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Artificial Intelligence (AI) encompasses a range of technologies that enable machines to perform tasks requiring human intelligence, such as learning, reasoning, and adapting. This infographic unravels the core concepts of AI, including machine learning, supervised and unsupervised learning, reinforcement learning, and deep learning.
Artificial Intelligence (AI) is a rapidly advancing field with no universally accepted definition. Different scholars have tried to define AI by linking it with human intelligence and cognitive abilities, from machines mimicking human behaviour to systems making autonomous decisions. In general, AI refers to the development of systems capable of performing tasks that would typically require human intelligence. These tasks include learning from data, recognizing patterns, making decisions, and adapting to new situations (Thornberry, 2021; McCarthy, 2007).
While there is no single accepted definition of AI, there are certain common features that are evident across the various descriptions of AI.
While there is no single accepted definition of AI, there are certain common features that are evident across the various descriptions of AI.
AI has been hailed as a technology of the future and a pinnacle of human technological advancement. Several research studies have elaborated on the scope of AI in almost every aspect of human life. These include robotics, healthcare, finance, space, social media, education, surveillance and e-commerce. The practicality of AI demonstrates that it is a dynamic tool which enhances performance, and efficiency and encourages innovation across industries.
Here are some of the applications of AI in various industries.
Machine learning (ML) is a core component of artificial intelligence (AI). It enables computers to learn from data and experiences in a manner similar to how humans learn. ML systems create models that can be enhanced, identify patterns, and resolve issues. By utilising historical data, they are able to address new challenges (Celik, 2018).
ML systems analyse historical data in order to identify patterns and make decisions
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These systems are designed to continuously improve their performance as they process more data.
ML is capable of solving complex problems that are difficult to address with traditional programming, such as recognising speech or images
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ML systems can adapt to new information and changing environments.
ML is used by businesses to forecast trends, manage risks, and improve efficiency.
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Examples include virtual assistants, recommendation systems, and fraud detection.
Supervised learning is the foundational approach in machine learning, where a machine learns to map an input variable to an output variable based on labelled data pairs. This method is divided into two main categories: classification and regression.
Classification categorizes data, such as spam detection in emails (Han, Pei & Tong, 2022). Regression, on the other hand, predicts continuous values based on existing data, such as the prediction of property prices (Sarker, 2021). Supervised learning requires structured data, as unstructured data can significantly reduce efficiency and accuracy (Kotsiantis, 2007). The process involves training the machine with a training set, which contains known input-output pairs, and then validating its performance on a test set, which evaluates the machine's ability to generalize to new, unseen data.
Structured data is highly organised and easily searchable, typically stored in relational databases and spreadsheets, with a fixed format of rows and columns. Financial records and sensor data are two examples of structured data. Unstructured data lacks a predefined format, making it more complex to analyse. It encompasses text documents, multimedia files, and social media content, necessitating the utilisation of advanced processing techniques such as natural language processing and machine learning for analysis.
Unsupervised Learning (UL) is dedicated to identifying patterns within datasets which has unstructured data points. Unlike supervised learning, UL operates independently, it does not require any external supervision and enables artificial intelligence systems to autonomously discern patterns from input data (Naeem, Ali, Anam & Ahmed, 2023). The strength of UL lies in its ability to reveal unexpected insights from data (Naeem et al., 2023). The algorithms are trained to process data which has no labels or classed data points.
Within the domain of unsupervised learning, clustering emerges as a primary learning task, focused on classifying items into cohesive groups based on similarities or patterns. Clustering methodologies encompass various forms, including partitioning, hierarchical, overlapping, and probabilistic clustering (Naeem et al., 2023).
Reinforcement learning (RL) empowers systems to autonomously make decisions in a dynamic environment using the principle of trial and error (Francois-Lavet, Henderson, Islam, Bellemare & Pineau, 2018). The decision which maximizes the collective rewards is reinforced based on feedback received for individual actions. The feedback can either be positive or negative, thus guiding the learning process for rational decision-making.
The RL emulates natural intelligence and mirrors human cognition. There is no explicit programming or human intervention in RL systems. These systems have algorithms which work on trial-and-error strategies to accomplish desired outcomes (Francois-Lavet et al., 2018).
For example, consider a scenario where an AI-powered autonomous vehicle learns to navigate a complex urban environment. The vehicle uses its RL algorithm to learn various actions such as acceleration, braking and steering, and receives feedback in the form of rewards or penalties based on its performance. Positive rewards can be given for reaching the destination safely and efficiently, while penalties can be given for violating traffic rules or causing accidents. Over time, the car learns to optimise its driving behaviour by associating actions with favourable outcomes. As a result, the car learns the art of safe and efficient navigation without human intervention, based on its RL algorithmic system.
Deep learning is a powerful type of machine learning that uses artificial neural networks (ANNs) to learn from complex data like images and speech (Kriegeskorte, 2015).
There are three fundamental layers in the architecture of an ANN. These include the input layer, hidden layers, and output layer. In image recognition tasks, for instance, the input layer receives pixel values representing the image, while the output layer produces predictions such as object labels or classifications. In between the input and output layers, the hidden layer is sandwiched. This sandwiched layer is vital in network learning, which extracts abstract features and representations from the input data through iterative training processes (Ghiassi & Saidane, 2005).
Data moves through the network to produce a prediction (Kag & Saligrama, 2021).
The network adjusts its parameters based on the prediction error to improve accuracy (Kag & Saligrama, 2021).
For example, in image recognition, an ANN is trained on labelled images. During forward propagation, it processes the pixel values and makes a prediction. During backward propagation, it adjusts its parameters based on the error between the prediction and the true label. Over time, the network improves its ability to accurately recognize objects in images (Kag & Saligrama, 2021).