Artificial Intelligence Technologies: What’s What

Artificial intelligence technologies are methods and systems that enable programs to perform tasks requiring data processing and pattern recognition. Machine learning is one approach to building such systems, a neural network is a type of model, and generative AI is a technology for creating new text, images and other content.

These concepts are often conflated, even though they describe different levels and ways of working. Understanding what each one means helps explain why some tools classify information, others learn from examples, and still others generate content.

• 3 levels How the terms relate: AI → machine learning → neural networks
• 2 approaches A system can combine learning from data with predefined rules
• 3 task types Examples of neural network applications: images, speech and complex predictions

What are artificial intelligence technologies?

Artificial intelligence is a broad field of computer science concerned with developing systems that can perform tasks usually associated with human thinking. AI is not the name of a single algorithm or product: the field includes machine learning and systems that operate according to predefined rules.

How AI, machine learning and neural networks relate

Machine learning is a branch of AI: an algorithm finds patterns in data and uses them, for example, to make predictions. Neural networks are one approach to machine learning; they process data through layers of artificial neurons and are used, among other things, to recognize images and speech.

Predefined rules and learning from data are not mutually exclusive: a single AI system can combine both approaches. That is why it is important to distinguish a service’s purpose from how it is implemented: “speech recognition” describes a task, while machine learning or a set of rules describes the technology that helps perform it. For example, a voice assistant might use a neural network to process speech and separate rules to carry out commands.

How is machine learning different from a regular program?

Rules versus patterns

A regular program follows rules set by a developer, while a machine-learning model finds patterns in examples and uses them to make predictions. For instance, to sort objects into categories, a regular program could be given separate conditions for each case, while a model could be trained on examples of objects with known categories.

The key difference is learning from data: the model infers the relationship between an object’s features and its category from the examples it has analyzed, rather than receiving a separate rule from the developer for every object. If a system operates only according to fixed instructions and does not learn from data, it is not a machine-learning model. A program itself can, however, combine both approaches: following predefined rules and using a trained model.

• Regular program: The developer sets the conditions for assigning an object to a category.
• Machine-learning model: Using examples with known categories, it identifies the relationship between features and outcomes, then applies it to new objects.
• How to tell if it uses machine learning: The system learns from data; fixed instructions without such training are not enough.

Machine learning is one method within the broader field of artificial intelligence, not another name for AI. A rule-based system can therefore be considered an AI solution, but without learning from data, it cannot be called a machine-learning model.

What is a neural network, and why is it a form of machine learning?

A neural network is a type of machine-learning model. It consists of layers of artificial neurons that process input data in sequence and find complex patterns in it. Its design is inspired by how the human brain works, but a neural network is a computational model, not an exact copy of the brain.

How neural networks work and what they are used for

In a neural network, data passes through a sequence of layers: each layer transforms the information it receives, and subsequent layers help identify more complex features. For example, when processing an image, a model can consider combinations of elements to recognize an object; when processing speech, it can analyze the audio signal.

Neural networks are used to process images and speech. They form the basis of voice assistants and facial-recognition systems. Machine learning, however, is broader: it includes different ways of building models, and not every model is structured as a neural network. So it is accurate to consider a neural network one type of machine-learning model, not a synonym for all machine learning.

How is a language model different from a neural network?

Purpose and structure are not the same thing

A language model describes what a system does with language, while a neural network describes how a model is structured: it is a computational system made up of layers of artificial neurons. The terms therefore belong to different levels of classification: one describes purpose, the other a type of model.

A language model can be implemented as a neural network. For example, a service that takes in text and generates a response performs a language task; the term “language model” alone does not tell you what other components the service includes or exactly how it is structured. To understand a familiar AI tool, check two criteria:

• Task: Does the system process text, recognize speech or perform another operation?
• Training: Does the model learn from data? Machine learning uses data to find patterns; a neural network is one type of model used in machine learning.

The word “neural network” does not by itself mean that a system works with language: neural networks are also used to process images and speech and to make predictions. Conversely, the term “language model” indicates an area of application but does not fully reveal the service’s architecture. Separate the question “What does the system do?” from “How is its model structured?” to avoid confusing its purpose with its technical type.

How can you identify the technology behind an AI service?

To identify the technology behind an AI service, first name its function, then state only the type of model that has been confirmed: voice input alone does not prove that the entire service is built on a neural network. A service may combine a model, rules and other software components; without information about its design, avoid describing its architecture in more specific terms.

• Voice assistant: Its function is to receive and respond to voice commands; it may use a neural network to process speech. A voice interface alone does not reveal which technologies are used for the assistant’s other tasks.
• Facial recognition: This is an example of using neural networks to process images. This description is appropriate when the model type has been confirmed, rather than inferred solely from the fact that the service works with photos.
• Data-based prediction: It is appropriate to talk about machine learning if the system uses a trainable model to analyze data and make predictions. Adding fixed rules to a model does not change the fact that the system uses machine learning.

Distinguish between the function and technology levels: “recognizes faces” describes a task, while “uses a neural network” describes how it is performed. If the service’s architecture has not been disclosed, stick to a verifiable description of its function and do not call the model a neural network without evidence.

When can’t you confidently call a service a neural network?

What you can tell from its function

The ability to answer a text prompt alone is not enough to confidently call a service a neural network: its outward behavior does not reveal how the system is structured internally. Its response could be generated by different components, such as a natural-language processing module and a set of predefined rules.

The terms describe different levels and are not interchangeable. AI is a broad field, machine learning is one approach within it, a neural network is a type of machine-learning model, and a language model is designed to work with text. To describe a service more precisely, it is important to distinguish its function from its technology.

What a function does not prove

A text conversation alone does not prove that a language model or neural network is behind it: the interface may combine several modules. Conversely, a service with a neural-network component may perform a task that does not look like conversation to the user.

• AI denotes a broad category of solutions, not a specific architecture.
• Machine learning describes an approach in which patterns are extracted from data.
• Neural network is a specific type of model, not the name of an entire service.
• Language model indicates work with language but does not necessarily describe the product’s other components.

Without information about how it was developed, it is more accurate to name the observable task—for example, “the service answers questions about text”—rather than attribute a specific technology to it. Confident classification requires information about which components are used and what role they play.

Frequently asked questions

Written byDmitriy Lastochkin

Дмитрий специализируется на программном обеспечении и приложениях, исследуя их функциональность и влияние на повседневную жизнь. Его подход основан на тщательном анализе и тестировании, чтобы предложить читателям самые актуальные и полезные решения.