Demystifying AI: Understanding Artificial Intelligence  (AI)

What is Artificial Intelligence (AI)?

The term Artificial intelligence (AI) describes a system’s or machine’s capacity to carry out operations that require human intelligence. Artificial intelligent systems are capable of carrying out tasks usually associated with cognitive abilities in humans, such as voice interpretation, decision-making, and natural language processing. analyzing trends and playing games.

These artificial intelligence (AI) systems acquire the necessary skills by sifting through huge amounts of data and searching for patterns to use as models for their own judgment. Most of the time, humans will oversee an AI’s learning process, rewarding wise choices and discouraging poor ones. However, some AI systems are built to learn on their own, for instance by repeatedly playing a video game until they master its rules and how to win.

AI involves the development of algorithms and models that can learn from data and improve their performance over time. AI can be categorized into several areas, including machine learning, deep learning, natural language processing, computer vision, robotics, and expert systems. These different areas of AI have different techniques and approaches for enabling machines to perform intelligent tasks.

AI has many applications in various fields, including healthcare, finance, manufacturing, transportation, and entertainment, AI can help healthcare professionals in diagnosing diseases and developing treatment plans, or help financial analysts in analyzing market trends and making investment decisions. AI is also being used to develop autonomous vehicles, improve manufacturing processes, and enhance the entertainment industry with personalized recommendations and content creation.

artificial intelligence

Types of Artificial Intelligence

Based on capabilities

Weak AI or Narrow AI:

Weak AI or Narrow AI refers to AI systems that are designed to perform a specific task or set of tasks within a limited domain and are not capable of general intelligence or learning beyond their programmed task.

Here are some examples of Narrow AI:

  1. Voice Assistants: Voice assistants, such as Amazon’s Alexa, Apple’s Siri, and Google Assistant, are examples of narrow AI. These systems use natural language processing and machine learning algorithms to perform tasks such as setting reminders, making calls, and answering questions.
  2. Recommendation Systems: Recommendation systems, commonly used by e-commerce sites and streaming services, are another example of narrow AI. These systems use machine learning algorithms to analyze user data, such as past purchases or viewing history, to provide personalized recommendations to users.
  3. Spam Filters: Spam filters are a type of narrow AI designed to identify and block unwanted email messages. These systems use machine learning algorithms to analyze the content of incoming messages and determine whether they are likely to be spam or not.
  4. Fraud Detection: Fraud detection systems, used by financial institutions and credit card companies, are another example of narrow AI. These systems use machine learning algorithms to analyze transaction data and identify suspicious activity, such as unusual spending patterns or attempted unauthorized access to accounts.
  5. Autonomous Vehicles: While self-driving cars may seem like a sophisticated AI system, they are actually an example of narrow AI. These vehicles use sensors and machine learning algorithms to analyze their surroundings and make driving decisions but are limited to the task of driving and do not possess general intelligence beyond that specific task

General AI

General AI is also known as Strong AI or Artificial General Intelligence. It refers to AI systems that are capable of performing any intellectual task that a human can do. These systems are designed to exhibit human-like intelligence and learning capabilities. However, developing general AI is still a long way off, and there are no true examples of general AI systems in existence yet.

Currently, most AI systems are designed for narrow or specific tasks within a limited domain, such as voice assistants or recommendation systems. These systems are not capable of general intelligence or learning beyond their programmed task.

However, some researchers and scientists are working on developing general AI systems that can perform a wide range of tasks and learn from their experiences in a way that is similar to human learning.

Super AI

The term “super AI” typically refers to an advanced form of artificial intelligence that surpasses human-level intelligence in various domains, such as problem-solving, decision-making, creativity, and communication.

super AI

Super AI is often associated with the concept of artificial general intelligence (AGI), which refers to AI systems that possess human-like intelligence and can perform a wide range of intellectual tasks across multiple domains.

While the idea of super-AI is exciting, it is also controversial and raises many ethical and existential concerns. Some experts worry that a super-AI could pose a significant risk to human civilization if it becomes uncontrollable or hostile. Therefore, many researchers are working on developing methods for making sure that future AI systems are safe, transparent, and aligned with human values.

Based on functionality

Reactive machines:

Reactive machines are AI systems that operate solely on the current input without any past memory or context. These systems are designed to react to specific situations based on pre-programmed rules, and cannot learn or adapt beyond their programmed responses..Elevator systems, Robotics: Autonomous vehicles Chess-playing computers, and self-driving cars are examples of reactive machines.Robotics, Autonomous vehicles, Chess-playing computersTraffic control systems:

Limited memory

Limited memory AI systems can make decisions based on past experiences and data, but only for a limited amount of time. These systems can learn and improve their performance, but their ability to do so is constrained by the amount of data that can be stored in their memory. Recommendation systems used by online retailers and streaming services are examples of limited memory AI.

Theory of Mind:

AI systems have the ability to understand the beliefs, desires, and intentions of other entities, and can use that information to interact with them more effectively. These systems are still in the research phase and are not yet widely available.


Self-aware AI is a theoretical type of AI that has consciousness and the ability to understand its own existence and the existence of other entities. Presently, it is not possible to create self-aware AI

How does Artificial Intelligence (AI) actually Work?

AI work through a process of data processing, learning, and decision-making, which involves several steps and techniques. Here is an overview of how AI works

  1. Data Collection – The first step in AI is to collect and prepare the data that will be used to train the AI model. The data can come from various sources such as sensors, databases, or user input.
  2. Data Preprocessing – The data is then preprocessed to remove noise, outliers, or errors, and to extract relevant features that can be used to train the AI model.
  3. Training – The AI model is trained on preprocessed data using various techniques such as supervised learning, unsupervised learning, or reinforcement learning. During training, the AI model learns to recognize patterns, generalize from examples, and make predictions or decisions.
  4. Validation and Testing – The trained AI model is then tested on new data to evaluate its performance and accuracy. If the model is not accurate enough, it can be retrained or fine-tuned using different hyperparameters or optimization techniques.
  5. Deployment – Once the AI model is trained and validated, it can be deployed in various applications such as image recognition, speech recognition, natural language processing, or autonomous vehicles.
  6. Feedback Loop – As the AI model is used in real-world applications, it can generate new data that can be used to improve and update the model over time. This feedback loop helps to improve the accuracy, robustness, and adaptability of the AI model.

Real-world examples of Artificial Intelligence (AI)

AI(Artificial intelligence) concept.

Siri, Alexa, and Google Assistant, Google Maps, Ridesharing Apps, Email Categorization, Spam Filtering, Fraud detection Check Deposit through, MobileManufacturing, robots, Self-driving, cars smart assistants, Manufacturing robots, Self-driving cars smart assistants, Healthcare Management, Automated financial investing, Virtual travel booking antisocial media monitoringMarketing chatbots Siri, Apple’s digital assistant, there are lot more.

You can read more here: The History of AI and its evolution over time

Read more: Exploring different types of AI algorithms


Frequently asked questions

What are the different types of AI?
There are three types of AI: narrow or weak AI, general or strong AI, and superintelligence. Narrow AI is designed to perform a specific task, while general AI aims to simulate human intelligence and can perform any intellectual task that a human can. Superintelligence refers to AI that surpasses human intelligence in all domains.
How is AI developed?
AI is developed through a process of designing, training, and testing models using data. The development of AI involves several steps, including data collection, data preprocessing, model selection, training, validation, and deployment.
What is artificial intelligence?
Artificial Intelligence (AI) refers to the ability of machines or computer systems to perform tasks that typically require human intelligence. Artificial intelligent systems can perform tasks commonly associated with human cognitive functions — such as interpreting speech, decision-making, and natural language processing. playing games, and identifying pattern
What are the different approaches to AI?
There are two main approaches to AI: rule-based systems and machine learning. Rule-based systems rely on pre-defined rules and logic to make decisions or solve problems, while machine learning uses statistical algorithms to learn patterns and relationships from data.