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Machine Learning (ML) is a branch of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that allow computers to perform tasks without being explicitly programmed. Instead of following predefined instructions, ML systems use data to learn and make decisions or predictions based on patterns. This ability to improve over time, often through experience, is a key characteristic of machine learning.
Machine learning is already powering many of the tools and technologies used every day—from streaming recommendations and fraud detection to self-driving vehicles and virtual assistants. But what exactly is it, how does it function, and what makes it distinct from broader artificial intelligence? This guide breaks down the core principles, types of machine learning, and where this powerful technology is making an impact.
How Does Machine Learning Work?
At its core, machine learning uses data to train algorithms to identify patterns. The more data an ML model is given, the better it can learn from it and predict or classify new data in the future. Machine learning models are trained using large labelled data sets, where the input data and the corresponding correct output are known.
The training process typically involves using a mathematical model to find relationships in the data and then adjusting the model to make accurate predictions. This process often requires optimisation algorithms that help the model improve over time.
What is Machine Learning?
Understanding how machine learning works is essential to grasping its capabilities and limitations. Although machine learning systems can seem complex, the process of teaching a machine to learn from data follows a structured series of steps. These steps ensure that the model can learn effectively, be evaluated for accuracy, and improve over time. There are four main stages in how machine learning works:
- Data Collection: The first step involves gathering large amounts of data that can help the model learn. The quality of this data is crucial since it influences how well the machine learning model can perform.
- Model Training: Once the data is collected, a machine learning algorithm is used to train the model. The goal of training is to enable the model to understand patterns and relationships in the data.
- Evaluation: After the model is trained, it is tested using unseen data (called a test set). This helps assess how well the model has learned and if it can generalise to new, unknown data.
- Improvement: If the model doesn’t perform well, changes might be made to the algorithm or the data to help improve its accuracy
When was Machine Learning invented?
Machine learning was formally established as a field in the mid-20th century, though its conceptual foundations date back earlier. In 1950, Alan Turing introduced the idea of machine intelligence in his paper Computing Machinery and Intelligence, proposing the Turing Test as a way to evaluate a machine’s ability to exhibit intelligent behaviour.
The term "machine learning" was first coined in 1959 by Arthur Samuel, who developed a self-improving checkers program, demonstrating that computers could learn from experience rather than relying solely on pre-programmed rules.
Throughout the 1960s and 1970s, researchers developed early ML models focused on pattern recognition and statistical learning. However, significant advancements occurred in the 1980s with the development of backpropagation, a method for training multi-layer neural networks, marking a major step towards modern ML.
The Fundamentals of Machine Learning
Machine learning is built on four fundamental concepts that help guide its development and use:
- Data: Data is the backbone of machine learning. The more relevant and accurate the data is, the better the model will perform. Data can come in various forms, such as images, text, or numbers. Machine learning models need to process and understand this data to make predictions or classifications.
- Model: A machine learning model is a mathematical representation of the system or process being studied. It is built using an algorithm and is used to make predictions or decisions. Models can be simple, like linear regression, or complex, like neural networks.
- Training: Training is the process of teaching the machine learning model using data. During training, the model adjusts its parameters based on patterns in the data and gradually improves its accuracy.
- Prediction: Once trained, the model can be used to make predictions or classifications on new, unseen data. The quality of these predictions depends on how well the model has been trained.
What are the Different Types of Machine Learning?
Machine learning can be divided into four main categories, each with a distinct approach for how data is processed and learned from. Supervised learning uses labelled data with known answers to guide the learning process. Unsupervised learning works with unlabelled data and looks for hidden patterns or groupings. Reinforcement learning learns by trial and error, receiving rewards or penalties based on actions taken. Finally, semi-supervised learning combines a small amount of labelled data with a large amount of unlabelled data to improve learning efficiency.
- Supervised Learning: In supervised learning, the model is trained using labelled data, meaning the input data comes with the correct output. The algorithm learns from the data by comparing its predictions to the actual outputs and adjusting until it gets it right. This is commonly used for classification tasks (e.g., identifying whether an email is spam or not) and regression tasks (e.g., predicting house prices).
Example: Gmail uses supervised machine learning algorithms trained on vast datasets of labelled emails (spam and not spam) to detect and filter unwanted messages. The system learns from patterns such as keywords, sender addresses, and user feedback (when users mark emails as spam or not spam). This allows the filter to continuously improve and adapt to new spam techniques.
- Unsupervised Learning: In unsupervised learning, the data used to train the model is not labelled. Instead, the algorithm tries to find hidden patterns or groupings within the data. This is useful for tasks like clustering, where the goal is to group similar data points together, or anomaly detection, where the goal is to find outliers.
Example: Guardian Analytics uses unsupervised machine learning to monitor financial transactions and detect unusual activity. By analysing patterns of normal customer behaviour, their system can identify anomalies that may indicate fraud, without the need for pre-labelled examples. This enables financial institutions to spot emerging fraud schemes that traditional rule-based systems might miss.
- Reinforcement Learning: Reinforcement learning is a type of machine learning where an agent learns by interacting with an environment. The agent takes actions and receives feedback in the form of rewards or penalties. Over time, the agent learns to take actions that maximise its reward.
Example: One of the most famous examples of reinforcement learning is AlphaGo, developed by DeepMind. AlphaGo played the game of Go against human professionals and learned strategies by playing thousands of games, improving its skills through experience.
- Semi-Supervised Learning: Semi-supervised learning is a type of machine learning where the model is trained on a small amount of labelled data and a large amount of unlabelled data. The goal is to make use of the abundant unlabelled data to improve the model’s performance, while the labelled data provides some guidance during training.
Example: Google’s image recognition system uses semi-supervised learning to improve its object recognition capabilities. By combining a small set of labelled images with a larger pool of unlabelled data, the system can learn to identify objects more accurately, even when labelled data is scarce. This method is especially useful in tasks like facial recognition and categorising images.
Is ChatGPT Machine Learning?
ChatGPT, a generative AI chatbot developed by OpenAI, is based on machine learning. It uses a specific type of machine learning called natural language processing (NLP), which enables it to understand and generate human-like text. ChatGPT has been trained on vast amounts of text data and learned to respond to various prompts by predicting the most likely next word or sentence based on its training.
The model is a type of deep learning model called a transformer, which is especially good at handling sequences of data, such as text. This allows ChatGPT to hold coherent conversations and provide informative answers to questions. So, while ChatGPT isn’t a simple machine learning model, it is indeed powered by advanced ML techniques.
What is the Difference Between AI and ML?
The terms artificial intelligence (AI) and machine learning (ML) are often used interchangeably, but they refer to different concepts. AI is a broader field of computer science focused on creating machines that can perform tasks that typically require human intelligence. These tasks include problem-solving, reasoning, understanding language, and perception. AI encompasses many approaches, including rule-based systems, expert systems, and machine learning.
ML, on the other hand, is a subset of AI that focuses specifically on developing algorithms that allow machines to learn from data and improve over time without explicit programming. In short, all machine learning is AI, but not all AI is machine learning.
Machine learning offers significant benefits, such as automating tasks, enhancing efficiency, and analysing large data sets for insights in sectors like healthcare, finance, and retail. However, it also has drawbacks, especially regarding inherited human bias.
For instance, Amazon’s recruitment tool was found to favour male candidates because it was trained on historical data, which predominantly included male résumés. This highlights the risk of ML perpetuating existing biases, such as those seen in facial recognition and healthcare systems. These issues underscore the need for careful, ethical oversight in ML deployment.
AI Enabled by Machine Learning is Here
Machine learning is a transformative technology that is changing the way people interact with machines and data. From improving recommendations on streaming platforms to powering self-driving cars, ML is behind many innovations in various industries. It is built on four basics: data, models, training, and prediction.
Its four main types—supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning—are used for different tasks, such as classification, clustering, and decision-making. As we continue to feed machines more data, their ability to make accurate predictions and decisions will only improve.
Whether you're asking Siri a question or getting a product recommendation on Amazon, machine learning is likely working behind the scenes to enhance your experience. As the field evolves, we can expect even more groundbreaking applications of machine learning that could revolutionise our world in ways we can’t yet fully imagine.
