AI, Machine Learning, GenAI, and Large Language Models – What's the Difference?

Understanding the rapidly evolving world of AI can feel overwhelming. Terms like Artificial Intelligence (AI), Machine Learning (ML), Generative AI (GenAI), and Large Language Models (LLMs) are often used interchangeably, but they represent distinct, though interconnected, areas of technology.

Part of the confusion surrounding AI is that the term is used both generically and specifically, and appended to many different things. This is particularly confusing in the customer support domain because, as analyst Teresa Cottam explained in her recent report for the TM Forum on AI in customer service, “Digital customer service is being transformed by the application of multiple types of AI which work hand in hand.”

Let's simplify these concepts.

Artificial Intelligence (AI)

AI is a broad field where computers or systems perform tasks that typically require human intelligence, such as decision-making, visual perception, speech recognition, and language translation. AI can range from simple algorithms to complex systems.

Example: Telecom customer support virtual assistants that handle basic troubleshooting and billing queries automatically.

Machine Learning (ML)

ML is a specific subset of AI that involves training algorithms on data to recognize patterns, enabling them to make decisions or predictions without explicit instructions.

Example: Predictive analytics in telecom that proactively identifies network faults or customer churn risks based on historical data.

Generative AI (GenAI)

Generative AI is a subset of AI focused on creating new content, such as text, images, or music, by learning from existing data. GenAI models predict or generate outputs based on learned patterns.

Example: Generating personalized support content or troubleshooting steps tailored to individual telecom subscribers based on their specific account and network usage.

Large Language Models (LLMs)

LLMs are specialized types of Generative AI trained on extensive datasets to understand, generate, and interact with human language effectively. They excel at text-based tasks and conversations.

Example: AI-driven telecom chatbots providing natural-language customer support, able to understand context and manage detailed service interactions.

How are they connected?

Think of AI as medicine: it's a broad field. ML is like cardiology — one specialized area. Generative AI is a further specialization, like heart surgery, and LLMs specifically are advanced surgical techniques within that specialization.

Common misconceptions

• Not all AI is ML: ML is just one way to achieve AI.

• GenAI ≠ LLMs exclusively: LLMs are one powerful application within Generative AI, but not the only one.

• LLMs have limitations: They can generate highly plausible yet incorrect information, known as "hallucinations."

How does GenAI work?

GenAI uses two main techniques – machine learning and deep learning – within two core components. The first of these is known as a ‘generator’ and the second as a ‘discriminator’, which work together in a system called a Generative Adversarial Network (GAN).

• The generator creates new, artificial content based on its analysis of a large dataset.

• The discriminator examines the generator’s output and determines whether it resembles the original data closely enough.

• The generator tries to improve its creations based on the feedback it gets from the discriminator.

  
  

This feedback-loop way of learning, mimicks how humans learn and results in an ongoing process of refinement that produces higher-quality, more convincing, artificial content.

GenAI learns to do this by adjusting settings called ‘parameters’. The more parameters the model has, the more detailed and specific the output will be. That’s what the ‘large’ refers to in ‘large language model’ – where the model has millions or even billions of parameters to generate outputs. This process of adjusting parameters through discovering patterns in datasets is called ‘training the model’. And this is why the quality of the data set matters a lot. Without high-quality data, the model might be trained to become specific, but still not be accurate.

Want to know how these techniques are being used in customer support? Check out our next post.