated chess grandmaster Garry Kasparov in the late 1990s. After losing, Garry experimented on how a computer helper can lead to raising the level of chess played by only a human or a computer. He discovered that humans, machines, and a good process, can result in better outcomes than a single, strong computer.

Today’s narrow AI has been quite effective in helping humans complete critical tasks. With AGI and ASI in near sight, technologies such as automation may further increase the effectiveness of human intelligence. This would imply the overall output of ‘human intelligence plus machine intelligence’ will be superior to that of a human or machine alone.

See More: What Is Artificial Intelligence (AI) as a Service? Definition, Architecture, and Trends

Key Differences Between Narrow AI, General AI, and Super AI

It is safe to say that advances in AI have made our lives easier today. As ANI becomes an integral part of our lives and AGI and ASI come to the fore, we stand at the cusp of realizing the true power of AI technology.

That being said, each type of AI is distinctly different from the other. Let’s understand the key differences between ANI, AGI, and ASI.

Category Narrow AI General AI Super AI
Definition Narrow AI is focused on a specific, singular, or focused task and lacks the self-expansion functionality to solve unfamiliar problems. Strong AI can perform a broad range of tasks, reason, learn, and improve cognitive capabilities comparable to humans. Super AI demonstrates intelligence beyond human capabilities.
Purpose Narrow AI is programmed to operate within a set of pre-defined functions to complete or address a specific problem. Strong AI will have a mind of its own and will be able to accomplish any type of task that its ‘mind’ can envision. Super AI will surpass human intellect to accomplish any task better than its human counterparts.
AI Model Narrow AI uses fixed domain models that are programmed. Strong AI self-learns and reasons with its operating environment. Super AI self-learns and evolves with a consciousness of its own.
Awareness Narrow AI lacks self-awareness, artificial consciousness, or cognitive abilities. Strong AI will be considered truly advanced, intelligent, and fully self-aware, implying that it will possess common sense, creativity, and the ability to express emotions. Super AI will simulate human reasoning and experiences to develop an emotional understanding, beliefs, and desires of its own.
Data Processing Narrow AI classifies data by using machine learning, natural language processing, artificial neural networks, and deep learning. Strong AI leverages clustering and association by using advanced versions of machine learning, deep learning, NLP, and artificial neural networks. Super AI may use the human brain as a model to elicit behavioral intelligence and understand and interpret human emotions and experiences.
Knowledge Transfer Narrow AI does not facilitate knowledge transfer to other domains or tasks. Strong AI leverages knowledge transfer to new domains and tasks. Super AI will invariably exercise knowledge transfer across tasks and domains.
Implications Narrow AI outperforms humans in specific repetitive tasks such as driving, medical diagnosis, and financial advice. Strong AI competes with humans across all endeavors, from earning university degrees to handling medical emergencies. Super AI outperforms humans to achieve societal objectives and facilitate space exploration, but also threatens the very existence of the human race.
AI Stage Today’s AI Future AI – around 2040 Soon after AGI

See More: AI Job Roles: How to Become a Data Scientist, AI Developer, or Machine Learning Engineer


Since the inception of AI in the mid-20th century, all that we have managed to achieve is the paradigm of narrow AI. It performs tremendously well when it comes to accomplishing designated tasks. Once narrow AIgets as sophisticated as the human brain, we may touch the realm of AGI.

Upon mastering the general AI stage where it surpasses human intellect across every field, we can begin to envision a future that marks the beginning of a super AI era. The future would then mean that we are surrounded by more intelligent, conscious, and self-aware entities. However, the question remains, will we ever reach these stages to achieve ultimate AI supremacy?

Do you think we will ever achieve ultimate AI supremacy? Comment below or let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to hear from you!