What Are the Types of Artificial Intelligence: Narrow, General, and Super AI Explained

• Artificial intelligence is defined as the capability with which a machine can exhibit intelligent characteristics.
• It has been classified into three types to determine the level and scope of this intelligence.

The types of artificial intelligence are a way to visualize the future of the technology as AI begins to take on the more human aspects of cognition. Learning about the types of AI is integral to understanding how things may progress in the future.

What is Artificial Intelligence?

Artificial intelligence refers to any cognitive process exhibited by a non-human entity. Today’s AI includes computer programs that perform tasks similar to human cognition, including learning, vision, logical reasoning, and more.

Owing to its several benefits, AI is prevalent in the enterprise and consumer space today. Modern algorithms can provide labor that is as accurate as a human employee, except many times faster.

AI has long been researched as a way to bring automation to the same level of cognitive functioning as humans. Now, smart automation is but one of the many applications of AI algorithms and software.

Artificial intelligence has many subcategories, such as neural networks and deep learning, machine learning, computer vision, and natural language processing. These are used mainly in smartphones and Internet applications, as they offer a quick and easy way to optimize the user experience.

When asked about the benefits of artificial intelligence, Oliver Tearle, Head of Research, The ai Corporation, says, “AI and machine learning exist for two purposes: solving problems that require a lot of repeated, often complex manual effort, and to solve problems humans can’t solve. It is impossible for a human to continually understand and extract insight from very large data sources that grow over time. A machine can do this very efficiently and never gets tired. It continually works on the problem with sustained effectiveness.

Applying AI and machine learning throughout a business not only saves time and effort by replacing manual processes with machines, but it allows those teams to innovate and pursue revenue generation activities.

What’s more, the machine can use the insight from the data it has seen to predict future behavior. AI and ML can be implemented as an insight generation tool, allowing a business to remain on top of the critical behavior of its customers. Enabling a business to take swift action to make the most of events identified by the technology.”

Types of Artificial Intelligence

In order to differentiate between the degree to which AI applications can conduct tasks, they are generally split into three types. These types are distinct from each other and show the natural progression to AI systems today.

Contrary to popular belief, artificial intelligence is not here to replace humanity, as it is nothing but a technology that is used by humans. In its current state, it is merely a program that can be trained to perform tasks. As it advances in the future, it is important to create a legal, ethical, and moral framework to govern AI.

See More: What Is Artificial Intelligence: History and Future of AI

The consequences of AI’s advancement must be considered when taking a look at the types of artificial intelligence that have been proposed to exist in the future. As the scale progresses from generally lower to higher intelligence, more human-like characteristics, such as emotions and thought processes, might present themselves. This makes the regulation of AI and its related technologies a difficult undertaking for a developed society.

It’s crucial to have a forward-thinking mentality as the future of artificial intelligence has been mapped out, primarily by the types of AI that are described today. Let’s dive deeper into what these types are.

Narrow Artificial Intelligence

Narrow AI is the most prevalent kind of AI today. From applications in mobile phones to the Internet to big data analytics, narrow AI is taking the world by storm.

The name stems from the fact that this kind of artificial intelligence systems are explicitly created for a single task. Owing to this narrow approach and inability to perform tasks other than the ones that are specified to them, they are also called as ‘weak’ AI. This makes their intelligence highly-focused on one task or set of tasks, allowing for further optimization and tweaking.

Why Is It Called Narrow Intelligence?

Narrow AI is designed for specific tasks and cannot perform tasks other than the ones specified to it. This narrow focus is mainly due to the following factors:

• Narrow AI is a Complex Computer Program
  Narrow AI is usually restricted in its scope because it is created to solve a problem. It is built with the explicit focus of ensuring that a task is completed, with its architecture and functioning representing this.

Today’s narrow AI is not made up of non-quantifiable parts; it is just a computer program running as per the instructions given to it. Due to these constraints and specified use-case, narrow AI has a laser focus on the tasks it was created for.

• Narrow AI is Created as per Today’s Standards and Tools
  With the increase in adoption of AI by enterprises, the technology is expected to do one thing and do it well. While the use-case might differ from company to company, the expectations are the same – an exponential increase to the bottom line. Today, this can be done only with narrow AI.Narrow AI is developed in an environment where the problem is front-and-center, using the latest technology. Artificial intelligence, in general, is a highly research-oriented field, and the research groundwork to create something more than a single use-case system has not been laid out yet. This creates AI with a narrow focus.

Owing to its high efficiency, speed, and consumption rate over humans, narrow artificial intelligence is one of the go-to solutions for corporates. For a wide variety of low-level tasks, narrow AI can employ smart automation and integration to provide efficiency while maintaining accuracy.

This makes it a favored choice for tasks that involve million-scale datasets, known as big data. With personal data collection being prevalent, companies have a large amount of big data at their disposal that can be used for training AI and deriving insights from it.

Examples of Narrow AI

Today, narrow AI is also used in various Internet applications that we use on a daily basis. Following are a few examples of narrow AI:

• Recommendation Systems
  Whenever you see a ‘recommended’ tag on a website, it is usually put there by a narrow AI. By looking at a user’s preferences with respect to the database of content or information, an AI is able to determine their likes and dislikes.
  This is then used to offer recommendations, thus providing a more personalized experience to the user. This is commonly seen on sites, such as Netflix (“Because you watched…”), YouTube (“Recommended”), Twitter (“Top Tweets first”), and many more services.
• Spam Filtering
  Narrow AI with natural language processing capabilities is employed to keep our inboxes clean. Employees cannot check every spam email they receive, but it’s a perfect task for narrow AI.
  Google is the leader in providing email services and has evolved its narrow AI to a point where it conducts various email-related tasks. Along with AI-powered spam filters, they can also assign a category to a particular type of email (promotions, reminders, important, and so on).
• Expert Systems
  Narrow AI has the capability to create expert systems, which could pave the way for the future of AI. Just as human intelligence is comprised of various senses and logical, creative, and cognitive tasks; expert systems also consist of many parts.
  Expert systems may represent the future of artificial intelligence. This term is used to denote an AI system made up of many smaller narrow AI algorithms. For example, IBM Watson, which is made of a natural language processing component along with a cognitive aspect.

Artificial General Intelligence (AGI)

While narrow AI refers to where artificial intelligence has reached today, general AI refers to where it will be in the future. Also known as artificial general intelligence (AGI) and strong AI, general artificial intelligence is a type of AI that can think and function just as humans do.

This includes perceptual tasks, such as vision and language processing, along with cognitive tasks, such as processing, contextual understanding, thinking, and a more generalized approach to thinking as a whole.

While narrow AI is created as a means to execute a specific task, AGI can be broad and adaptable. The learning part of an adaptive general intelligence also has to be unsupervised, as opposed to the supervised and labeled learning that narrow AI is put through today.

AGI is still a far off reality, as the tools required to build it are not available today. Many argue that neural networks are a dependable way to create the forerunners of what can be called artificial general intelligence, but the reality of the situation is that human intelligence is still a black box.

While we as humans are beginning to decode the inner workings of our minds and brains, we are still a long way from figuring out what ‘intelligence’ means. In addition to this obstacle, the need to define ‘consciousness’ is integral to creating a general AI. This is due to the fact that an AGI needs to be ‘conscious’ and not just an algorithm or machine.

Challenges of General Artificial Intelligence

There are various pitfalls associated with general artificial intelligence. Some of them include:

• Replicating Transfer Learning
  Transfer learning is a term used to denote applying the knowledge learned in one domain to another. This is something that humans engage in every day and is an important part of society. For example, the knowledge of how to ride a bicycle is applied in riding a motorcycle.

  This is something that neural networks have become better at doing recently; a positive sign for the future. A capable AGI needs to have strong transfer learning capabilities to avoid retraining.

• Enabling Common Sense and Collaboration
  Common sense is integral to human functioning, along with collaboration on tasks with other human minds. Due to the narrow nature of today’s algorithms, dependable collaboration has not been achieved, with common sense being a far-off reality.

  To create a true general artificial intelligence, it needs to be imbued with such characteristics to ensure that it is not just another narrow AI. This will set it apart from the systems that came before it, as it will not only collaborate with other machines but also with humans.

• Figuring Out Consciousness and Mind
  Consciousness is an integral part of being human and being conscious is the most resilient method of determining the existence of intelligence. In addition to this, the human mind is still something that has not been decoded. These remain as significant obstacles to the creation and achievement of general artificial intelligence.

Learn more: Embracing Technology Using AI

Artificial Super Intelligence

Artificial super intelligence (ASI) is a term used to denote an AI that exceeds human cognition by a great extent in every possible way. This is one of the most far-off theories of artificial intelligence but is generally considered to be the eventual endgame of creating an AI.

While artificial super intelligence is still a theory at this point in time, a lot of scenarios involving it have already been envisioned. A common consensus among those in the field is that ASI will come from the exponential growth of AI algorithms, also known as ‘Intelligence Explosion’.

Intelligence explosion is a concept required for the creation of artificial super intelligence. As the name suggests, it is an explosion of intelligence from human-level, general artificial intelligence to an unthinkable level. This occurs through recursive self-improvement.

Self-improvement in AI comes in the form of learning from user input in neural networks. Recursive self-improvement, on the other hand, is the capacity of an AI system to learn from itself, at rapidly increasing levels of increasing intelligence.

To illustrate this, take the example of an AGI functioning at the level of average human intelligence. It will learn from itself, using the cognitive capabilities of an average human, to reach genius-level intelligence. However, this begins compounding, and any future learning made by the AI will be conducted at a genius-level cognitive functioning.

This accelerates at a fast pace, creating an intelligence that is smarter than itself at every step. This continues to build up quickly, until the point where intelligence explodes, and a super intelligence is born. Containing or creating a super intelligence is something that we as a human race are far from, making it an entry for sci-fi novels. For now.

Theory of Mind in Artificial Intelligence

In psychology, the theory of mind is the ability to connect what one is feeling to the reality that they are feeling it. In other words, it is the ability of humans to attribute their mental state to themselves while recognizing that they are different from others.

It is an important part of social cognition and is integral for a human being to function in society. Replicating the theory of mind and a construct such as the ‘mind’ may be what we are missing to create a true general artificial intelligence.

In a way, the theory of mind is a way for individuals to create a simulation of the consequences of their actions. This can be replicated in AI by creating an internal simulation which contains a model of the AI itself, along with a model of the world.

This brings a perspective of ‘common sense’ to the model and can represent solving one of the primary challenges of creating a general AI. Experts believe that this is one of the biggest steps that will bridge the gap between the neural networks of today to create a stronger, more adaptive AI.

Key Takeaways

Artificial intelligence can be leveraged by techies to stay ahead of the curve in the IT space today. As this technology is transforming at a rapid pace, those in the market are especially required to stay ahead of the curve.

It is also important to understand concepts, such as AGI and ASI, as they may eventually turn into reality. Moreover, it is also important to note that we are at the beginning of using AI, and the algorithms used today are restricted to narrow tasks.

It is safe to say that AGI and ASI will disrupt the world in a way we cannot envision. The narratives portrayed in movies involving AI, especially those such as ‘Her’ and ‘I, Robot’, may, in fact, come true with the rise of general AI.

One of the more important topics for the techies to consider is the ethics of AGI and ASI. While it might be redundant in the latter case, the dependable regulation and ethical treatment of general AI is important for many reasons.

Society as a whole needs to catch up with the idea of computers being able to think and put laws in place to ensure the ethical treatment of such machines. Owing to the disruptive nature of general AI, the consequences must be foreseen to avoid several issues in the future.

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