Generative AI and Large Language Models (LLMs)
- 1. Generative AI and Large Language Models (LLMs) Prof. Roma Smart Joseph March 10, 2025)
- 2. Introduction to Generative AI What is Generative AI? AI models that generate new, original content (text, images, audio, video, etc.). Examples: DALL-E 2 (images), GPT-3 (text), Midjourney (images), RunwayML (videos), Jukebox (music). Real-world Applications: Content creation, drug discovery, design, code generation, fraud detection. How it differs from other AI approaches Focus on creation vs. prediction or classification. Unsupervised or self-supervised learning as opposed to supervised learning. Transforms noise into data and learns underlying patterns.
- 3. Types of Generative AI Models 1 GANs (Generative Adversarial Networks) Concept: Two neural networks (generator and discriminator) compete against each other to generate realistic data. 2 VAEs (Variational Autoencoders) Concept: Probabilistic models that learn a compressed latent space representation of data. 3 Autoregressive Models Concept: Predict the next token (word, pixel, etc.) based on previous tokens.
- 4. Introduction to Large Language Models (LLMs) What are LLMs? Large Language Models (LLMs) are advanced AI systems that can understand and generate human-like text. They are trained on massive amounts of text data and can answer questions, write stories, summarize information, translate languages, and even generate code. Simple Example: 🔹 You type: "Tell me a joke." 🔹 LLM responds: "Why don’t robots get tired? Because they recharge!" LLMs work like super-smart chatbots that can help with research, writing, and problem-solving. Examples include ChatGPT, Google Bard, and Claude. Training Data Examples: Common Crawl (petabytes of web data), Books3 (large collection of books), Wikipedia, code repositories. Data Cleaning and Preprocessing: Critical for model performance and safety. Tokenization: Converting text into numerical tokens for model input.
- 5. Transformer Architecture: The Engine Behind LLMs Attention Mechanism Concept: Allows the model to focus on relevant parts of the input sequence when making predictions. Encoder-Decoder Structure Encoder: Processes the input sequence and creates a contextualized representation. Key Components Positional Encoding: Adds information about the position of tokens in the sequence. Benefits of Transformers Parallelization: Attention mechanism allows for parallel processing of the input sequence.
- 6. Capabilities and Applications of LLMs Text Generation Content creation (articles, blog posts, marketing copy). Language Translation Real-time translation of text and speech. Question Answering Extracting answers from large documents or knowledge bases. Summarization Generating concise summaries of long documents or articles.
- 7. Challenges and Limitations of LLMs 1 Bias and Fairness Models can perpetuate and amplify biases present in the training data. 2 Factuality and Hallucination LLMs can generate incorrect or nonsensical information. 3 Computational Cost Training and deploying LLMs require significant computational resources. 4 Security and Misuse LLMs can be used to generate disinformation, spam, and malicious content. 5 Ethical Concerns Job displacement due to automation.
- 8. The Future of Generative AI and LLMs Continued scaling and improvement of models Larger models with more parameters and better training data. Integration with other AI modalities Combining LLMs with computer vision, robotics, and other AI systems. Democratization of access Making LLMs more accessible to developers and researchers through APIs and open-source initiatives. New applications and industries Revolutionizing healthcare, education, entertainment, and other sectors. Focus on responsible AI development Addressing ethical concerns and mitigating risks associated with LLMs.