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Types of Learning in AI, Explained in Plain English

types of learning in ai

Types of Learning in AI, Explained in Plain English

AI is touted as a groundbreaking development in technology, and with good reason. The ability to automate time-consuming, expensive, or monotonous tasks is amazing. Just take a look at the buzz created over applications like ChatGPT.

Personalization is another big benefit of AI, both in terms of marketing and entertainment. Predicting future outcomes is another area where AI gets to shine. However, AI is not as all-powerful as some people may think. To get an AI model to work effectively and efficiently, it must first be trained on one or more sets of data.

As well as this, the model must be given some guidelines and algorithms to make use of. This field is known as AI learning, and there are many categories of learning within this. Read on to discover the main types of AI learning and how they’re used, as well as their advantages and drawbacks.

What Is Learning in AI and Why Is It Important?

When talking about AI learning, we mean the way that AI models are trained to enhance their performance in certain tasks. In most cases, the model is provided with some input data. This can be labeled with the correct output, or unlabelled. The main idea is for the AI model to learn from this data, usually calculating an appropriate function.

This can then be used to predict future outputs based on new input data. The model is often also provided with certain algorithms to do its job. The significance of AI learning is that it’s essential to improve the accuracy and consistency of AI models.

This is crucial in many applications, but especially those where high accuracy is necessary — think medical diagnosis, self-driving vehicles, or financial planning. Training AI models well can also help them be versatile, and be applied to new situations.

What Are the Main Categories of AI Learning?

At a high level, there are 4 main categories of learning in AI: supervised, semi-supervised, unsupervised, and reinforcement learning. We’ll explain what these mean next.

Supervised Learning

types of supervised learning
Various algorithms and computational techniques are used in supervised learning processes.

This is a form of learning where the dataset that the machine is being trained on has inputs that are already labeled. Because the inputs are mapped to outputs, the machine has a helping hand here.

The idea is that, once the machine has been trained and has recognized the relationships between input and output, it will be able to calculate outputs without supervision. For example, we could train a machine on input data related to stock prices.

This dataset would be vast, with hundreds or thousands of examples of inputs. If successful, the machine would calculate a function that could map inputs to outputs, and use this to predict future stock prices.

Generally, there are two kinds of algorithms used within supervised learning: classification algorithms and regression algorithms. Classification algorithms are concerned with finite, discrete data, i.e. colors or shapes, regression algorithms work with continuous data, i.e. numbers.

Meanwhile, supervised learning can potentially give us an exact answer, but it does require a lot of computational power. Supervised learning is often used in image and speech recognition, medical diagnosis, and fraud detection.

Semi-Supervised Learning

This is sort of like supervised learning, but with not as many inputs labeled, and even these may not be completely accurate. Semi-supervised learning is a type of AI learning mostly used where completely labeling the inputs would be expensive computationally, or labeling is particularly difficult.

In addition, if the labeled data is in short supply or contains a lot of noise, the unlabelled data can help to mitigate this. However, semi-supervised learning can’t have as high accuracy as fully supervised learning.

Overall, semi-supervised learning aims to strike a balance between accuracy and computational costs. A real-life analogy would be if a student received support from a teacher, but was then left to solve problems based on their gained knowledge. This type of learning is used in image and text classification, as well as speech recognition.

Unsupervised Learning

types of learning in ai
Unsupervised learning is a type of algorithm that learns patterns from untagged data.

You may imagine that unsupervised learning refers to learning where the input data isn’t labeled. In this case, you’d be correct. No figurative teacher is guiding the machine, which must figure out the appropriate functions by itself.

Unsurprisingly, this is a much more complex process to execute than supervised learning, but the potential results here are much greater. In theory, mastering unsupervised learning will lead to exponential leaps in AI capabilities, as machines will be completely able to teach themselves. Naturally, this is extremely hard to achieve, since the machine must rely on its own logic completely.

As an example, take a variety of shapes. These will have differences, such as the number of corners and sides, as well as internal and external angles. The machine will attempt to figure out these differences and the patterns of the shapes to predict future outputs.

Unsupervised learning is mainly used where we want to group similar data points (known as clustering), detect anomalies in data, or study generative models so that new data can be created.

All in all, unsupervised learning can be used on more complicated tasks than supervised learning, but does tend to be more inaccurate and difficult. It’s used in applications such as anomaly detection, information extraction, and network analysis.

Reinforcement Learning

types of learning in ai
The focus of reinforcement learning is on finding a balance between exploration and exploitation (of current knowledge).

When it comes to this type of learning in AI, no training data is used, but the machine does receive some help. The guidance is in the form of an “environment,” meaning it’s provided with goals, prescribed actions, and feedback on its performance.

In some sense, this is similar to supervised learning, although there are no data labels and the feedback received can be appreciably noisy. The machine learns only through trial and error and is incentivized to improve its performance through rewards and penalties for its actions.

As such, the machine will learn to maximize the “points” it receives, improving its efficiency. Reinforcement learning is used in scenarios where certain actions are desirable, such as training robots to perform tasks or play games, financial trading, and autonomous vehicles.

This learning type has many advantages because it’s suitable for elaborate problems and closely simulates the way that humans learn to act. However, substantial amounts of data are required, as well as a lot of computations.

What Algorithms Are Used in AI Learning?

The number of algorithms in AI learning is very extensive, but some of the most common ones are listed here. For a closer look at some of the algorithms, check out our article on supervised learning.

AlgorithmCategoryUses
Logistic regressionSupervisedClassification, e.g. spam filtering
Linear regressionSupervisedPredictions, e.g. house or stock prices
Support vector machines (SVMs)SupervisedClassification, e.g. text and images
K-nearest neighborsSupervisedClassification and regression tasks
Random forestsSupervisedClassification and regression tasks
Decision treesSupervisedClassification and regression tasks
K-means clusteringUnsupervisedSegmentation, i.e. for customers or markets
Hierarchical clusteringUnsupervisedSocial network analysis, bioinformatics
Principal component analysisUnsupervisedImage compression, information extraction
DBSCANUnsupervisedImage segmentation, anomaly detection
SarsaReinforcementVideo game playing, robotics
Monte CarloReinforcementEngineering, finance, computational chemistry
Q-learningReinforcementVideo game playing, robotics
Deep Q networkReinforcementVideo game playing, robotics

What Are the Most Common Types of Learning in AI?

Now that we’ve covered the learning types at the top of the hierarchy, it’s time to delve a little deeper. There are many types of AI learning, but some are more common than others. These are listed in the table below, along with the category of learning that they fall into.

Learning TypeCategory
EnsembleAny
TransferAny
OnlineAny
ActiveSupervised or semi-supervised
TransductiveSemi-supervised
Multi-instanceSupervised
Multi-taskSupervised
DeductiveSupervised
InductiveSupervised
Self-supervisedUnsupervised

There are quite a lot of types of learning here, so we’ll examine each one briefly.

Ensemble Learning

This makes use of ensemble algorithms, where multiple models are combined to improve accuracy. Generally, two types of ensemble learning are used. The first is where results from multiple models trained on different algorithms are combined, and the second is where models are trained one after the other on the same data, correcting the previous model’s errors as they go.

Ensemble learning tends to be used in both regression and classification tasks. It helps to make models more robust so that they’re less easily affected by anomalous data. However, ensemble algorithms can be complicated and laborious, and the results may be difficult to comprehend due to many models being used.

Transfer Learning

The term “transfer” comes from the principle of this learning where the machine uses its knowledge from one task to improve its performance on another. In essence, it “transfers” what it has learned to this new situation.

This type is used a lot in natural language processing and image classification and helps to reduce the computational power needed. As the model is trained on a specific task with test data, this can then be applied to related tasks with a lot more data to process.

This results in lower costs and better performance than using a model that hasn’t been trained already. Like with any model, there are some drawbacks, however. Transfer learning can only be used when tasks are related in some way, and isn’t always the cheaper option. If a lot of adjusting is needed to find the best model for the task, costs can easily add up.

Online learning

Online learning can also be called incremental learning. This is because the model is updated gradually as it receives new data. The machine is trained on these inputs as it receives them, updating its parameters as it goes. Online learning can be very useful when memory is restricted, and all the related data cannot be stored in one instance.

This type is also helpful when data is changing significantly over time because the model updates itself systematically. Online learning can be used for text classification and fraud detection, cybersecurity, and most situations where data is changing in real time.

This type of AI learning can require more fine-tuning, however, and can be prone to fitting itself to noisy or incorrect data as it receives inputs one after the other.

Active Learning

autogpt
When it comes to active learning, an algorithm can ask the user to label new data points with the desired outputs.

As a subtype of supervised learning, active learning permits the model to ask human operator questions as it’s being trained. It’s often used where it would be expensive to label and collect data and can be considered a strategy for approaching problems that would usually be semi-supervised.

The amount of data needed can be minimized by asking for support from a human. Active learning applies to bioinformatics (the field of using technology to interpret biological data), natural language processing, and image recognition.

Although it’s extremely useful, active learning isn’t appropriate for all learning problems and is very dependent on the strategy used to select the best data examples. As such, a high degree of discernment is required from the operator.

Transductive Learning

Transductive learning can be considered a form of supervised learning, as it uses labeled data. However, it doesn’t use this to create a general function but to make predictions based on specific inputs. In this way, transductive learning doesn’t assume consistency between the training dataset and the test dataset.

Naturally, transductive learning is limited in that it’s not suitable for generalizing to new inputs, but is handy when the distribution of input data is prone to change. This type has similar applications to active learning, such as in the fields of natural language processing, bioinformatics, and image recognition.

Multi-Instance Learning

This type of model is used in a situation where we have groups of labeled data, but where individual inputs are unlabelled. This is often done because inputs may be duplicated, i.e. several data values may be identical.

You can think of this data as being labeled in “bags,” rather than individually. As such, multi-instance learning aims to categorize these “bags,” and is advantageous where labeling each input would be expensive or time-consuming.

This can reduce the accuracy, but the learning type is still useful for image and speech recognition, as well as medical diagnosis. The model could, for example, aim to predict if a patient has a particular disease based on their medical history as a whole.

Multi-Task Learning

In contrast to multi-instance learning, multi-task learning works with one dataset but aims to solve multiple problems at once. The objective here is to generalize more accurately across the tasks, by informing the model from the results of each task.

Multi-task learning can be used in natural language processing, where tasks often have underlying similarities. When tasks are unrelated, however, this learning type won’t be appropriate. The model must also balance between completing each task, which can lead to subpar performance. Generally, though, this makes for a more efficient model if tasks are interrelated.

Deductive Learning

When we try to determine a specific outcome from a general rule, we’re said to be using deductive reasoning. Therefore, deductive learning is concerned with achieving a specific result given a set of general premises, using logic.

This can be used in natural language processing, where we want to decipher specific meanings from unstructured text. Deductive learning is also used in knowledge-based systems, where knowledge is applied to new situations to give specialized advice.

Examples would be financial planning and medical diagnosis, where an expert may not always be available. However, deductive learning is limited to the knowledge and rules it must follow and is therefore highly dependent on this data as well as relatively incapable of handling uncertainty.

Inductive Learning

Inductive reasoning refers to when we use specific cases to predict more generalized outcomes. This can be thought of as the opposite process of deductive learning. In this way, inductive learning attempts to generalize new data from pre-existing data.

To be clear, inductive learning is used in many other types of AI learning that we’ve covered. The exception would be deductive learning.

As such, inductive learning is used in virtually all of the applications previously discussed. It’s rarely used for unsupervised learning, but it’s possible that the algorithm can cluster data using patterns it has identified in the input data.

Self-Supervised Learning

Finally, let’s discuss self-supervised learning. This is almost a hybrid of unsupervised and supervised learning. A problem that’s typically considered an unsupervised learning problem is represented as a supervised learning problem.

The approach is similar since the model is using data to make predictions. But the data comes in the form of modified input data. The initial task is then to recreate the original input data. As such, labeling data isn’t required to form predictions from the input data.

This can result in more data and resources being needed. But this is useful in situations where labeled data is scarce and unlabelled data is abundant. Most AI learning applications can make use of self-supervised learning, except for those that require a lot of labeled data.

What’s the Difference Between Narrow AI and General AI?

We’ve talked about the main types of AI learning that are being employed. So, at this stage, it would be useful to distinguish between “narrow” and “general” AI. All of the learning types mentioned here fall under narrow AI. This is because the models are designed to perform specific tasks, but can’t apply this knowledge to completely new areas.

On the other hand, general AI aims to possess what’s known as general intelligence. This is often seen as close to human intelligence. In this sense, general intelligence refers to the ability to learn and then apply the skills and knowledge gained to entirely new scenarios, being able to successfully perform any task that a human would be capable of.

One of the most recent approaches is deep learning. This is where a neural network is constructed that’s based on the structure of the human brain. In a neural network, interconnected nodes mimic the brain’s neurons.

Overall, general AI is seen as one of the ultimate goals of AI development. There has been promising progress in this regard. However, many more advancements will be needed before we’re close to accomplishing general AI.

AI Learning: Wrapping Up

To conclude, there’s a huge range of learning types within the field of AI learning, which can be considered supervised, unsupervised, semi-supervised, or reinforcement learning. All types of narrow AI have their own pros and cons, so which one is suitable for you depends on the task at hand.

While all of these are impressive advancements and have their place, the overarching objective of many people in the AI field is to develop general AI, which could possess human-like intelligence. We’ll have to wait and see if we get there but, so far, the progress made in narrow AI is certainly impressive and has already shaped our technological world.

Frequently Asked Questions

What are the main types of learning in AI?

Most types of AI learning can be categorized as either supervised, semi-supervised, unsupervised, or reinforcement learning.

What's the difference between supervised and unsupervised learning?

Whereas supervised learning relies on labeled data in order to calculate a function to predict future outputs, unsupervised learning relies on an unlabelled dataset without any specific outputs.

What is reinforcement learning?

Reinforcement learning is where a model is trained to perform actions based on punishments and rewards. The model is incentivized to act efficiently due to receiving rewards when it does so.

What are the types of supervised learning?

Deductive, inductive, multi-task, and multi-instance learning are all examples of supervised learning.

What are the types of unsupervised learning?

Self-supervised learning is an example of unsupervised learning. Ensemble, transfer, and online learning can also be unsupervised but are usually supervised.

What learning type is suitable for dynamic data?

Online learning is often used for dynamic data, as the model updates its parameters as it analyzes each input as they come in, one after the other.

What's the difference between multi-task and multi-instance learning?

Multi-task learning is where a model is trained on multiple tasks simultaneously using one dataset. Multi-instance learning is where the model is learning from multiple input instances known as “bags,” rather than individual instances.

What's the difference between inductive and deductive learning?

Inductive learning is used by many types of AI learning and involves making generalized predictions from specific examples. Deductive learning, on the other hand, aims to obtain specific decisions from general rules, using logic.

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