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Generative AI Demystified: Exclusive for Business Leaders

1. Introduction to Generative AI

   - Overview of Generative AI Foundations

   - Importance of Generative AI for CTOs


2. Generative AI Algorithms Demystified

   - Introduction to Large Language Models (LLMs)

   - Different types of AI: Narrow-purpose vs. General-purpose

   - Balancing intent alignment in AI models


3. Opportunities with Generative AI

   - Identifying opportunities for AI applications

   - Labeling, training, and deploying AI solutions

   - Narrow-purpose vs. General-purpose AI models


4. Custom Comparison and Custom Demonstration

   - Evaluating generative AI solutions

   - Conducting custom comparisons and demonstrations

   - Frameworks to assess AI solutions


5. Environments for Generative AI

   - Understanding different environments for AI solutions

   - Proprietary, Open-source, Cloud, On-prem, Edge

   - Prompt Engineering, Plugins, and Model Fine-tuning


6. Large Language Models (LLMs)

   - Overview of LLMs like GPT-4, LaMDA, PaLM, Alpaca

   - Comparing LLMs with Chatbots

   - LLM Leaderboard and advancements in the field


7. From Data to ChatGPT

   - ML training process for LLMs

   - Base LLM and Chatbot models

   - Training and inference steps for LLMs


8. AI Intent Alignment and Risk Management

   - Understanding intent alignment in AI models

   - Risk management techniques for safe AI solutions

   - Addressing biases and behavioral issues in AI models


9. Evaluation Frameworks for Generative AI

   - Frameworks to evaluate and benchmark AI solutions

   - Importance of human evaluations and limitations

   - Benchmarking, red teaming, and risk analysis


10. Generative AI MLOps

    - Overview of MLOps for generative AI

    - Development, deployment, and maintenance lifecycle

    - Monitoring, retraining, and risk control mechanisms

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