What is Artificial intelligence (AI)? Its types and uses.

Building intelligent machines that can carry out activities that traditionally require human intellect is the focus of the vast field of artificial intelligence (AI), a subfield of computer science. Despite the fact that AI is a diverse area with many viewpoints, developments in machine learning and deep learning in particular are profoundly changing practically every business, particularly the technological one.

Machines can mimic or outperform human cognitive skills thanks to artificial intelligence. AI is quickly integrating itself into daily life, from the creation of self-driving cars to the emergence of generative AI tools like ChatGPT and Google’s BERT. Businesses from many sectors are investing in this area.

Definition of artificial intelligence

AI Simply said, artificial intelligence (AI) systems are capable of performing cognitive tasks including voice interpretation, game play, and pattern recognition. They get the necessary skills by analysing a lot of data and looking for patterns in their models to come to their own conclusions. Humans will frequently oversee the AI learning process, rewarding wise choices and discouraging poor ones. Some artificial intelligence (AI) systems, however, are created to learn on their own, for instance by playing a video game repeatedly until they ultimately figure out the rules and winning tactics.

Weak AI vs. Strong AI Since intelligence is hard to define, AI specialists frequently distinguish between strong AI and weak AI.

Powerful AI Strong AI, commonly referred to as artificial general intelligence (AGI), is the ability of robots to solve issues without having received any specialised training, much like a person. These examples of artificial intelligence (AI) include the robots in Westworld and Data from Star Trek: The Next Generation. Such AI does not, however, exist at the moment.

The goal of many AI researchers is to create a computer that can do a variety of jobs with human-level intelligence, but finding artificial general intelligence has proven to be difficult. There has been little study on strong AI because some people think that developing a powerful AI without adequate safeguards may be dangerous.

However, attaining this feat has not been simple. Weak AI, on the other hand, refers to robots that can simulate a portion of human cognitive skills and has a wide variety of applications.

Poor AI Weak AI, also known as narrow AI or specialised AI, operates in certain situations and imitates human intellect in order to solve a given task (such as operating a vehicle, trancribing human speech, or selecting material for a website, for example).

Weak AI systems frequently excel at just one job very well. While they could seem intellectual, they really have more restrictions and limits than a normal human being.

Examples of weak AI include:

  • Siri, Alexa, and other smart assistants
  • Self-driving cars
  • Google search
  • Conversational bots
  • Email spam filters
  • Netflix recommendations

Examples of artificial intelligence

Artificial Intelligence (AI) can be categorized into four types based on the nature of tasks per


formed and complexity involved. They are:

    1. Reactive Machines
    2. Limited Memory
    3. Theory of Mind
    4. Self-Awareness


Reactive Machines

A reactive machine is one that adheres to the core AI principles and, as the name implies, is solely capable of sensing and responding to its immediate surroundings. Because reactive machines lack memory, they are unable to use knowledge from the past to guide their current actions.

Understanding the environment directly implies that reactive robots are created with a restricted set of specified jobs in mind. Although there are advantages to restricting the scope of a reactive machine, an AI will be more dependable and predictable, responding consistently to the same inputs.

Reactive Machines Example In a game of chess, Deep Blue, a supercomputer created by IBM in the 1990s, defeated Garry Kasparov, an international grandmaster. Deep Blue was capable of identifying the chess pieces on the board, comprehending the game’s rules to ascertain each piece’s potential movements, and selecting the move that made the most sense at the time. The computer was not analyzing its opponent’s future moves or strategizing to keep its pieces in a better position. Each move was evaluated in the context of its current reality, separate from any preconceived notions.

On the other hand, Google’s AlphaGo is unable to estimate potential moves for the future, instead relying on its own neural network to determine the significance of recent game events. This elevates it over Deep Blue in difficult games. In 2016, AlphaGo outperformed top-tier challengers in the game of Go by defeating champion player Lee Sedol.

Limited Memory

Memory is scarce. AI has the capacity to learn from data collection and evaluate possible outcomes by looking at the past—basically, by searching the past for hints about prospective futures. Reactive machines have limited memory, which makes limited memory AI more complicated and open to more options.

Limited memory When a group continuously applies a model to fresh data or develops an environment for AI where the model can be trained and updated automatically, AI is formed.

When using limited memory AI in machine learning, the following steps are typically followed:

  1. Establish training data
  2. Build a machine learning model
  3. Ensure the model can make predictions
  4. Ensure the model can receive human or environmental feedback
  5. Store human and environmental feedback as data
  6. Iterate through the above steps as a cycle

Theory of Mind

The theory of mind is only an idea at this point since we lack the technical and scientific capacity to advance AI to this level.

According to psychological theory, other living things have feelings and ideas that affect how they behave. This would imply that AI systems are capable of comprehending how presence and emotional state are perceived by people, animals, and other robots. It would allow AI to make judgements based on introspection and resolve and use this knowledge for autonomous decision-making. Machines must be able to understand the “mind” idea, the fluctuation of emotions, and other psychological conceptions in order to comprehend and synthesise emotional concepts and other psychological notions in real-time.


Once the idea of the mind has been established, self-awareness will be the pinnacle of AI development. This kind of AI is sentient on a par with humans; it is aware of its own presence in the environment and is aware of the presence and emotional condition of others. Not only based on how they communicate, but also on how they convey it, it will be able to comprehend what other people require. Researchers who comprehend the origins of consciousness and then recreate it in computers are necessary for the development of self-aware AI.



Benefits, Difficulties, and Future of AI

The use of artificial intelligence (AI) has several advantages, from speeding up the development of vaccines to automatically spotting possible fraud. CB Insights found that AI businesses raised $66.8 billion in investment in 2022, more than twice as much as they did in 2020. AI is making ripples across several industries as a result of its quick adoption.

Discreet Banking Over half of financial services businesses now utilise AI solutions for risk management and revenue production, according to a research on AI in business insider intelligence. Applications of AI in banking might save more than $400 billion.

Better Healthcare According to a 2021 World Health Organisation research, incorporating AI in the healthcare industry has inherent difficulties. However, it holds up the possibility of substantial advantages, such as enhanced health care programmes and more precise patient diagnostics.

Cutting-edge Media AI has also had its effect on the entertainment sector. Grand View Research predicts that the worldwide AI industry for media and entertainment would grow from its $10.87 billion worth in 2021 to up to $99.48 billion by 2030. AI is being used to create high-definition visuals and detect literary theft, among other things.


AI Challenges and Limitations

Although AI is thought of as a significant and quickly developing asset, it also has several drawbacks. According to a Pew Research Centre study from 2021, 37% of respondents are more concerned than delighted about AI, while 45% of respondents are both excited and concerned. Additionally, more than 40% of respondents believe that autonomous automobiles would be bad for society. However, most respondents (around 40%) thought it was a good idea to employ AI to detect the spread of false information on social media.

The potential of AI Given the associated computing expenses and technological infrastructure, implementing AI is a challenging and expensive procedure. The good news is that computing technology has advanced significantly, as demonstrated by Moore’s Law, which claims that the cost and number of transistors on a microchip half every two years. Some scientists, however, predict that Moore’s Law may stop in the 2020s.

However, it has had a significant influence on contemporary AI technology. According to recent studies, AI advancements are actually outperforming Moore’s Law, doubling their performance every six months as opposed to every two years.

According to this line of reasoning, artificial intelligence has advanced several sectors and is predicted to have an even bigger influence in the ensuing decades, making it all but inevitable.