What Is Machine Learning?
Machine learning is an AI methodology used to enable systems to complete tasks through a process of data analysis and pattern recognition.
The goal of machine learning is for systems using AI ML models to respond dynamically to new data without the need for additional programming or other human intervention. This adaptability, with continuous iteration, leads to improvements in the system’s responses and actions over time.
Machine learning applications are being adopted rapidly with a profound impact on many industries, including healthcare, education and automotive, as well as daily life and communication.
Classical Machine Learning
Until recently, machine learning has relied on data modeling and analysis techniques, including regression, support vectors, decision trees, and other statistical methods. This approach is categorized as classical machine learning.
In classical machine learning, human domain expertise is needed to design and select relevant features from a dataset and to label or otherwise structure the data that will be used to train ML algorithms.
Classical machine learning is particularly effective with structured, smaller datasets and tends to be less computationally intensive than advanced ML approaches such as deep learning.
An attribute of classical machine learning is that it is interpretable. This means a developer can often understand why an AI ML model made certain decisions or predictions and more easily adjust its parameters or process when needed.
Deep Learning and Neural Networks
The broader category of machine learning also includes contemporary approaches that are more complex, data-intensive, and less interpretable.
Deep learning, a subset of machine learning, can model multilayered patterns in complex datasets. Deep learning algorithms can detect and classify features within raw data, so developers don’t need to apply human domain expertise to identify or label each feature.
To perform well, deep learning typically requires vast amounts of data. That’s because the algorithms need to identify and categorize many parameters, and all of them must be present in the dataset in significant quantities to train the model. Deep learning systems with more than three layers are called neural networks because they mimic the interconnected nodes of the human nervous system.
Deep learning models, especially deep neural networks, may seem opaque, like “black boxes,” because the algorithms are so complex that it’s hard to understand how they arrive at a particular decision. A set of processes and methods known as explainable AI (XAI) are being used to bring more transparency to deep learning model decisions, which is important for building trust and accountability in these AI systems, particularly in regulated industries.
Benefits of Machine Learning
Machine learning is a fundamental component of all AI use cases, and its capabilities are transforming how people work, create, and interact and how businesses operate.
Machine learning can facilitate automated, personalized interactions, such as chatbots and recommendation systems, to help customers gather information and make informed decisions more quickly.
By automating routine tasks that once required human intervention, machine learning can help save time and improve efficiency. In many cases, the ML-enabled processes are faster and more accurate than a human-powered equivalent, yielding better results while diverting employees to more stimulating, creative tasks that computers cannot accomplish.
Machine learning can also be used to identify patterns and trends to detect anomalies or predict future events. These capabilities enable a broad range of innovative solutions to streamline or customize processes, such as in manufacturing, or to inform planning, such as in the energy sector to adjust the power grid based on forecasted demand.
How Machine Learning Works
Machine learning relies on statistical techniques to recognize and act on patterns in data without human intervention. An ML algorithm is trained to complete specific tasks by exposing it to a relevant dataset. During training, it can be optimized iteratively to improve the accuracy of its predictions and decisions. The quality and quantity of data and the algorithm’s adaptability are crucial for the success of the resulting AI model.
Machine Learning Methods
There are four main methods or types of machine learning:
- Supervised Learning: The algorithm is trained on a labeled dataset, which means that each training example is paired with an output label. The algorithm learns to predict the output from the input data.
- Unsupervised Learning: The algorithm is given unlabeled data without explicit instructions. The system must learn the patterns and structure from the data itself.
- Semi-Supervised Learning: This approach uses both labeled and unlabeled data for training. Typically, a small amount of labeled data is paired with a larger amount of unlabeled data.
- Reinforcement Learning: The algorithm learns as it tries to achieve goals in a dynamic environment. Examples include driving a car or playing an interactive game. The system receives feedback on its actions as it navigates the problem space.
How Machine Learning Is Used
AI machine learning is used in a range of computing tasks that are too complex to be administered by explicit, rules-based algorithms. Examples of these complex applications include speech recognition, computer vision, recommendation systems, and fraud detection.
Machine Learning Use Cases
Customer Service
Machine learning methods are used to streamline and improve many aspects of customer service, especially for online service providers.
Large language models (LLMs)—deep learning models designed to understand, translate, and generate humanlike language—are the foundational powerhouses behind speech and text-focused tools, such as voice-to-text, voice assistants, chatbots, and generative AI (GenAI) applications, that help automate customer service functions.
Online retail and service providers also deploy recommendation systems to improve customer service and engagement. Recommendation systems depend on machine learning and data filtering techniques to make accurate predictions about the types of content a user wants based on past choices, feedback, engagement, and similar patterns from other users with similar profiles.
Manufacturing
Quality control in manufacturing is assisted by computer vision, which in turn relies on machine learning and deep learning. Computer vision interprets visual data in many industrial settings to detect defects and anomalies, monitor and control processes, accelerate responses, and produce valuable insights, often in near-real time.
Financial Services
Machine learning methods contribute to the finance and banking industries by harnessing pattern recognition to streamline and improve such diverse activities as fraud detection, regulatory compliance, personalized banking, payment processing, and even automated stock trading.
Challenges of Machine Learning
Some machine learning solutions can be implemented with support from pretrained models and open source tools. In most cases, however, customization is required even for modest implementations. For organizations that are just beginning to adopt AI, it can also be difficult to recruit team members with the necessary skillsets.
Once staff is in place, the next challenge is to design or customize an effective and efficient model. Many iterations may be required to prevent either overfitting—when the model’s interpretation of the training data is too precise and does not encompass new data—or underfitting—when a model is too simple to capture underlying data patterns. In some cases, the model will require large amounts of data to improve the accuracy of its predictions, and additional data may be difficult, costly, or time-consuming to acquire.
Data security is another challenge. Some machine learning applications, such as recommendation systems, rely on users’ browser and purchase histories, as well as demographic data. Multilayered security solutions, including those that leverage AI, can help protect users’ privacy and personal information, as well as the organization’s confidential data and intellectual property.
New AI ML implementations should also conform to responsible AI practices, to ensure the system will be used in safe, trustworthy, and ethical ways.
Future of Machine Learning
AI adoption is just in its infancy. Rapid advances in AI ML and supporting technologies are making it more accessible to businesses and, in turn, unlocking unlimited possibilities for its application and global impact. The future of machine learning will see it applied in new use cases and new contexts.