In modern culture, preconceived notions about artificial intelligence (AI) run rampant. Without a true understanding of what AI is and how it impacts both businesses and life in general, it’s easy to form incorrect conclusions about the different types of AI.
But the power of AI is unfolding at an unstoppable rate. In fact, the projected annual growth rate for artificial intelligence is estimated to be 33.2% in the next 7 years alone. The only potential limitations to further growth would be due to a lack of trained staff.
To help more business leaders and staff members leverage the true power of AI, this post will provide an overview of the different types of AI and how they occur in modern life.
Artificial intelligence basics
Artificial intelligence (AI) is a specialized field of computer science; it focuses on producing machines that mimic the natural intelligence that is typically displayed by humans and animals. But AI isn’t just for computer engineers or programming nerds; AI has multiple applications for human life, social activity, workplace interactions, and business needs including online shopping, automating tedious daily tasks, and preventing future disease outbreaks.
There different types of AI?
AI technologies are classified by how readily they mimic human intelligence or natural functions. Therefore, there are a few different types of AI categorized by how evolved their “cognition processes” are. The capability of an AI system usually increases, or builds upon itself, with each new type.
How many different types of AI exist?
Most sources recognize four types of artificial intelligence, ranging from those that are fully developed to those that are still in the ideation phase. The four types of AI include:
Reactive mechanics
Limited memory
Theory of mind
Self-awareness
1. Reactive mechanics
Reactive mechanics is the most basic type of artificial intelligence. These machines and systems only perform simple, primary operations. There is no dynamic learning process in a reactive mechanics AI system, meaning that the machine does not continually add or learn new data. Once it’s set with its task, that’s the only task it’ll do until given a new one.
In reactive mechanics, predefined output guides the machine’s actions. Any accomplishment is purely reactive and usually fulfills a singular purpose or end goal. A reactive mechanic model does not include memory or storage capabilities for new information.
Examples of reactive mechanics AI
The most famous examples of reactive mechanics artificial intelligence include:
IBM’s “Deep Blue,” which is a computer that successfully competed against and beat world-famous chess player, Gary Kasparov
Google’s AlphaGo, which is a computer program that plays the Chinese game of Go and has beaten several Go experts throughout its existence
Advantages to reactive mechanics
Many artificial intelligence experts and researchers appreciate the distinct simplicity of reactive mechanics. Because this type of AI can’t build upon its pre-loaded content, there is no world in which a reactive mechanics system “goes rogue.” For many individuals who are worried about the unknown dangers of AI, this is a selling point.
Additionally, the simplicity of reactive mechanics makes it easy to develop, use, and maintain.
Disadvantages to reactive mechanics
For researchers and developers who want to push the boundaries on AI, reactive mechanics is far too limited. Although its purpose can make tasks easier, it’s not the most innovative approach to using artificial intelligence in modern life.
2. Limited memory
Limited memory AI is one step up from reactive mechanics, although it contains all the capabilities of the reactive mechanical setup. In this type of artificial intelligence, the limited memory machine or system stores data and takes memory-based actions.
Because this type of AI is capable of remembering previous reactions, it is better suited to make future decisions. Many of the recognizable AI systems in place today fall into the limited memory category.
To get even more granular, some AI experts categorize limited memory AI in three categories:
Reinforcement learning
Long short term memory (LSTM)
Evolutionary generative adversarial networks (E-GAN)
Examples of limited memory AI
Modern products and services such as online chatbots, self-driving vehicles, and predictive personal assistants (like Amazon’s Alexa) are built using limited memory AI. These services use pre-built features and remember past responses. Then, they use those responses to guide future actions, recommendations, or decisions.
Advantages to limited memory
The benefit of limited memory AI is that it has numerous practical functionalities. It is simple enough to use that most average technology users are capable of handling it without difficulty.
The predictive nature is also helpful for businesses looking to automate processes critical to their day-to-day operations, such as lead generation or customer support.
Disadvantages to limited memory
Although limited memory AI is powerful, it can also be fallible. Like most technology, it is subject to errors, misunderstanding, and incorrect responses. It is important to still be mindful, alert, and ready to troubleshoot as needed.
3. Theory of mind
The first two types of artificial intelligence already exist and are in regular use. Theory of mind, however, is a type of AI that still requires development.
In most instances, theory of mind is unique to human behavior and emotion. Developers have not yet been able to build a machine that perfectly captures the human response to internal thoughts, feelings, and emotional needs. This level of emotional intelligence goes beyond the simple task of reactivating to stimuli or inputs.
Examples of theory of mind AI
Because this type of AI system doesn’t yet exist, there are currently no physical examples. A hypothetical example would be a robot or other type of smart machine that can sense, perceive, and respond to thoughts and feelings.
Advantages to theory of mind
One advantage to theory of mind AI is that it opens up an entirely new world for AI researchers. Artificial emotional intelligence is an area of untapped potential, and trailblazers are eager to design innovative machines that can process human thought at a deeper level.
Disadvantages to theory of mind
Although most people are comfortable using AI to perform basic tasks, building machines with the ability to read human thoughts and feelings is a matter of debate. In order to succeed, there would need to be regular interaction and exposure between human beings and robotic systems.
Additionally, some humans may not openly welcome artificial emotional intelligence. Imagine if Alexa, instead of offering a pre-programmed response to a search query, corrected a user on the basis of that user’s intrinsic emotional wellbeing. This level of awareness would expose human thoughts, feelings, and internal needs at a deeper level.
4. Self-Awareness
The final type, self-aware AI, is the pinnacle of artificial intelligence technology. This type of system would be capable of performing all operations independently and of having conscious thought processes. Instead of simply performing tasks, it would be able to know why it was performing those tasks.
Self-aware AI is often represented in movies and in science fiction stories. While there is not a significant desire or need to build this type of AI, researchers may rely on it to learn more about memory, cognition, and mechanical independence.
Examples of self-aware AI
Most examples of self-aware technology are found in entertainment sources. Consider Ultron, the robotic character in Marvel Comics’ Avengers: Age of Ultron film.
In the movie, Ultron breaks free of his original framework, understands and anticipates human responses, and begins to make his own decisions and plans. Although he is self-aware, Ultron’s ultimate goals are in direct contrast to human safety and stability as he hopes to exert his own authority.
Advantages to self-awareness
The most prominent objective of self-aware AI is to push artificial intelligence to its greatest capabilities. Other goals could be reliant on the person(s) creating the technology, and brings in the conversation of AI ethics into play. Achieving this type of design would mean that AI has essentially reached or surpassed human capability. While some may believe this is an advantageous goal, others have reservations.
Disadvantages to self-awareness
Self-aware AI is often the kind depicted in “doomsday” scenarios, which creates a lot of unnecessary fear around the topic of innovating AI. Despite the idealistic media representations that have existed for decades, some scientists believe that self-aware AI is decades, if not centuries, away from fruition.
Which type of AI is right for your business?
Despite fears that AI will eventually surpass human responsibility in the workplace, the truth is that companies must adapt to using AI at some level in order to scale and meet demand. Currently, AI is expected to contribute more than $15 trillion to the global economy in the next decade, underscoring the need for implementation and application.
No matter which type of artificial intelligence an organization leverages, there are several suitable ways to harness its power and potential.
Bolt-on – Several AI applications in use today are bolt-on systems. This means that they plug onto existing business systems, such as a CRM, to improve efficiency. These solutions are categorized as SaaS, since they add functionality to other services.
Enhanced process – The enhanced process model boosts workflows across an organization. This type of AI implementation can be helpful when analyzing large quantities of data or improving the traditional sales cycle.
Stand alone – Applying a stand alone AI system requires very little human input to accomplish an automated task. An example is the use of drones to deliver packages and customer orders. The stand-alone system offers a seamless way to accomplish routine tasks.
Types of AI vs. Stages of AI
One critical aspect of artificial intelligence is the concept of evolution. The stage of an AI system should not be confused with the type of AI. Stage refers to the AI’s ability to evolve, add new data, and produce different results throughout its existence.
The three different stages of artificial intelligence are:
1. Artificial Narrow Intelligence (ANI) – This stage, also known as “weak AI” refers to machines that can only perform one simplified task at a given time. There is no native thinking ability or the opportunity for independent behavior. Every function that happens in the ANI stage is predetermined.
2. Artificial General Intelligence (AGI) – This stage, commonly referred to as “strong AI,” includes the ability to think independently. The decision-making component of this stage is characteristic of humans. As such, there is no current example of AGI apart from natural human behavior.
3. Artificial Super Intelligence (ASI) – ASI is the stage that occurs when artificial intelligence moves beyond human capability. Most of the time, ASI is only referenced in fiction. There are many different opinions on whether this stage is possible, preferable, or safe.
How to leverage the power of artificial intelligence
As the possibilities of AI continue to expand with research and monetary investment, it’s critical that businesses develop an appropriate and forward-thinking AI strategy. Whether that involves choosing the most suitable type of AI for a specific end goal, or devoting time and resources into new developments, the way of the future is already here.
Grow your business, reach your goals, and drive innovation forward by leveraging the untapped potential of each type of AI. In doing so, you can automate core operations, save valuable time, and become more versatile in the midst of a constantly evolving digital marketplace.