Artificial Intelligence definition: How it works, advantages and types

Artificial Intelligence (AI) is the technology that allows machines to mimic human behaviour and intelligence. These machines can learn from experiences, adapt to new inputs‌ and perform human-like tasks. AI ranges from simple programmes that can play chess to complex systems that can understand human language, drive cars and make decisions.

AI is increasingly being integrated into various industries where it's improving efficiency and creating new opportunities for innovation.

AI works by processing large amounts of training data sets to identify patterns and relationships within the data. These patterns enable AI to predict future events or behaviours.

For instance, an AI-powered chatbot becomes more adept at mimicking human conversation by studying numerous text interactions, while an image recognition system learns to recognise and label objects after examining countless images.

The development of AI systems involves several key steps:

• Learning - the AI system collects data and employs algorithms‌ ‌to convert it into useful insights. These algorithms guide the AI in performing tasks methodically.

• Reasoning - this step involves choosing the most effective algorithm to achieve a specific goal and making sure that the AI's actions are both logical and purpose-driven.

• Self-correction - the AI system continually refines its algorithms based on new data, thereby improving its precision and functionality over time.

• Creativity - AI uses methods like neural networks and statistical models to generate unique content such as images, text‌ and music, demonstrating its capability for creativity.

There are many benefits of using AI. Let's go through some of them 👇

1 - Increased precision

AI helps to identify subtle patterns and relationships within data, which might be missed by humans.

In healthcare, for instance, AI systems have detected early-stage cancers. According to research, a deep learning algorithm was able to classify 5 different forms of cancer, including prostate and colon, breast, kidney and lung cancer. This enables early intervention and better patient outcomes.

2 - Efficiency in handling large datasets

AI significantly improves efficiency in industries that manage lots of data, such as finance, insurance‌ and healthcare.

AI automates routine data entry and complex data analysis, speeding up processes and aiding decision-making. For example, in the financial sector, AI models can swiftly analyse vast amounts of data to predict market trends and assess investment risks.

3 - Productivity enhancement

In sectors like manufacturing, AI and robotics not only automate operations but also improve safety. Robots, equipped with AI, perform dangerous or repetitive tasks, minimising human risk and boosting productivity.

This automation extends to various aspects of warehouse operations, streamlining processes and reducing human fatigue.

4 - Consistency in operations

AI-driven analytics tools process large volumes of data consistently while adapting to new information through continuous learning. This is particularly valuable in fields requiring high reliability and precision, such as legal document review and language translation, ensuring uniformity and accuracy in results.

5 - Process optimisation

AI significantly enhances process optimisation across industries by automating and streamlining complex operations.

In the energy sector, for instance, it forecasts demand and optimises supply, enhancing grid efficiency and integrating renewable sources effectively.

This not only cuts expenses but also promotes sustainable practices, making operations more environmentally friendly and economically viable.

Artificial Intelligence (AI) can be categorised into 4 distinct types, each representing a different level of capability and complexity. Here's a detailed look at each type 👇

1 - Reactive machines

Reactive machines are the most basic type of AI systems. They operate based on pre-programmed rules and don't have the ability to form memories or use past experiences to influence current decisions. These machines react to a set of inputs with a specific output.

A classic example is IBM's Deep Blue, the chess-playing AI that defeated Garry Kasparov in the 1990s. Deep Blue could identify the pieces on a chessboard and make predictions solely based on the current game, without any memory of past games.

2 - Limited memory

Limited memory AI systems can look into the past. They use historical data to inform decisions, though this memory is transient. These systems are more advanced than reactive machines because they adjust their actions by looking at past experiences or data.

Most present-day AI applications fall into this category, such as self-driving cars. These vehicles use AI to observe other cars' speed and direction, which helps them navigate safely.

Similarly, chatbots and virtual assistants use data from previous interactions to make more contextually relevant responses.

3 - Theory of mind

Theory of mind AI represents a future stage of AI development where machines will be able to better understand the emotions, beliefs, thoughts‌ and expectations of the humans they are interacting with, and can adjust their behaviour accordingly.

This type of AI requires not only understanding information about the world but also how that information affects the mental states of others. While fully functional Theory of Mind AI has not yet been developed, research in this area is ongoing and aims to make AI interactions more natural and effective.

4 - Self-awareness

Self-aware AI is the most advanced type of AI, which researchers are still far from achieving. These systems would have their own consciousness, sentiments‌ and self-awareness.

Self-aware AI would understand its own state, could infer the feelings of others‌ and make decisions based on its self-driven experiences and emotions.

Today, AI has revolutionised many sectors and has found use cases in industries like software development, healthcare, education and finance. Let's take a look at how it's being used in various fields 👇

1 - AI in software development

In software development, AI enhances coding efficiency and accuracy. AI tools can help programmers by suggesting code corrections, detecting bugs‌ and automating routine tasks.

Machine Learning models are used to understand coding patterns and suggest optimisations, reducing the time developers spend on debugging and maintenance.

AI also powers intelligent programming assistants that help in writing code, thereby speeding up the development process and helping developers focus on more complex problems.

2 - AI in healthcare

AI in healthcare is transforming patient care and administrative processes. AI algorithms can analyse complex medical data faster than human providers, helping diagnoses and treatments.

For example, AI-driven imaging tools can detect abnormalities in X-rays and MRIs with high accuracy. AI is also used in predictive analytics to forecast patient outcomes, personalise treatment plans‌ and manage chronic diseases by tracking patient data in real-time.

3 - AI in education

AI is reshaping education through personalised learning and automation. AI systems can adapt educational content to fit the learning pace and style of individual students, enhancing engagement and effectiveness.

AI tutors provide additional support by offering explanations, resources‌ and feedback. AI also facilitates language learning by offering real-time translations and pronunciation assistance.

4 - AI in finance

AI plays a crucial role in the finance sector by enhancing decision-making and operational efficiency. AI algorithms are used for algorithmic trading, where they analyse market data to make fast trading decisions.

In risk management, AI assesses the creditworthiness of clients more accurately and detects fraudulent activities through pattern recognition. AI also improves customer service in finance through chatbots that handle inquiries and transactions, providing a personalised banking experience.