This outline is designed for a presentation to the Lexington Computer Group, incorporating the topics and suggestions discussed in recent emails. It emphasizes practical demonstrations and a comparison of current AI tools, while also delving into more advanced concepts.

• Briefly introduce the purpose of the presentation: to explore the landscape of current AI engines and discuss their capabilities and implications.
• Acknowledge the group's interest in AI and the collaborative nature of this topic (as suggested by recent email discussions).

• Acknowledge the rapid evolution of AI and the need to understand its various facets.
• Briefly touch upon the presenters' journey into understanding AI, perhaps mentioning the BOLLI AI course.

• To provide a comparison of popular AI chatbots.
• To demonstrate practical applications and advanced features of AI.
• To foster discussion and shared learning within the group.

• Briefly introduce Chat GPT, Claude, and Gemini.
• Highlight their general purpose and key differentiating philosophies (if any).

• Interactive Demos: For each chatbot, perform a standard set of tasks (as suggested by Harry Forsdick).
  • Example Tasks:
    • Generating creative text (e.g., a short story, a poem).
    • Summarizing a complex article.
    • Answering factual questions.
    • Performing a simple coding task or debugging.
• Discussion of Strengths (from experience):
  • Share personal insights on which chatbot excels in specific areas (e.g., creativity, factual accuracy, coding, conversational flow).
  • Discuss user interface and ease of use.
• Sentiment Analysis Comparison (Peter Albin's contribution):
  • Briefly present findings or a live demonstration of how different AI engines respond to sentiment analysis queries, as Peter Albin explored with Perplexity.

• Concept Explanation: Use visualizations to explain what RAG is and why it's important for overcoming AI hallucinations and providing more accurate, grounded responses.
• Demonstration: Show an example of an AI (e.g., Gemini) leveraging RAG to answer a question by citing specific sources or external knowledge.

• Concept Explanation: Discuss how AI can be used for in-depth research, going beyond simple search queries.
• Demonstration: Showcase a "Deep Research" session (as John Day mentioned using Gemini for the energy consumption report).
  • Walk through the process of posing a complex query.
  • Show how the AI gathers and synthesizes information from multiple sources.
  • Present the results and discuss their utility.

• Concept Explanation: Address the challenge of ambiguity in generative AI responses and how programmatic approaches can help refine and control outputs.
• Demonstration: Provide a simple example of using AI for programming tasks, perhaps demonstrating how to refine a prompt or use structured output to get a more precise response.

• Open the floor for questions from the group.
• Encourage attendees to share their own experiences with AI tools.
• Discuss potential future topics for the group related to AI.

• Briefly touch upon the ongoing advancements in AI and what the future might hold.
• Emphasize the importance of continuous learning in this rapidly evolving field.

• Reiterate the main points: AI tools offer diverse strengths, and advanced techniques like RAG and deep research enhance their utility.
• The importance of hands-on experience and critical evaluation.

• Thank the audience for their participation.
• Suggest resources for further exploration (e.g., specific AI platforms, online courses).
• Invite continued discussion within the Lexington Computer Group.

Visuals & Style Notes:

• Use a mixture of high-level visualizations for concepts and live, on-line demonstrations (heavy emphasis on demos).
• Keep explanations concise and focus on illustrating capabilities through practical examples.
• Maintain an engaging and interactive tone.