Difference between AI, ML, LLM, and Generative AI

Toloka Team
by Toloka Team

Subscribe to Toloka News

Subscribe to Toloka News

For those who are new to the field of artificial intelligence, grasping the many complex terms associated with it can prove to be quite overwhelming. Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), and Generative AI are all related concepts in the field of computer science, but there are important distinctions between them. They have significant differences in their functionality and applications. We will take a closer look at these concepts and gain a better understanding of their distinctions further.

Empower your GenAI development

Get your expert data for Fine-tuning, RLHF and Evaluation. High-quality, for any domain, at scale.
Talk to us

Artificial Intelligence

AI belongs to the field of computer science that deals with the development of computer systems that can perform tasks that typically require human intelligence, such as speech recognition, natural language processing (NLP), text generation and translation, video, sound, and image generation, decision making, planning, and more.

AI, in general, refers to the development of intelligent systems that can mimic human behavior and decision-making processes. It encompasses techniques and approaches enabling machines to perform tasks, analyze visual and textual data, and respond or adapt to their environment. One of the key advantages of artificial intelligence is its ability to process large amounts of data and find patterns in it. AI tools are designed to make decisions or take actions based on that knowledge.

AI has applications in many fields including marketing, medicine, finance, science, education, industry, and many others. For example, in marketing it is applied to generate marketing materials, in medicine it is utilized to diagnose diseases, and in finance, it is used to analyze financial markets and make investment decisions.

There are a handful of types and classifications of AI, including one based on the so-called AI evolution. According to this hypothetical evolution classification, all forms of AI existing now are considered weak AI because they are limited to a specific or narrow area of cognition. Weak AI lacks human consciousness, although it can simulate it in some situations.

The next stage of AI development may be a conceptual (so far) form called strong AI or artificial general intelligence, endowed with human consciousness and capable of performing human tasks, constructing mental abilities, reasoning, and learning from experience. It will no longer “mimic” human behavior, it will practically become a real thinking being.

The peak of AI development may result in Super AI, which would outperform humans in all areas and may even become the cause of human extinction. But for now, this is only a hypothesis. Artificial Intelligence can also be categorized into discriminative and generative.

Discriminative and Generative AI

Discriminative and generative AI are two different approaches to building AI systems. Discriminative AI focuses on learning the boundaries that separate different classes or categories in the training data. These models do not aim to generate new samples, but rather to classify or label input data based on what class it belongs to. Discriminative models are trained to identify the patterns and features that are specific to each class and make predictions based on those patterns.

Discriminative models are often used for tasks like classification or regression, sentiment analysis, and object detection. Examples of discriminative AI include algorithms like logistic regression, decision trees, random forests and so on.

In contrast to discriminative AI, Generative AI focuses on building models that can generate new data similar to the training data it has seen. Generative models learn the underlying probability distribution of the training data and can then generate new samples from this learned distribution.

Generative AI tools are capable of image synthesis, text generation, or even music. Such systems typically involve deep learning and neural networks to learn patterns and relationships in the training data. They use that knowledge to create new content. Examples of generative AI models include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), transformer and diffusion models, and many more.

Generative AI is inconceivable without foundation models, that play a significant role in advancing it. They are large-scale algorithms that serve as the backbone of AI systems. By leveraging the learned knowledge of foundation models, generative AI systems can generate high-quality and contextually relevant content. These models have seen tremendous progress recently, allowing them to generate human-like text, answer questions, write essays, create stories, and much more.

Through the utilization of a foundational model, we have the capacity to craft more specialized and advanced models that are specifically designed for particular domains or use cases. For instance, generative AI can utilize foundation models as a core for creating large language models. By leveraging the knowledge learned from training on vast amounts of text data, generative AI can generate coherent and contextually relevant text, often resembling human-generated content.

Generative AI, which can generate new content or create new information, is becoming increasingly valuable in today's business landscape. It can be used to create high-quality marketing materials, and various business documents ranging from official email templates to annual reports, social media posts, product descriptions, articles, and so on. Generative AI can help businesses automate content creation and achieve scalability without compromising on quality. Such systems are already being incorporated into numerous business applications.

Machine Learning vs. AI

Machine Learning is a specific subset or application of AI that focuses on providing systems the ability to learn and improve from experience without being explicitly programmed. ML is a critical component of many AI systems. ML algorithms are used to train AI models by providing them with datasets containing labeled examples or historical data. The model then learns the underlying patterns in the training data, enabling it to make accurate predictions or decisions on new, unseen data. By continuously feeding data to ML models, they can adapt and improve their performance over time.

AI encompasses the broader concept of developing intelligent machines, while ML focuses on training systems to learn and make predictions from data. AI aims to replicate human-like behavior, while ML enables machines to automatically learn patterns from data.

A machine learning model in AI is a mathematical representation or algorithm that is trained on a dataset to make predictions or take actions without being explicitly programmed. It is a fundamental component of AI systems as it enables computers to learn from data and improve performance over time.

Generative AI vs. Large Language Models

Generative AI is a broad concept encompassing various forms of content generation, while LLM is a specific application of generative AI. Large language models serve as foundation models, providing a basis for a wide range of natural language processing (NLP) tasks. Generative AI can encompass a range of tasks beyond language generation, including image and video generation, music composition, and more. Large language models, as one specific application of generative AI, are specifically designed for tasks revolving around natural language generation and comprehension.

Large language models operate by using extensive datasets to learn patterns and relationships between words and phrases. They have been trained on vast amounts of text data to learn the statistical patterns, grammar, and semantics of human language. This vast amount of text may be taken from the Internet, books, and other sources to develop a deep understanding of human language.

An LLM can take a given input (a sentence or a prompt) and generate a response: coherent and contextually relevant sentences or even paragraphs based on a given prompt or input. The model uses various techniques, including attention mechanisms, transformers, and neural networks, to process the input and generate an output that aims to be coherent and contextually appropriate.

Both generative AI and large language models involve the use of deep learning and neural networks. While generative AI aims to create original content across various domains, large language models specifically concentrate on language-based tasks and excel in understanding and generating human-like text.

Large Language Models applications

Large language models can perform a wide range of language tasks, including answering questions, writing articles, translating languages, and creating conversational agents, making them extremely valuable tools for various industries and applications.

By providing prompt or specific instructions, developers can utilize these large language models as code generation tools to write code snippets, functions, or even entire programs. This can be useful for automating repetitive tasks, prototyping, or exploring new ideas quickly.

Code generation with large language models has the potential to greatly assist developers, saving time and effort in generating boilerplate code, exploring new techniques, or assisting with knowledge transfer. However, it's important to judiciously use these models in software development, validate the output, and maintain a balance between automation and human expertise.

Companies are employing large language models to develop intelligent chatbots. They can enhance customer service by offering quick and accurate responses, improving customer satisfaction, and reducing human workload.

Large language models can help businesses automate content creation processes, as well as save time and resources. Additionally, language models assist in content arrangement by analyzing and summarizing large volumes of information from various sources.

Businesses process and analyze unstructured text data more effectively with the help of large language models. They can fulfill tasks like text classification, information extraction, sentiment analysis, and more. All of this plays a big role in understanding customer behavior and predicting market trends.

Here are some popular large language models which have revolutionized many NLP tasks and have applications in chatbots, virtual assistants, content creation, and machine translation, among others:


Developed by OpenAI, GPT-4 is one of the largest publicly available LLM models. It is a language model which is an extension of the GPT-3. It has been trained on a large amount of data and has higher accuracy and ability to generate text than previous models. The system can read, analyze or generate up to 25,000 words of text. The exact number of GPT-4 parameters is unknown, but according to some researchers it has approximately 1.76 trillion of them.

GLaM (Generalist Language Model)

GLaM is an advanced conversational AI model with 1.2 trillion parameters developed by Google. It is designed to generate human-like responses to user prompts and simulate text-based conversations. GLaM is trained on a wide range of internet text data, making it capable of understanding and generating responses on various topics. It aims to produce coherent and contextually relevant responses, leveraging the vast knowledge it has learned from its training data.

BERT (Bidirectional Encoder Representations from Transformers)

Developed by Google, BERT is another widely-used LLM model with 340 million parameters. BERT is a pre-trained model that excels at understanding and processing natural language data. It has been used in various applications, including text classification, entity recognition, and question-answering systems.

LLaMA (Large Language Model Meta AI)

LLaMA (Large Language Model Meta AI) NLP model with billions of parameters and trained in 20 languages released by Meta. The model is accessible to all for non-commercial use. LLaMA has the capability to have conversations and engage in creative writing, making it a versatile language model.

Overall, the operation of LLMs involves complex computations and sophisticated algorithms to generate coherent and contextually relevant text based on the given input. Such systems have a wide range of applications, including text completion, translation, chatbots, content generation, and more.


Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), and Generative AI are all related concepts in the field of computer science, but there are important distinctions between them. Understanding the differences between these terms is crucial as they represent different vital aspects and features in AI.

In summary, AI is a broad field covering the development of systems that simulate intelligent behavior. It encompasses various techniques and approaches, while machine learning is a subfield of AI that focuses on designing algorithms that enable systems to learn from data. Large language models are a specific type of ML model trained on text data to generate human-like text, and generative AI refers to the broader concept of AI systems capable of generating various types of content.

ML, LLMs, Generative AI: these are just a few of the many terms used in AI. Gaining insight into these distinctions is essential for comprehending the unique characteristics and uses of AI, ML, LLMs, and Generative AI within the constantly changing world of technology. As the AI landscape continues to evolve, new concepts will inevitably appear and the terminology we employ to characterize these systems will transform in the future.

About Toloka

Toloka is a European company based in Amsterdam, the Netherlands that provides data for Generative AI development. Toloka empowers businesses to build high quality, safe, and responsible AI. We are the trusted data partner for all stages of AI development from training to evaluation. Toloka has over a decade of experience supporting clients with its unique methodology and optimal combination of machine learning technology and human expertise, offering the highest quality and scalability in the market.

Article written by:
Toloka Team
Toloka Team

Recent articles

Have a data labeling project?

Take advantage of Toloka technologies. Chat with our expert to learn how to get reliable training data for machine learning at any scale.

More about Toloka

  • Our mission is to empower businesses with high quality data to develop AI products that are safe, responsible and trustworthy.
  • Toloka is a European company. Our global headquarters is located in Amsterdam. In addition to the Netherlands, Toloka has offices in the US, Israel, Switzerland, and Serbia. We provide data for Generative AI development.
  • We are the trusted data partner for all stages of AI development–from training to evaluation. Toloka has over a decade of experience supporting clients with its unique methodology and optimal combination of machine learning technology and human expertise. Toloka offers high quality expert data for training models at scale.
  • The Toloka team has supported clients with high-quality data and exceptional service for over 10 years.
  • Toloka ensures the quality and accuracy of collected data through rigorous quality assurance measures–including multiple checks and verifications–to provide our clients with data that is reliable and accurate. Our unique quality control methodology includes built-in post-verification, dynamic overlaps, cross-validation, and golden sets.
  • Toloka has developed a state-of-the-art technology platform for data labeling and has over 10 years of managing human efforts, ensuring operational excellence at scale. Now, Toloka collaborates with data workers from 100+ countries speaking 40+ languages across 20+ knowledge domains and 120+ subdomains.
  • Toloka provides high-quality data for each stage of large language model (LLM) and generative AI (GenAI) development as a managed service. We offer data for fine-tuning, RLHF, and evaluation. Toloka handles a diverse range of projects and tasks of any data type—text, image, audio, and video—showcasing our versatility and ability to cater to various client needs.
  • Toloka addresses ML training data production needs for companies of various sizes and industries– from big tech giants to startups. Our experts cover over 20 knowledge domains and 120 subdomains, enabling us to serve every industry, including complex fields such as medicine and law. Many successful projects have demonstrated Toloka's expertise in delivering high-quality data to clients. Learn more about the use cases we feature on our customer case studies page.