Machine learning: Everything you need to know

What is machine learning?

Establishing a clear machine learning definition can be challenging. ML is a type of artificial intelligence that allows machines to learn from data without being explicitly programmed. It does this by optimizing model parameters (i.e. internal variables) through calculations, such that the model’s behaviour reflects the data or experience. The learning algorithm then continuously updates the parameter values as learning progresses, enabling the ML model to learn and make predictions or decisions based on data science.

The applications of machine learning are wide-ranging, spanning industries such as healthcare, finance, marketing, transportation, and more. Machine learning models are already being used for image recognition, natural language processing, fraud detection, recommendation systems, autonomous vehicles and personalized medicine.

Overall, machine learning plays a crucial role in enabling computers to learn from experience and data to improve performance on specific tasks without being programmed. It has the potential to revolutionize various industries by automating complex processes and making intelligent predictions or decisions by “digesting” vast amounts of information.

How does machine learning compare to deep learning and neural networks?

Deep learning is a subset of machine learning, which is focused on training artificial neural networks. With multiple layers, neural networks are inspired by the structure and function of the human brain. Like our brains, they consist of interconnected nodes (neurons) which transmit signals.

These complex algorithms excel at image and speech recognition, natural language processing and many other fields, by automatically extracting features from raw data through multiple layers of abstraction. Deep learning can handle datasets on a massive scale, with high-dimensional inputs. To do so, it needs a significant amount of computational power and extensive training.

How machine learning works

The first step in machine learning is collecting relevant data which may come from sources such as databases, sensors or the Internet.

• Preprocessing data: Once the data is collected, it needs to be preprocessed to ensure its quality and suitability for analysis.
• Training the model: The next step is to train a machine learning model: an algorithm or mathematical representation that learns to make predictions or decisions from input data.
• Feature selection and engineering: That machine learning model then selects the most relevant features from the input data that will have a significant impact on the model’s performance.
• Evaluating and optimizing the model: Once a model is trained, it needs to be evaluated to assess its performance and determine whether it meets the desired criteria.
• Deployment and monitoring: After successful training and evaluation, the model can be deployed in real-world applications of machine learning.

Common machine learning training models and algorithms

Machine learning builds on existing computer science, relying heavily on statistics, probability theory and optimization techniques. There are three main types of machine learning:

• Supervised learning, in which the algorithm learns from input-output pairs provided in a training dataset.
• Unsupervised learning, in which it finds hidden patterns or structures in unlabelled data.
• Reinforcement learning, which uses rewards or punishments to train an algorithm to make optimal decisions in a given environment.

What are the advantages of machine learning?

Machine learning offers a wide range of benefits across various industries and applications. With the ability to process vast amounts of data in real time, machine learning can also identify inefficiencies in processes, optimize workflows and improve overall productivity.

Here are some more specific advantages of machine learning:

• Automation of repetitive tasks, saving time and resources. This allows humans to focus on more complex and creative aspects of their work.
• Personalization and recommendations: By analysing user preferences and behaviour, machine learning powers personalized experiences. Platforms like Netflix, Amazon and Spotify use it to suggest content based on individual user patterns.
• Data analysis and pattern recognition: Machine learning excels at analysing large datasets to identify patterns and trends that may not be apparent through traditional methods. This can lead to valuable insights and informed decision making.
• Improved decision making: By providing accurate and data-driven insights, machine learning aids more informed decision making across various domains, from marketing strategies to supply chain optimization.
• Predictive analytics: Machine learning algorithms can make predictions based on historical data, anticipating future trends, customer behaviour and market dynamics. This is particularly useful in financial forecasting, demand prediction and risk management.
• Enhanced customer experiences: Machine learning enables the chatbots and virtual assistants that interact with users in a natural language format, providing quicker and more personalized responses to enhance customer support and engagement.
• Fraud detection and cybersecurity: Machine learning algorithms can detect unusual patterns and behaviours in data, aiding fraud detection in financial transactions and enhancing cybersecurity by identifying potential threats.
• Medical diagnosis and healthcare: Machine learning helps predict patient outcomes and personalize treatment plans. It can analyse medical images, such as X-rays and MRIs, to assist in the detection of diseases.
• Optimized resource allocation: Machine learning predicts demand, manages inventory and streamlines supply chain processes. This is crucial for industries dealing with perishable goods or fluctuating market demands.
• Efficient recruitment and HR processes: Machine learning algorithms can speed the recruitment process by analysing resumés, identifying suitable candidates and predicting employee performance.

Machine learning: promises and challenges

Machine learning in artificial intelligence opens a realm of possibilities for businesses and society. As well as the numerous benefits listed above, it is part of an AI landscape which promises world-changing innovation in the field of climate change resilience and mitigation, powering the acceleration of solutions to some of the planet’s most serious problems.

However, this comes with risks. It’s essential to address ethical considerations, data privacy and potential biases to ensure responsible and fair use of these technologies. Additionally, the effectiveness of machine learning applications depends on the quality of the data and the appropriateness of the chosen algorithms for specific tasks.

This is where International Standards play a critical role in providing clear guidelines and regulations to prevent misuse and protect users. ISO, in collaboration with the International Electrotechnical Commission (IEC), has published a number of standards related to machine learning through its dedicated group of experts on artificial intelligence (ISO/IEC JTC 1/SC 42). Its most recent standard on the subject is ISO/IEC 23053 which provides a framework for AI systems using machine learning.

Will machine learning be the future of AI?

The ultimate goal of AI is to design machines that are capable of reasoning, learning and adapting to various domains. This will require advanced capabilities in a variety of AI subfields and machine learning is a vital part of this.

The future of machine learning, as part of the wider field of AI, is exciting for many and concerning for some. The development of International Standards is crucial if we are to minimize its risks and maximize its many benefits in every part of our lives.