Course Content
Introduction to Artificial Intelligence
About this learning activity Less than a century old, artificial intelligence (AI) has already undergone three waves of transformative development. Today it gives humanity the most powerful tools for analyzing complex data, not only to find meaning but to learn without human intervention. In this course, you'll survey AI's history and explore ways that it can shed light on unstructured data. What you'll learn After completing this course, you should be able to: Define artificial intelligence Describe three levels of artificial intelligence Describe the history of AI from the past to the possible future Define and describe machine learning Differentiate between structured and unstructured data Describe how machine learning structures data Describe how machine learning structures unstructured data Describe how machine learning uses probabilistic calculation to solve problems Describe three methods by which machine learning analyzes data Describe an ideal relationship between humans and machine learning
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About this course
Course design
Module 1 What is artificial intelligence?
What is AI?
Reflection: What do you think?
What is the difference between AI and augmented intelligence?
What does AI do?
What predictions can AI make?
How is AI evolving?
Match each scenario to the level of AI.
Quiz
Introduction to Large language models
Welcome to Introduction to Large Language Models! In this module, you'll learn what large language models are, how they work, and some typical business applications. Estimated duration 30 minutes
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Learning objectives
About this lesson: Large language model basics
What are large language models?
How large language models work
Quiz LLM
Points to remember
Explore more IBM resources!
IBM References
IBM – Getting Started with Artificial Intelligence

A large language model is a type of artificial intelligence (AI) model that is trained on a large data set of text to generate language outputs that are coherent and natural-sounding. 

  • Large refers to the enormous scale of the model that gives them their nuanced understanding and capability.
  • Language refers to how they are designed to understand and interact using human languages.
  • Model refers to the computational model or algorithms that processes the input and produces the output.1

Large language models (LLMs) are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks.2

 

It might seem like large language models have arrived out of the blue along with new developments in generative AI. However, many companies, including IBM, have spent years implementing LLMs at different levels to enhance their natural language understanding (NLU) and natural language processing (NLP) capabilities. This has occurred alongside advances in machine learning, machine learning models, algorithms, neural networks and the transformer models that provide the architecture for these AI systems.

LLMs are a class of foundation models, which are trained on enormous amounts of data to provide the foundational capabilities needed to drive multiple use cases and applications, and resolve a multitude of tasks. This is in stark contrast to the idea of building and training domain-specific models for each of these use cases individually, which can be expensive and time consuming. 

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