Understanding Generative AI: Generative AI refers to systems that can create content autonomously, such as text, images, or music by learning from existing data. Measuring this skill ensures that candidates can grasp the foundational concepts and mechanisms driving these AI systems.
Applications of Generative AI: This skill involves identifying and utilizing various use cases for generative AI, including creative industries, healthcare, and automated content generation. Assessing this helps gauge the candidate's ability to apply generative AI in real-world scenarios.
Ethics in Generative AI: Ethics in generative AI deal with the moral implications, bias mitigation, and responsible use of AI-generated content. Understanding this is crucial for creating technology that aligns with societal values and avoids harmful consequences.
Popular Generative AI Models: Knowledge of popular generative AI models like GPT, StyleGAN, and VQ-VAE is pivotal for implementing and experimenting with state-of-the-art technologies. Candidates must be familiar with the strengths and limitations of these models.
Generative AI Algorithms: This skill encompasses the algorithmic foundations of generative models, such as variational autoencoders (VAEs) and Generative Adversarial Networks (GANs). Mastery here is vital for understanding how generative AI creates new data from existing datasets.
Data Preprocessing for Generative AI: Data preprocessing involves cleaning, normalizing, and organizing data to feed into generative models. Proper preprocessing is essential for the accuracy and efficiency of the AI outputs.
Evaluating Generative AI Outputs: This involves techniques and metrics to assess the quality and reliability of content produced by generative AI. It ensures that the generated outputs meet desired standards and objectives.
AI in Art and Creativity: AI's role in art and creativity includes generating original artwork, music, and literature through learned patterns. This skill highlights AI's innovative potential in augmenting human creativity.
Language Generation: Language generation focuses on creating coherent and contextually relevant texts using models like GPT-3. It's a critical skill for developing applications in natural language processing (NLP) and content automation.
Generative Adversarial Networks: GANs consist of two neural networks, a generator and a discriminator, that work together to produce realistic synthetic data. Knowledge of GANs is fundamental for creating high-quality generated content and advancing AI research.
