Notes from Introduction to Generative AI Session
Welcome and Introduction
- Host: Live session at the Reactor, organized by the London Business Analytics Group.
- Speaker: Corey, involved with Microsoft’s generative AI curriculum.
- Code of Conduct: Respectful engagement and participation encouraged.
Speaker Introduction
- Mark introduces Corey and mentions upcoming in-person events.
- Events in June and July:
- Power BI talk by Prakash Jain (June)
- Excel talk by Alan Murray (July)
Overview of Generative AI
- Corey’s Brand: Apologizes for potential video lag and background noise.
- Audience Engagement: Check-in on audience experience with generative AI using emojis:
- Starry-eyed (excited)
- Confused
- Sad (feeling behind)
- Generative AI Definition: Aims to cater to varying levels of experience in the audience.
Generative AI Reactions
- Starry-eyed: Excited and engaged.
- Confused: Uncertainty about where to start.
- Sad: Feeling behind in knowledge.
Importance of Generative AI
- Current Trends: Rapid advancements mainly due to technologies like ChatGPT.
- Three Key Areas to Learn About Generative AI:
- Ubiquity: Generative AI is prevalent in various applications.
- Multi-modality: Integration with different data types (text, images, audio).
- Autonomy: Maintaining user autonomy while utilizing generative AI tools.
Applications of Generative AI
- Practical Uses in Business:
- Content Generation: Enhanced customer service responses, blog posts, and more.
- Code Generation: Tools like GitHub Copilot facilitating code creation for developers and non-developers.
- Semantic Search: Efficient retrieval of information from both structured and unstructured data.
- Summarization: Quick insights from reports, logs, and meeting notes.
Intelligent Applications
- Definition: Applications enhanced with generative AI capabilities for better user interaction and data processing.
- User Interaction: Natural language queries leading to personalized experiences.
Getting Started with Generative AI
- Value vs. Complexity Chart:
- Summarization and Q&A are easy to implement and provide high value.
- Automation offers high value but comes with increased complexity.
Tools and Technologies
- Overview of tools available for developers:
- Microsoft Fabric, Power Platform for fast application development.
- Orchestration tools (e.g., Prompt Flow, Semantic Kernel) for managing model interactions.
- Importance of model selection based on use cases and performance.
Prompt Engineering
- Importance: Crafting effective prompts is essential for successful interactions with generative AI models.
- Key Techniques:
- Context: Providing roles and specific goals.
- Constraints: Formatting, length, and prioritization in responses.
Responsible AI Usage
- Human-Centric Approach: Prioritizing user interests in application development.
- Impact Monitoring: Continuous evaluation of model behavior and user interactions.
- Addressing Potential Harms: Awareness of issues like hallucinations, bias, and harmful content.
Security Considerations
- Challenges: Prompt injection, supply chain vulnerabilities, and over-reliance on AI outputs.
- Mitigation Strategies: User education, input validation, and monitoring user interactions.
Search Applications: Retrieval-Augmented Generation (RAG)
- Concept: Enhancing model responses with relevant and dynamic data sources.
- Benefits: Provides personalized responses based on current data rather than outdated training data.
AI Agents
- Emerging Concept: AI models executing tasks autonomously rather than just providing information.
- Applications: Scheduling meetings, executing actions based on user requests.
Community Engagement and Resources
- Encouragement to join the AI community Discord for ongoing learning and support.
- Mention of a GitHub repository with a generative AI for beginners course and lessons available for users and developers.
Conclusion
- Corey’s Contact: Available on social media and Discord for questions.
- Mark’s Closing Remarks: Thanked Corey and the audience for participation.
- Future Talks: Information about more events in the pipeline.
Note: For further details, refer to the GitHub repo mentioned during the session.