https://genai.gitpull.in/index.html Details The paper “Attention is All You Need” is a famous research paper in the field of artificial intelligence, specifically in natural language processing (NLP). It introduced a new type of model called the Transformer, which has become the foundation for many modern AI systems like ChatGPT, BERT, and others. Attention is All You Need The paper “Attention is All You Need” introduces a new model called Transformer for processing sequences of data, like language. Before this, models like RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory networks) were used for tasks like translation or text generation. These models processed data one step at a time, in a sequence, which made them slow and inefficient. They also struggled with long sentences because they had trouble remembering information from far back in the sequence. Hugo en-us https://genai.gitpull.in/1-intro/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://genai.gitpull.in/1-intro/index.html What is AI? Artificial Intelligence (AI) refers to the field of computer science that aims to create machines or systems capable of performing tasks that typically require human intelligence. These tasks include reasoning, problem-solving, understanding language, and recognizing patterns. Artificial Narrow Intelligence (ANI): This is AI designed for a specific task. For example, a machine learning model used to recommend videos on YouTube is an ANI. Artificial General Intelligence (AGI): AGI refers to a theoretical form of AI that can perform any intellectual task a human can. This is still in the research phase and has not yet been achieved. Artificial Superintelligence (ASI): This is the next stage beyond AGI, where AI surpasses human intelligence in all aspects. Key Terminology Machine Learning (ML): A subset of AI where algorithms learn patterns from data to make predictions or decisions. There are three primary types: https://genai.gitpull.in/2-intro-genai/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://genai.gitpull.in/2-intro-genai/index.html What is Generative AI? Generative AI refers to a class of artificial intelligence models that generate new content, such as text, images, audio, or video. Unlike traditional AI models focused on classification or prediction, generative models create new data based on learned patterns, producing outputs similar to the input data but with variability. The ultimate goal is to produce realistic content that’s indistinguishable from human-created work. Types of Generative AI Models Large Language Models (LLMs): Text Generation: Models that generate human-like text using deep learning. Examples: GPT-4, LLaMA, Claude, Mistral, Gemini Diffusion Models: Image & Video Generation: Generate images/video from noise, refining them over multiple steps. Examples: Stable Diffusion, DALL·E, Midjourney, Sora Audio & Music Generators: Generate realistic speech, music, or sound effects. Examples: MusicGen, Jukebox, VALL-E, Bark Multi-modal Models: Can process and generate text, images, video, and audio in a single model. Examples: Gemini, GPT-4 Turbo (Vision), LLaVA Example Use Cases of GenAI 📝 Text Generation: Article generation, essay writing etc. (ChatGPT, Gemini, LLaMA) 🎨 Image Generation: Creating art, photos, or designs. (Stable Diffusion, DALL·E, Midjourney) 🎶 Audio Generation: Composing music or generating speech. (Jukebox, MusicGen) 🎥 Video Generation: Deepfake technology and AI-assisted filmmaking. (Sora, Pika Labs) 🧑‍🎨 Chatbots: Conversational agents that can interact with users. (ChatGPT, Gemini, LLaMA) Popular Generative Models GPT (Generative Pretrained Transformer): https://genai.gitpull.in/3-llm-and-text-gen/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://genai.gitpull.in/3-llm-and-text-gen/index.html How LLMs Generate Text LLMs don’t “think” like humans. They predict the most probable next word (token) based on previous words. Step 1: Convert Text to Tokens Example (Word-based tokenization): Sentence: "The cat sat on the mat." Tokens: ["The", "cat", "sat", "on", "the", "mat", "."] Example (Sub-word tokenization, used in LLaMA models): Sentence: "Artificial intelligence" Tokens: ["Art", "ificial", "intelli", "gence"] Why sub-word tokenization? Handles new words by breaking them into smaller known parts. Reduces vocabulary size, improving efficiency. Step 2: Assign Probability to Next Token Example: Predicting the next token for the phrase: "The capital of France is" https://genai.gitpull.in/4-embedding-and-vectors/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://genai.gitpull.in/4-embedding-and-vectors/index.html What Are Word Embeddings? Word embeddings are numerical representations of words in a continuous vector space. These embeddings capture the meaning, relationships, and context of words based on how they appear in text data. Why Do LLMs Need Word Embeddings? LLMs like GPT, BERT, and LLaMA work with numbers, not raw text. Embeddings convert words into numerical format so they can be processed by neural networks. Without embeddings: The model treats words like independent tokens (e.g., “king” and “queen” would be unrelated). With embeddings: The model understands relationships (e.g., “king” and “queen” are semantically close). Key Idea: Words with similar meanings will have similar vector representations in the embedding space.