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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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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IBM – Getting Started with Artificial Intelligence
  • Artificial intelligence refers to the ability of a machine to learn patterns and make predictions. AI does not replace human decisions; instead, AI adds value to human judgment.

  • AI performs tasks without human intervention and completes mundane and repetitive tasks, while augmented intelligence allows humans to make final decisions after analyzing data, reports, and other types of data.

 

  • The three levels of AI include: Narrow AI, Broad AI, and General AI. Narrow AI and Broad AI are available today. In fact, most enterprises use Broad AI. General AI won’t come online until sometime in the future.

 

  • The history of AI has progressed across the Era of Tabulation, Era of Programming, and Era of AI.
  • Data can be structured, unstructured, or semi-structured. 
    • Structured data is quantitative and highly organized, such as a spreadsheet of data. 
    • Unstructured data is qualitative data that doesn’t have structure, such as medical records. It’s becoming increasing valuable to businesses. 
    • And semi-structured data combines features of both structured data and unstructured data. It uses metadata.

     

  • About 80% of all the data in today’s world is unstructured.

     

  • Machine learning has advantages compared to programmable computers. Machine learning can predict and machine learning learns!
  • Machine learning uses three methods.
    • Supervised learning requires enough examples to make accurate predictions.

    • Unsupervised learning requires large amounts of information so the machine can ask a question, and then figure out how to answer the question by itself.

    • Reinforcement learning requires the process of trial and error.

  • With AI everywhere, AI will move into all industries, from finance, to education, to healthcare.

 

  • AI can increase productivity, create new opportunities, provide deeper insights, and enable personalization.
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