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

Large language models represent a significant breakthrough in natural language processing and artificial intelligence, and are easily accessible to the public through interfaces such as Open AI’s Chat GPT-3 and GPT-4. Other examples include Meta’s Llama models and Google’s bidirectional encoder representations from transformers (BERT) model. IBM has also recently launched its own model series on watsonx.ai, which has become the generative AI backbone for other IBM products like watsonx Assistant and watsonx Orchestrate.

 

LLMs are designed to understand and generate text like a human, in addition to other forms of content, based on the vast amount of data used to train them. They have the ability to infer from context, generate coherent and contextually-relevant responses, translate to languages other than English, summarize text, answer questions, and even assist in creative writing or code-generation tasks. 

They can do this thanks to billions of parameters that enable them to capture intricate patterns in language and perform a wide array of language-related tasks. LLMs are revolutionizing applications in various fields, from chatbots and virtual assistants to content generation, research assistance and language translation.

As they continue to evolve and improve, LLMs are poised to reshape the way we interact with technology and access information, making them a pivotal part of the modern digital landscape.

Watch the following video to learn more about what LLMs are and how they work from IBM’s Martin Keen.

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