Types of Artificial Intelligence: Based on Capabilities, Functionality & Technologies

Artificial intelligence is defined as machine technologies that enable computational systems to think and learn to draw human-like conclusions. It operates throughout the healthcare sector, including finance, customer service, and more. The different types of AI functioning today depend on their capabilities and operational features. If you want to learn how many types of artificial intelligence are there, this article explains what are the types of artificial intelligence and their real-world applications.

Who is the Father of AI?

In 1955, John McCarthy coined the term “Artificial intelligence. ” He is considered the father of AI. McCarthy continued to help fund the development of the LISP programming language, which led to modern AI research. Artificial intelligence is a technology that makes machines act and think like humans and learn from huge amounts of data. When administered to these systems, they learn from past experiences and can perform tasks generally needed for human intelligence. Items processed via AI are likely to be completed faster, results more accurately, and tasks more efficiently. AI can make independent decisions using advanced machine and deep learning techniques.

Also Read: Top Advantages and Disadvantages of AI

AI is classified into three broad categories:

1. AI Type-1: Based on Capabilities
2. AI Type-2: Based on Functionality
3. AI Type-3: Based on Technologies

{Additional changes: Add a third section- Type-3 with sub-parts: Machine Learning, Deep Learning, Natural Language Processing, Robotics, Computer Vision, Expert Systems}

Artificial Intelligence Type-1: Based on Capabilities

This classification focuses on how advanced an AI system is and how much it can learn or evolve.

Narrow AI (Weak AI)

The narrow type of artificial intelligence is designed to perform certain tasks within a very small scope. Unlike human intelligence, it has no capacity for self-awareness, reason, or adaptation beyond its programmed functions. It is programmed with an algorithm that learns patterns from data and efficiently executes tasks, but it can not work outside the desired field.

Examples:

• Virtual assistants like Siri and Alexa
• Chatbots used for customer support
• Face recognition technology in smartphones

General AI (Strong AI)

General Artificial or Artificial General Intelligence (AGI) is a machine with anthropomorphic cognitive capabilities. AGI is not the same as narrow AI, which is task-dependent. Unlike AGI, AGI can learn, understand and apply knowledge in various spaces without human intervention. A human brain would adapt to new challenges, problem-solving, creativity, and reasoning like it would be capable of. Although AGI is theoretical, it hasn’t been developed yet.

Possible applications in the future:

• Fully autonomous robots
• AI-powered doctors diagnosing and treating patients
• AI systems replacing human intelligence in decision-making

Super AI (Artificial Superintelligence)

Super AI (or Artificial Superintelligence, ASI) is a hypothetical point where machines exceed our intelligence, not only in one or two areas we use for determining intelligence but also in creativity, emotional intelligence and decision-making. Super AI does not mimic human cognition; it would beat human intellect and its ability to solve incomprehensible problems. Research into AI safety and ethics tries to foresee the potential pitfalls and possible benefits of creating such technology while they are still an item of science fiction.

Potential capabilities of Super AI:

• Advanced scientific discoveries
• Solving complex world problems like climate change
• Automating industries with high-level decision-making

Artificial Intelligence Type-2: Based on Functionality

This classification focuses on how AI systems process and react to information.

Reactive Machines

The simplest kind of artificial intelligence is Reactive AI. It is strictly based on real-time inputs with predefined rules and patterns to produce definite outputs. Unlike other more advanced AI systems, it doesn’t have memory or learning capability, as no previous experience will allow it to improve its results. It, however, works based on immediate data, giving it a walk of repetition, but it can not easily adapt.

Examples:

• IBM’s Deep Blue, which defeated chess grandmaster Garry Kasparov
• Spam filters that block unwanted emails
• AI-powered product recommendations on e-commerce websites

Limited Memory AI

Reactive machines are better than limited memory AI because they can learn from past data for an even shorter period to make better decisions. It learns from experience by analysing historical information with real-time input to improve its performance. Modern applications that use this type of AI deal with pattern recognition and predictive analysis.

Examples:

• Self-driving cars that analyse road conditions and traffic
• Chatbots that learn from conversations to provide better responses
• Fraud detection systems in banking

Theory of Mind AI

Research in the Theory of Mind AI is a new field that explores creating AI systems to understand human emotions, beliefs, and social interactions. While existing AIs respond using only logic and prewritten rules, this AI should be able to interpret human intent and alter its behaviour in response. Such interactive and emotionally intelligent AI systems may be possible if developed successfully.

Future applications:

• AI-powered therapists and counsellors
• AI-driven customer service that understands human emotions
• Social robots that interact like humans

Self-Aware AI

Self-aware AI represents the most advanced and currently hypothetical form of artificial intelligence. It refers to AI that possesses consciousness, emotions, and an independent sense of self. Unlike Theory of Mind AI, which focuses on understanding human emotions, Self-Aware AI would have its awareness, making decisions based on self-driven reasoning rather than external programming. It exists only as a theoretical concept in science fiction and academic discussions.

Potential applications (if developed):

• Machines making ethical and moral decisions
• AI creates its improvements without human help
• Fully independent robotic systems

Artificial Intelligence Type-3: Based on Technologies

This classification focuses on the technologies that enable AI systems to function and learn. These technologies form the foundation of modern AI applications.

Machine Learning (ML)

AI is a science that simulates human and other animals’ thought processes and actions in robots. Machine Learning is a part of AI that allows systems to learn from data and improve over time without explicit programming. It works on data, finds patterns, predicts what could happen, and corrects its outputs by feedback. Nevertheless, there are plenty of applications across a variety of industries that use ML models to automate decision-making and optimise processes.

Examples:

• Personalised content recommendations on streaming platforms
• Fraud detection in banking and finance
• Spam detection in emails

Deep Learning

Machine Learning is a harder field that feeds Artificial Intelligence; deep learning is a subset of Machine Learning that uses Artificial Neural Networks with multiple layers (deep neural networks) to process and analyse huge volumes of Data. Deep learning models, like the brain’s structure, can distinguish intricate patterns, relate to complex relationships, and develop different predictions.

Examples:

• Voice assistants like Siri and Google Assistant
• Image recognition in medical diagnosis
• Autonomous driving technology in self-driving cars

Natural Language Processing (NLP)

Natural language processing, or NLP, enables AI to understand, interpret, and respond to human language. In theory and practice, it combines the specifics of computational techniques and linguistics to close the gap between humans and machines. Some of the uses of NLP are text analysis, translation, speech recognition, and sentiment analysis.

Examples:

• Chatbots and virtual assistants
• Language translation apps like Google Translate
• Sentiment analysis in social media monitoring

Robotics

AI is integrated with mechanical systems, which are known as robotics. In repetitive or poisonous environments, AI-driven robots are employed in different industries to increase productivity, efficiency, and human labour.

Examples:

• Industrial robots in manufacturing
• AI-powered robotic vacuum cleaners
• Humanoid robots for customer service

Computer Vision

Computer vision is a part of Reinforcement Learning That allows AI to interpret and analyse visual data such as images and videos. It imitates human vision by detecting patterns, recognising objects, and extracting meaningful information from the visual input. This technology is used in security, healthcare, retail, and autonomous systems.

Examples:

• Facial recognition in security systems
• Medical imaging analysis for detecting diseases
• AI-powered object detection in surveillance

Expert Systems

AI-based programs that can be implemented to replicate human expertise in a certain field are known as Expert Systems. They provide intelligent decision support through rule-based logic and knowledge databases. This is commonly used in fields that demand critical analysis, medicine, law and finance.

Examples:

• AI-driven legal advisors for contract analysis
• Medical diagnosis systems assisting doctors
• Automated financial advisors for investment planning

Branches of AI

Artificial Intelligence can be divided into multiple branches, some of which study specific aspects of machine intelligence. These branches perform the tasks AI systems can learn, reason about, perceive, and make decisions about.

Machine Learning (ML)

Machine Learning enables AI systems to process information and search for patterns, resulting in increased operational results without the need for human code writing.

Deep Learning

Some of these capabilities use deep learning, a subset of ML and an important method of understanding, processing, or analysing complex data, including image and speech recognition.

Natural Language Processing

With NLP, AI understands and interprets what humans say and generates words. This is crucial for virtual assistants, chatbots, and language translation.

Computer Vision

AI, with its aids, Computer Vision, interprets visual information from images and videos, such as facial recognition and object detection.

Robotics

By combining AI with robotic systems, robotics allows machines to perform autonomous tasks to improve the performance of industrial automation and provide healthcare assistance.

Expert Systems

An expert system emulates a human decision-making process with pre-grouped rules and knowledge bases to solve problems in several fields.

Fuzzy Logic

In control systems and decision-making cases, AI can benefit from handling uncertainty and approximate reasoning; this is where Fuzzy Logic comes in.

Evolutionary Computation

Algorithms inspired by natural selection for optimisation are used in exercise computation to solve problems and improve AI performance over time.

Conclusion

Artificial intelligence has grown greatly, and its use has spread to many industries. Understanding what are the different types of artificial intelligence helps us understand how AI works. While people speak of narrow AI today, national AI and super AI are being researched. The article describes AI, including its power, function, and DSL real-world examples. The harder AI evolves, the stronger its impact on society will be. Enrol in the Certificate Program in Artificial Intelligence and Machine Learning powered by Hero Vired for more insight into Artificial Intelligence and gain professional certification.