What is machine learning? Models & definition

Machine learning is a subset of artificial intelligence (AI) that employs a specific technique to develop algorithms that computers use to improve decision-making and predictive capabilities. Scientists can create increasingly intelligent and accurate AI models through machine learning and computer and information research.

Machine learning models grow their abilities over time by gathering insights from large quantities of data and using them to hone their skills. Machine learning is so named because, much like the way humans learn, this subset of AI allows computers to apply what they’ve learned to future challenges, processing enormous quantities of data much faster than a human could. As a result, machine learning serves as a powerful tool to help humans understand information, identify trends, and make decisions faster.

Machine learning vs AI vs deep learning

Machine learning is sometimes confused with the concepts of AI and deep learning. In similar contexts, AI and deep learning are often related to machine learning, but the three terms are not synonymous.

Machine learning vs AI

AI is short for artificial intelligence, which refers to the category of technologies that mimic human cognitive capabilities using logic and math. By definition, AI doesn’t necessarily use or require machine learning. Consider one of the earliest examples of AI rule-based systems: these models use a set of if-then rules (also known as explicit programming) to make logical decisions. A rule-based system can make simple decisions that don’t require nuance.

However, most AI developed today is trained using machine learning. Machine learning enables AI models to replicate human behavior and intelligence more sophisticatedly using inferences drawn from large amounts of data. Different types of machine learning algorithms can make quantitative and qualitative predictions, differentiate truth from fiction, recognize patterns, and sort items into categories, all much more complex tasks than a rule-based system could handle.

Imagine that you’re trying to create an AI tool that is able to understand the product lifecycle and identify common problems in the manufacturing process before they happen. Using a rule-based system, you would need to create a specific and detailed set of warning signs that indicate the potential for an issue to occur (these are your “rules”). This system may be able to detect the problems you told it to expect, but it won’t catch problems you didn’t foresee.

With machine learning, you can build a system that handles tasks and decisions like issue detection in a more advanced way, closer to how a human could. This kind of AI model doesn’t need to be told precisely what decision to make in a particular scenario. Instead, using information gathered from the large quantities of data on which they’re trained, machine learning algorithms can learn to identify patterns and problems independently. A machine learning model can even identify trends of which humans may be unaware.

Machine learning vs deep learning

Just like machine learning is a subfield of AI, deep learning is a subfield of machine learning. Deep learning takes the principles of machine learning “deeper” by using networks of artificial neurons to mimic the processes of the human brain.

This network of neurons takes an enormous amount of data but can replicate both the physical layout of the brain and the shape of human thought processes. That means deep learning algorithms support non-linear thinking with greater nuance and better decision-making power. Remember our example using an AI tool to predict issues in the product lifecycle? A machine learning tool would be able to learn how to identify problems. But a deep learning tool could take those capabilities further, identifying more complex trends, prioritizing issues, and suggesting — or executing — solutions.

How does machine learning work?

At its heart, machine learning entails giving a computer large quantities of data and allowing it to identify patterns and relationships within that data. Four phases correspond to different types of data: training, validation, testing, and fine-tuning.

Machine learning training

First, the model is given large quantities of training data. This data should be cleaned beforehand, including removing statistical errors and outliers, so that the AI has reliable information to use. Then, the algorithm processes the data, adjusting its parameters to make more and more accurate predictions.

Machine learning validation

Following training comes the validation stage, which uses new data called validation data to measure the accuracy of the AI model. Ideally, the model should be able to generalize its insights from the training data to make accurate predictions about the validation data. Just like in training data, validation requires input with a known output. However, unlike training, in the validation phase, the model can only see the input, not the output, for each data point.

If you’re training a model to recognize and filter out spam email, the training data would include a set of emails and whether each was marked as spam. However, validation data would only include the emails themselves. Then, as the last step in validation, the user compares the machine-generated outputs to the correct outputs. At this stage, the model is still learning and changing, so it has the opportunity to gather new insights that it will use to revisit the training phase.

Machine learning testing

The model is in the testing stage and getting ready for deployment. The test data is another new dataset the model has never seen before, with outputs excluded. Comparing the machine-generated outputs to the correct outputs measures the model’s actual performance in its final form.

Machine learning fine-tuning

At this point, most machine learning models could still stand to improve their performance. Fine-tuning is the process of iteratively improving a machine learning model on an ongoing basis, helping it evolve into a more accurate tool, including for niche or hyper-specific use cases. In this process, the human builder or maintainer of the AI will adjust certain parameters in an attempt to achieve higher accuracy.

What are the different types of machine learning?

There are a few ways to use machine learning to teach AI models. These include supervised, unsupervised, semi-supervised, and reinforcement learning.

Supervised learning

Supervised learning is a type of machine learning in which the model is given training data that is labeled. Labeled training data tells the algorithm what the correct output should be for a given input. Imagine that you’re building a tool to predict whether supply chains will slow or halt in inclement weather, affecting whether you have the materials needed for manufacturing. As part of supervised learning, you’ll give the algorithm a set of labeled data that matches each input, the weather on a given day, to an output, whether or not the supply chain was slowed. After processing the dataset pairs, the model should learn to determine whether to expect delays given a specific forecast so that you can adapt your manufacturing strategy as early as possible.

Unsupervised learning

In unsupervised learning, machine learning algorithms are presented with unlabeled or raw data, with many inputs but no instructions about what to look for. Then, the model scans through the data and looks for patterns itself. That means it may be able to find trends that are not obvious to humans or would take humans much longer to notice. For instance, a tool to segment contract manufacturing customers by their custom order habits would better suit unsupervised learning. With supervised learning, this tool would only use the segments in the training data. However, with unsupervised learning, the tool can identify segments for itself, resulting in more accurate targeting and detailed information.

Semi-supervised learning

Semi-supervised learning is aptly named because in this technique, models are partly trained using supervised learning and partly unsupervised: a small portion of the data is labeled, but most is unlabeled. This technique blends supervised and unsupervised learning to give the model a solid jumping-off point to identify further trends. Semi-supervised learning is most useful for applications where a slight nudge in the right direction will help with pattern recognition accuracy, like medical imaging software to detect abnormalities.

Reinforcement learning

Reinforcement learning is a type of machine learning technique that rewards AI systems for correct results and penalizes them for incorrect results. Just like in psychological reinforcement, these positive and negative reinforcements are meant to incentivize correct behavior and disincentivize misbehavior. Using reinforcement learning, a machine learning algorithm can be trained to interact with its environment while thinking long-term.

For instance, let’s say a machine learning model analyzes images of materials to determine whether or not a common defect exists. If the model ever marks a defective part as regular or a regular part as defective, we’d want it to learn that it shouldn’t make that same mistake: a great opportunity for negative reinforcement.

Why is machine learning important?

Machine learning is crucial because it is a foundational technique used in AI development. AI is the defining technology of today’s digital era and will be invaluable to driving innovation and efficiency across the business world of the future. Machine learning allows AI models to achieve better performance out of the gate and continue to improve as they encounter more data.

Using machine learning enables the creation of pivotal technologies such as large language models (LLMs) and image generators. As these technologies continue to grow in scale and capabilities, a comprehensive understanding of machine learning will become even more essential to understanding how AI is reshaping the world.

Common machine learning algorithms

Several kinds of machine learning algorithms exist, including neural networks, linear regression, logistic regression, clustering, decision trees, and random forests.

• Neural networks: Mimic the structure and function of the human brain, building multilayered networks that rely on artificial neurons to process complex information with nuance.
• Linear regression: The model learns to create a linear equation that describes the relationship between input and output.
• Logistic regression: The model learns to predict the likelihood that a given input will fit into a certain category or not.
• Clustering: Built with an unsupervised learning technique, a clustering algorithm learns to group raw data into clusters based on its perception of their shared attributes.
• Decision trees: Classify inputs using a branching system of decisions, starting with larger groupings and focusing on more specific characteristics as it goes.
• Random forest: Multiple decision trees are used together to evaluate various aspects of an item and better classify it with a more holistic understanding of its attributes.

How machine learning applies to PLM

Machine learning is reshaping PLM by enhancing decision-making, automating processes, and uncovering patterns that were previously difficult to detect. It enables organizations to streamline operations, improve product quality, and gain deeper insights throughout the product lifecycle.

Here are key areas where machine learning makes a difference:

• Requirements management: By analyzing increasingly complex requirements coming from multiple sources, in multiple formats and with poorly structured content, AI can identify duplications, resolve inconsistencies and identify existing implementations that satisfy them reducing human errors and costs of over or under design.
• Predictive maintenance: Machine learning models analyze historical and real-time IoT sensor data to predict potential failures before they happen, reducing downtime and maintenance costs.
• Design optimization: By analyzing past product designs, machine learning can identify inefficiencies and suggest improvements to enhance performance and manufacturability.
• Supply chain management: Machine learning helps predict disruptions, optimize sourcing strategies, and assess supplier performance based on quality metrics, lead times, and defect rates.
• Compliance and risk management: Machine learning algorithms can analyze regulatory data and product documentation to detect potential compliance risks early, reducing costly redesigns and delays.
• Intelligent search and automation: AI-powered PLM tools, such as copilots, assist in automating classification, impact analysis, and documentation, making it easier to find and act on critical product information.

By integrating machine learning into PLM, companies can accelerate innovation, improve product quality, and drive efficiency across the entire product lifecycle.

To learn more about machine learning in PLM, check out our blog post
Exploring Practical AI Use Cases in Product Lifecycle Management

The future of machine learning

In the years to come, machine learning capabilities will continue to expand and grow, reshaping even more of our world. This rise will be supercharged by the continued boom of data available with which to train AI models, resulting in more complex capabilities.

For instance, while many models can make solid decisions now, machine learning is expected to handle even more complex inputs and arrive at more nuanced conclusions in the future. Machine learning algorithms may be more portable, able to be reused, and work together to create improved outcomes. While AI is already capable of generating code, future machine learning-driven code generation tools will be able to handle more difficult requests and build more sophisticated products.