Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
Artificial intelligence can be categorized in different ways. From chatbots to super-robots, here are the types of AI to know and where the tech’s headed next.
If you’ve ever used ChatGPT or Amazon’s Alexa, you’ve interacted with one type of artificial intelligence. However, AI is much broader than the common tools gaining mass adoption. Despite the rapid advancements in AI, most of the current technologies in existence can be divided into different types. These classifications reveal more of a storyline than a taxonomy, one that can tell us how far AI has come, where it’s going and what the future holds.
These are the seven main types of AI to know, and what we can expect from the technology.
Based on how they learn and how far they can apply their knowledge, all AI can be broken down into three capability types: narrow AI, artificial general intelligence and artificial superintelligence.
Narrow AI, also known as artificial narrow intelligence (ANI) or weak AI, describes AI tools designed to carry out very specific actions or commands. They are built to serve and excel in one cognitive capability, and cannot independently learn skills beyond their design. All AI systems used today fall under the category of narrow AI.
Narrow AI often utilizes machine learning, natural language processing and neural network algorithms to complete specified tasks. Some examples of narrow AI include self-driving cars and AI virtual assistants.
Artificial general intelligence (AGI), also called general AI or strong AI, refers to a theoretical form of AI that can learn, think and perform a wide range of tasks at a human level. The ultimate goal of AGI is to create machines that are capable of versatile, human-like intelligence, functioning as highly adaptable assistants in everyday life.
Though still a work in progress, the groundwork of artificial general intelligence could be built from technologies such as supercomputers, quantum hardware and generative AI products like ChatGPT.
Artificial superintelligence (ASI), or super AI, is truly the stuff of science fiction. It’s theorized that once AI has reached the general intelligence level, it will soon learn at such a fast rate that its knowledge and capabilities will become stronger than that of even humankind.
ASI would act as the backbone technology of completely self-aware AI and other individualistic robots. Its concept is also what fuels the popular media trope of “AI takeovers.” But at this point, it’s all speculation.
“Artificial superintelligence will become by far the most capable forms of intelligence on earth,” said Dave Rogenmoser, CEO of AI writing company Jasper. “It will have the intelligence of human beings and will be exceedingly better at everything that we do.”
More on AI4 Types of Machine Learning to Know
Functionality is focused on how an AI applies its learning capabilities to process data, respond to stimuli and interact with its environment. As such, AI can be sorted by four functionality types.
Reactive machines are just that — reactionary. They can respond to immediate requests and tasks, but they aren’t capable of storing memory, learning from past experiences or improving their functionality through experiences. Additionally, reactive machines can only respond to a limited combination of inputs. Reactive machines are the most fundamental type of AI.
In practice, reactive machines are useful in basic autonomous functions, such as filtering spam from your email inbox or recommending items based on your shopping history. Beyond that, reactive AI can’t build upon previous knowledge or perform more complex tasks.
Limited memory AI can store past data and use that data to make predictions. This means it actively builds its own limited, short-term knowledge base and performs tasks based on that knowledge.
The core of limited memory AI is deep learning, which imitates the function of neurons in the human brain. This allows a machine to absorb data from experiences and “learn” from them, helping it improve the accuracy of its actions over time.
Today, the limited memory model represents the majority of AI applications. It can be applied in a broad range of scenarios, from smaller-scale applications, such as chatbots, to self-driving cars and other advanced use cases.
Theory of mind in AI refers to the ability to recognize and interpret the emotions of others. The term is borrowed from psychology, describing humans’ ability to read the emotions of others and predict future actions based on that information. Although it has not been achieved yet, theory of mind would be a substantial milestone in AI’s development.
An emotionally intelligent AI could bring a lot of positive changes to the tech world, but it also poses some risks. Since emotional cues are so nuanced, it would take a long time for AI machines to perfect reading them, and could potentially make big errors while in the learning stage. And some worry that an AI capable of responding to both emotional and situational signals could lead to the automation of more jobs.
Self-aware AI refers to the hypothetical stage of artificial intelligence where machines possess self-awareness. Often referred to as the AI point of singularity, self-aware AI represents a stage beyond theory of mind and is one of the ultimate goals in AI development. It’s thought that once self-aware AI is reached, AI machines will be beyond our control, because they’ll not only be able to sense the feelings of others, but will have a sense of self as well.
AI technologies are also developed for specific purposes and use cases. While these types of AI are more application-based, they’re still helpful to know.
While machine learning might seem synonymous with AI, it is actually a subset of the technology. Machine learning focuses on creating algorithms that learn from data and improve over time through continuous learning without being explicitly programmed to do so. Machine learning models can identify patterns and make predictions based on new or unseen data.
NLP is another branch of artificial intelligence and focuses on understanding and generating human language. It is the concept that powers ChatGPT and the various popular generative chatbots in use today. NLP works by breaking down text data into tokens, which can be text, phrases or symbols, and are more manageable to process and analyze.
AI is improving the field of robotics in various ways. AI powers machine vision, giving robots the ability to identify objects and navigate different environments on their own. Robots can also perform repetitive tasks with the help of AI, such as picking produce on farms, transporting packages in warehouses and identifying medical images that reveal disease risk factors in healthcare settings.
The next generation of robots will depend on AI to function, too. Collaborative robots possess a system of sensors and advanced functions that enable them to remain aware of their surroundings and engage with human workers safely. AI is also leading to more general-purpose robots, which can understand verbal commands and learn new tasks independently.
Computer vision gives AI technologies the ability to process visual information and convert it into usable data. This makes it possible for AI software, robots and other machines to detect objects, track moving objects and map out a physical environment, among other applications.
A subset of computer vision known as image recognition offers even more possibilities. Referring to the process of identifying and classifying different elements within an image, image recognition supports many use cases like image-based medical diagnoses, security systems and inventory management.
Expert systems are trained on data sets to solve complex problems using rule-based decision-making processes. AI expert systems are trained either through forward chaining or backward chaining. In forward chaining, a system starts with facts and learns how to make inferences to gain more information until a goal is achieved. In backward chaining, the system starts with the goal and works backward to determine the facts used to reach the goal.
Neurosymbolic AI combines neural networks with symbolic AI to develop a system capable of pattern recognition, reasoning and knowledge representation. This hybrid AI model can use logic and symbols to solve complex tasks instead of just statistical data from neural networks.
One key advantage is that it can utilize both GPUs and CPUs. Because of its enhanced understanding, Neurosymbolic AI supports autonomous vehicles, robotics and knowledge-based systems like drug discovery.
The 7 types of artificial intelligence (AI) include:
Narrow AI and limited memory AI are the most common types of AI used today.
ChatGPT is a form of narrow intelligence. It is only capable of specific tasks (like text, image and code generation), and does not have the ability to generalize or adapt beyond its training data. But, eventually, ChatGPT could contribute to the creation of artificial general intelligence — AI that is capable of performing a wider range of actions typically done by humans and possesses human-level intelligence.
Matthew Urwin contributed reporting to this story.
