What is AI?

Artificial Intelligence (AI) can be understood by comparing it to human abilities. AI is a broad field within computer science that aims to create intelligent and autonomous systems.

Humans communicate through spoken language, and the field of speech recognition focuses on enabling machines to understand and interpret human speech. This area often relies on statistical learning techniques, hence the term “statistical learning.”

Humans also read and write text, and Natural Language Processing (NLP) is the field that deals with teaching machines to understand and generate human language. It encompasses tasks like language translation, sentiment analysis, and question-answering.

The ability to see and process visual information is crucial for humans, and AI includes computer vision, which enables machines to interpret and understand visual data. Computer vision often involves image processing techniques and plays a significant role in object recognition.

While not directly related to AI, image processing is essential for computer vision as it deals with analyzing and manipulating images.

Humans possess the ability to understand their environment and move fluidly within it, which is the field of robotics. AI is instrumental in developing intelligent robots capable of perceiving, learning, and acting autonomously.

Humans excel at recognizing patterns, and AI leverages this through machine learning, where machines learn to identify patterns from large datasets. Machines have the advantage of handling high-dimensional data and can make predictions based on learned patterns that humans might not discern. Machine learning techniques can be used for classification or prediction tasks.

Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning. In supervised learning, algorithms are trained using labeled data to predict desired outputs. Unsupervised learning involves finding patterns and structures in unlabeled data. Reinforcement learning trains algorithms to achieve specific goals through trial and error, based on feedback from the environment.

Neural networks emulate the structure and function of the human brain, enabling machines to exhibit cognitive capabilities. Deep learning refers to the utilization of complex neural networks for learning intricate tasks.

Convolutional Neural Networks (CNNs) are a type of deep learning network widely used in computer vision tasks, specifically for object recognition in images.

Another type of neural network is the Recurrent Neural Network (RNN), which can remember sequential data, making it suitable for tasks involving past information, such as language modeling or time series analysis.

In summary, AI encompasses various fields such as speech recognition, NLP, computer vision, robotics, pattern recognition, machine learning, neural networks, and deep learning. It utilises both symbolic-based and data-based approaches to achieve intelligent behaviour. The different types of learning algorithms, including supervised, unsupervised, and reinforcement learning, enable machines to classify, predict, and learn from data, while neural networks replicate human brain functions.