Unleashing Innovation: The Generative AI Revolution

Overview

We find ourselves in a society where the line between what is created by humans, and what is created by machines, is increasingly blurred. Generative AI has been a turning point in how we create, design, and interact with technology. But how does it work, what are the benefits beyond novelty and what are the risks?

Join us for an approachable introduction into how Generative AI works in a no-nonsense, understandable way; and how we can use this technology not just as a stand-alone tool, but in collaborative partnership to responsibly drive innovation and transformation.

• Course level: Fundamental
• Duration: 2 hours

Activities

This course includes presentations based on practical examples, with use-cases and demonstrations.

Course objectives

By the end of this session, attendees will be able to:

• Define artificial intelligence, machine learning, and the three types of machine learning
• Describe how machine learning algorithms learn and subsequently output a machine learning model
• Understand the difference between traditional machine learning algorithms and deep learning algorithms
• Describe how artificial neural networks work
• Understand the difference between discriminative (predictive) AI and generative AI
• Describe how Large Language Models (LLMs) are trained and used for text generation
• Understand the purpose and importance of Foundation Models (FMs) and how prompt engineering and fine-tuning can be used to customize FMs.
• Describe how diffusion models are trained and used for image generation
• Describe the practical applications of generative AI
• Identify issues surrounding responsible and inclusive use of generative AI

Intended audience

This course is intended for:

• Non-technical enthusiasts
• Technical enthusiasts
• Decision makers

Prerequisites

None

Course outline

Introduction

• This section provides an overall context of the current state of the artificial intelligence and machine learning landscape. It starts from the basics with a definition of artificial intelligence before drilling down into machine learning and the three main types of machine learning: supervised, unsupervised, and reinforcement learning. There is a high-level discussion about how these enable artificial intelligence and machine learning to actually learn, which leads into comprehensive coverage about deep learning and artificial neural networks - the technologies which underpin generative AI.
• What is artificial intelligence?
  • What is machine learning?
    • Supervised learning
    • Unsupervised learning
    • Reinforcement learning
• How does machine learning learn?
• What is deep learning?
  • Artificial neural networks

Generative AI

In this section we dive deep into two of the most popular, and well-known, forms of generative AI: text generation and Natural Language Processing, and image generation and diffusion models.

• Text generation and Natural Language Processing
• What is a Large Language Model?
• How does this differ from past approaches?
• Transformer architectures
• Conditional text generation
• Developing Large Language Models (LLMs)
• Demo: Foundation Models with Amazon SageMaker JumpStart
• Image generation and diffusion models
• Diffusion models
• Training diffusion models
• Effective diffusion models
• Improving noise prediction and removal

  Syllabus

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  Taught by

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  Tags

  united states