Understanding Artificial Intelligence

What is Artificial Intelligence

Artificial Intelligence or AI for short is the attempt to mimic human like behaviour, learning and reasoning through code in a computer. The most common types use something called a Deep Neural Network. This is a machine learning algorithm that uses some relatively simple maths (basic calculus) to be able to search for patterns in data. 

What is the difference between AI and Machine Learning?

Although we use the term AI a lot especially in the media, it can sound daunting, mysterious and even dangerous. Yet it is far more accessible than you realise and behind it is a computer that is running a programme that you could write with very little experience or even knowledge. The term Artificial intelligence (AI) is the broader concept of creating intelligent machines that can perform tasks typically requiring human intelligence. Machine learning is a subset of AI that focuses on teaching computers to learn from data and improve their performance over time. In essence, AI is the overarching goal of building intelligent machines, while machine learning is a specific method used to achieve that goal.

What is an Algorithm?

An algorithm is a set of step-by-step instructions or rules to be followed to solve a specific problem or achieve a particular outcome. Think of it as a recipe for a computer: it outlines the exact steps a computer needs to take to complete a task.

How does AI learn?

AI learns through machine learning, which involves training algorithms on data to identify patterns and make predictions. There are three main types: supervised learning, unsupervised learning, and reinforcement learning. By processing data, identifying patterns, and adjusting algorithms, AI can improve its performance over time.   

– Supervised Learning

Supervised learning is a machine learning technique where algorithms are trained on a dataset with labeled inputs and outputs. Think of it like teaching a child to identify animals by showing them pictures of dogs, cats, and birds along with their respective labels. The algorithm learns to recognise patterns and relationships between the input data and the corresponding output, enabling it to make predictions on new, unseen data. 

– Unsupervised Learning 

Unsupervised learning is another machine learning technique, but unlike supervised learning, it doesn’t require labeled data. Instead, the algorithm is tasked with finding patterns, structures, or relationships within the data itself. Imagine giving a child a box of toys and asking them to sort them into groups based on similarities. This is essentially what unsupervised learning does. 

– Reinforcement Learning 

Reinforcement learning is a type of machine learning where an agent learns by interacting with an environment. It’s like training a dog with treats: the agent receives rewards for performing desired actions and penalties for undesired ones. This feedback helps the agent learn the best strategy to maximise its rewards over time. 

How does AI work?

At the heart (or the brain) of an AI model is something called a neural network is a computational model inspired by the structure and function of the human brain. It is made up of interconnected nodes, called neurons, that process and transmit information. These neurons are organised in layers: an input layer, one or more hidden layers, and an output layer.