Artificial Intelligence: A Brief Overview
Artificial intelligence (AI) is fundamentally the replication of human intellect in robots trained to think like people and copy their activities [6]. The phrase also refers to any machine that has characteristics similar to the human mind, such as learning and problem-solving. AI’s strength lies in its ability to analyze, comprehend, and learn from massive volumes of data, find patterns, make predictions, and adapt to new information. At the heart of many AI systems is the backpropagation algorithm, a mathematical technique used to train artificial neural networks [7]. AI is a broad field with many sub-areas, but at a high level, it can be divided into two categories: narrow AI, which is designed to perform a specific task, such as voice recognition, and general AI, which can understand, learn, and apply knowledge across a wide range of tasks, much like a human. The notion of AI has its origins in antiquity, with myths, legends, and theories about artificial entities endowed with intellect or consciousness by skilled artisans. However, as a scientific subject, AI is relatively new. It was initially presented as a topic of study at the Dartmouth Conference in 1956 when the term ’Artificial Intelligence’ was created [8]. This signalled the beginning of AI as an autonomous discipline. The subsequent decades saw a series of ups and downs, with periods of intense excitement and funding (known as AI summers) followed by periods of disappointment and reduced funding (known as AI winters) [9]. Early AI research concentrated on rule-based systems and symbolic thinking. However, when dealing with complicated, real-world challenges, these techniques proved to be limiting. The development of machine learning in the 1980s and 1990s transformed the area, allowing computers to learn from data rather than writing explicit rules. This revolution culminated in the creation of deep learning in the 2000s and 2010s, a technique based on the architecture of the human brain that analyzes and learns from data using artificial neural networks [11].
The backpropagation algorithm, central to training neural networks, involves the iterative application of the chain rule from calculus to update the weights in the network [12]. It can be summarized by the following equation:
\begin{equation} \Delta W\text{ij}=-\eta\frac{\partial E}{\partial w\text{ij}}\nonumber \\ \end{equation}
Where: Δwij ​ is the change in weight connecting neuron ito neuron j , η is the learning rate, ​∂E/∂wij ​ is the partial derivative of the error function Ewith respect to the weight wij ​.
Deep learning, powered by backpropagation, has been responsible for many of AI’s most exciting recent advancements, including self-driving vehicles, voice assistants, and recommendation systems. Today, AI technologies have permeated nearly every part of our lives and are evolving at an unprecedented rate. As we approach a new age in AI research and application, it is critical that we examine and exploit its ability to interpret complex patterns in human behaviour, leveraging tools such as the backpropagation algorithm to advance our understanding and capabilities.