Final Exam: Demystifying Generative AI

WHAT YOU WILL LEARN

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  Define generative ai and its key components
  

  differentiate between generative ai and other ai methods, showcasing their unique features and use cases
  

  identify the fundamental principles and theories that drive generative ai
  

  outline the various tools, technologies, platforms, and communities that comprise the generative ai ecosystem
  

  describe generative models like gans (generative adversarial networks) and vaes (variational autoencoders), including their structure and function
  

  outline the potential biases, ethical considerations, and societal impact when designing and using generative ai
  

  identify popular types of generative models
  

  outline how generative models are trained using images and practical examples
  

  outline bayes theorem examples of prior probability, posterior probability, likelihood, and evidence
  

  recognize the differences between generative and discriminative modeling
  

  provide an overview of generative adversarial networks (gans) and their groundbreaking applications
  

  define large language models and outline their significance in the world of artificial intelligence (ai)
  

  outline the key components and general architecture that make up typical llms
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  contrast the pre-training methods used in training llms
  

  use a pre-trained language model for text generation and recognize its constraints
  

  analyze the architectural components of large language models (llms) and their role in generative artificial intelligence (ai)
  

  implement techniques to fine-tune large language models for specific generative tasks
  

  analyze the challenges and limitations of large language models in generative ai applications
  

  compare and contrast various techniques for controllable and conditioned text generation using large language models
  

  identify methods for handling bias and promoting fairness in large language models for generative ai
  

  define the concept of generative artificial intelligence (ai) and its significance in the business landscape
  

  identify key challenges and opportunities associated with the adoption of generative ai in different industries
  

  outline the various algorithms and techniques used in generative ai for business applications
  

  identify the risks and limitations of generative ai implementation and strategies to mitigate them
  

  develop a strategic roadmap for incorporating generative ai into a business's digital transformation journey