Introduction-to-Generative-AI-CATL-Presentation-Slides.pptx

introduction to generative ai. Generative AI is a type of artificial intelligence that creates new content, such as text, images, music, and videos, based on existing data. It learns patterns from data and then generates new, original content that resembles the training data. oundation Models: Generative AI is often powered by large AI models called "foundation models" that can perform various tasks like summarization, Q&A, and classification. Training Data: Generative AI models are trained on vast amounts of data, allowing them to learn the underlying structures and relationships within the data. Content Generation: Once trained, these models can generate new content based on user input or prompts. Deep Learning: Generative AI often utilizes deep learning, neural networks, and machine learning techniques to enable computers to produce content that resembles human-created output. Examples of Generative AI Applications: Text Generation: Creating articles, poems, scripts, and other forms of text. Image Generation: Producing new images based on text descriptions or other inputs. Music Generation: Composing original music pieces. Code Generation: Creating code snippets or programs. Video Generation: Producing new videos. Tools and Technologies: Large Language Models (LLMs): These are a type of AI model trained on large amounts of text data to understand and generate human-like text. Variational Autoencoders (VAEs): These models can generate new data by learning from existing data, using encoders and decoders to transform and recreate data. Generative Adversarial Networks (GANs): These models use two neural networks, a generator and a discriminator, to generate increasingly high-quality content. Diffusion Models: These models use forward and reverse diffusion processes to transform data into a latent space representation and generate new content. Industries and Applications: Marketing: Creating advertising campaigns, social media content, and other marketing materials. Entertainment: Generating new movies, video games, and other forms of entertainment. Education: Creating educational materials, quizzes, and personalized learning experiences. Design: Designing logos, websites, and other visual elements. Healthcare: Generating synthetic patient data, helping in drug discovery, and more.