This title slide is named "Key AI and Generative AI Fundamentals." Its subtitle states it covers essentials from AI basics to deployment.

Key AI and Generative AI Fundamentals

Covering essentials from AI basics to deployment.

Source: Welcome slide for 10-slide PowerPoint on AI and GenAI fundamentals

AI involves machines simulating human intelligence, while ML is its subset that learns from data without explicit programming. AI is rule-based, ML is data-driven, with examples like Siri for AI and Netflix recommendations for ML.

What is AI and ML

• AI: Machines simulating human intelligence
• ML: AI subset learning from data
• ML: No explicit programming required
• Difference: AI rule-based; ML data-driven
• Examples: AI-Siri; ML-Netflix recommendations

Neural Networks Basics slide outlines the input layer receiving data, hidden layers processing it, and the output layer producing predictions from activations. It explains forward propagation computing layer by layer and backward propagation adjusting weights via gradients.

Neural Networks Basics

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• Input layer receives data, hidden layers process it
• Output layer produces predictions from activations
• Forward propagation computes layer by layer
• Backward propagation adjusts weights via gradients

Source: Artificial neural network

Generative AI creates novel content like text, images, audio, and code by learning patterns from vast datasets, with examples including ChatGPT and DALL-E. Transformers power modern GenAI through self-attention for efficient sequence processing and long-range dependencies, as seen in GPT (decoder-only) and BERT (encoder-only), forming the foundation for LLMs.

Generative AI & Transformers

The slide stresses prioritizing clean, diverse data to build robust AI models, as high quality boosts performance. It explains tokenization as converting text into words or subwords for AI processing, noting that poor tokenization causes errors and inefficiencies.

Data Quality & Tokenization

• Prioritize clean, diverse data for robust models
• Tokenization converts text into words or subwords
• Tokens form basic units for AI processing
• High data quality boosts model performance
• Poor tokenization causes errors and inefficiencies

The Attention & Encoder-Decoder Mechanism workflow starts with the encoder processing input sequences into contextual vectors, followed by the attention mechanism weighing input elements' importance relative to each output position. The decoder then generates the output token-by-token using attention-weighted inputs, culminating in autoregressive final output production.

Attention & Encoder-Decoder Mechanism

{ "headers": [ "Stage", "Component", "Function" ], "rows": [ [ "1. Input Encoding", "Encoder", "Processes input sequence into contextual vector representations" ], [ "2. Attention Computation", "Attention Mechanism", "Weighs importance of input elements relative to current output position" ], [ "3. Output Decoding", "Decoder", "Generates output sequence token-by-token using attention-weighted inputs" ], [ "4. Final Output", "Generation", "Produces complete output by autoregressively predicting tokens" ] ] }

The slide compares pretraining and fine-tuning across key aspects like data requirements, objectives, compute needs, advantages, and disadvantages. Pretraining demands massive diverse data and high compute for broad general knowledge, while fine-tuning uses smaller task-specific data and moderate compute for high accuracy but with overfitting risks.

Pretraining vs Fine-tuning

{ "headers": [ "Aspect", "Pretraining", "Fine-tuning" ], "rows": [ [ "Data Requirements", "Massive, diverse data", "Smaller, task-specific data" ], [ "Training Objective", "General knowledge", "Specific tasks" ], [ "Compute Needs", "Very high", "Moderate" ], [ "Advantages", "Broad capabilities", "High task accuracy" ], [ "Disadvantages", "Costly, less specialized", "Overfitting risk" ] ] }

The slide outlines GANs through their Generator producing realistic data, Discriminator detecting fakes, and Adversarial Training for mutual improvement. It also covers Diffusion Models via Forward Diffusion adding noise and Reverse Diffusion denoising to yield photorealistic images.

GANs & Diffusion Models

{ "features": [ { "icon": "🤖", "heading": "GAN Generator", "description": "Competes to produce realistic synthetic data." }, { "icon": "👮", "heading": "GAN Discriminator", "description": "Identifies fakes to challenge the generator." }, { "icon": "⚔️", "heading": "Adversarial Training", "description": "Mutual improvement yields high-quality outputs." }, { "icon": "➕", "heading": "Forward Diffusion", "description": "Progressively adds Gaussian noise to images." }, { "icon": "➖", "heading": "Reverse Diffusion", "description": "Denoises step-by-step to generate images." }, { "icon": "🖼️", "heading": "Photorealistic Results", "description": "Creates detailed, high-fidelity visuals." } ] }

This slide outlines prompting techniques for LLMs as efficient alternatives to full fine-tuning: zero-shot (direct instructions without examples), few-shot (examples to guide behavior), and Chain-of-Thought (step-by-step reasoning for better performance). These in-context learning methods enable quick adaptation without retraining the model.

Fine-tuning LLMs (Zero-shot, Few-shot, CoT)

• Zero-shot prompting: Direct instruction without examples
• Few-shot prompting: Few examples guide model behavior
• Chain-of-Thought (CoT): Step-by-step reasoning boosts performance
• Efficient in-context learning vs. full fine-tuning

The slide titled "Ethics & Bias in AI" features a quote from AI pioneer Fei-Fei Li, Co-Director of Stanford’s Human-Centered AI Institute. The quote states: "AI amplifies human intent – good and bad."

Ethics & Bias in AI

> AI amplifies human intent – good and bad.

— Fei-Fei Li, Co-Director of Stanford’s Human-Centered AI Institute and AI Pioneer

Source: Fei-Fei Li

This conclusion slide, titled "Cloud Deployment & Project Presentation," highlights the main message: "Master AI: Deploy, Scale, Present Confidently." The subtitle calls to action with: "Contact us to launch your AI project today."

Cloud Deployment & Project Presentation

Master AI: Deploy, Scale, Present Confidently.

Contact us to launch your AI project today.