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Meeting Summary: CS 486-686 Lecture/Lab Spring 2025

Date: February 4, 2025, 08:10 AM Pacific Time (US and Canada)
Meeting ID: 893 0161 6954

Quick Recap

The session was led by Greg, who guided a discussion on generative AI and large language models (LLMs). The presentation covered:

  • OpenAI’s Deep Research: Its potential for agentic problem-solving.
  • Models as Functions: Understanding models as complex neural networks that process inputs and produce outputs.
  • Coding Assistance Tools: Demonstrations on how such tools can assist with tasks like information extraction and summarization.
  • Problem Decomposition: The importance of breaking down larger problems into manageable steps.
  • Experimentation: Encouraging the class to explore different approaches and tools.
  • Future Plans: Outlining upcoming class activities and additional resources.

Next Steps

  • All students must get their render.com deployment working before the next lab session.
  • Students should brainstorm a theme for their chat assistant project.
  • Read the assigned paper before Thursday’s discussion.
  • Greg will share the GitHub repository for the summary enhancer tool.
  • Experiment with different approaches for embedding prompts in code.
  • Prepare discussion questions or points regarding the assigned paper.
  • Greg will add book recommendations and other resources to the class website.
  • Continue working on chat assistant projects during tomorrow’s lab session.

Detailed Discussion Topics

1. Exploring Generative AI and Agents

The session began with a review of recent innovations in generative AI. Key topics included:

  • Industry Developments: Sharing exciting advancements and updates.
  • Deep Research Release: Discussion of OpenAI’s new product available to pro subscribers.
  • Benchmark Challenges: Introduction of the HLE challenge as a potential leap forward.
  • Agents vs. Models: An exploration of the emerging, yet indistinct, boundaries between autonomous agents and traditional language models.

Mermaid Diagram: Generative AI Workflow

flowchart TD
    A[Explore Innovative Ideas] --> B[Introduce LLM Concepts]
    B --> C[Discuss OpenAI's Deep Research]
    C --> D[Benchmark Challenge: HLE]
    D --> E[Explore Agent vs. Model Distinctions]

2. Model Functionality and Reasoning Levels

Greg explained that models function as complex neural networks that transform inputs into outputs. The discussion emphasized:

  • Structural Complexity: The architectural facets of neural networks.
  • Hardware Investments: Efforts by companies like OpenAI and Amazon to build custom training and inference hardware.
  • Agents vs. Pure Models: Clarifying that while agents leverage LLMs, they are more than just language models.
  • Tool Calling: The practice of interacting with models in a formalized way.
  • Reasoning Levels:
    • Low Reasoning: Faster and more cost-effective.
    • High Reasoning: More token-intensive, slower, and expensive.

3. News Sources and Paper Evaluations

Key points in this section included:

  • Reliable Sources: Greg’s primary information channels (e.g., Hacker News, OpenAI updates).
  • Tool Limitations: Discussion of DeepSeek’s limitations in handling certain topics due to censorship.
  • Paper Evaluation Guidelines:
    • A brief summary is required.
    • Three discussion questions must be prepared.
  • Lab Expectations: Encouraged collaboration and repository access for lab activities.

4. Exploring LLMs for Data Processing

Greg highlighted how LLMs can be leveraged for data processing by:

  • Backend/Frontend Separation: Structuring services to optimize data workflows.
  • Sequential Processing: Breaking down challenges into a sequence of LLM calls.
  • Local vs. Cloud: Considering local LLM implementations to avoid dependencies on cloud providers.

5. LLM Applications and Image Encoding

The discussion also covered innovative uses of LLMs:

  • Light LLM Applications: Demonstrated potential applications such as a chat assistant.
  • Multimedia Messages: Constructing messages with text and images.
  • Base64 Image Encoding: Process of encoding images locally, which was demonstrated using a cat image.

Python Example: Base64 Image Encoding

import base64

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        encoded = base64.b64encode(image_file.read()).decode("utf-8")
    return encoded

if __name__ == "__main__":
    image_path = "cat_picture.jpg"  # Ensure this path is valid
    encoded_image = encode_image(image_path)
    print("Encoded image (first 100 chars):", encoded_image[:100] + "...")

6. Coding Assistance Tools and LLM Integration

The integration of coding assistance tools with LLMs was another focus of the session. Points discussed include:

  • Tool Integration: Using platforms like Root Code along with the VS Code LLM API.
  • Balancing Act: Balancing programming fundamentals with the efficiency gains from these coding tools.
  • Feedback Request: Gathering feedback on tool effectiveness and user experience.

7. Exploring Open Router and OpenAI

Discussion points in this section included:

  • API Utilization: The potential of using Open Router and OpenAI APIs for developing applications.
  • Tier Verification: Validating individual API tiers using personal API keys.
  • Resource Sharing: Plans to share a GitHub repository for the summary enhancer tool.
  • Recommended Reading: The book “LLM Applications: Building LLM Applications” by Chip Huan was recommended as a useful resource.

8. Language Models and Automation Impact

The conversation touched on the broader impact of LLMs on industries such as:

  • Optical Character Recognition (OCR): Automating contract extraction and reconciliation.
  • Multi-Step Applications: Demonstration of the “Summary Enhancer” tool that:
    • Extracts key concepts.
    • Converts text summaries into Markdown.
    • Changes the voice from straightforward text to a more dynamic summary.

9. Exploring Markdown and LLM Integration

The final part of the discussion focused on combining Markdown with LLMs:

  • Markdown Conversion: Techniques for converting plain text into well-formatted Markdown using LLMs.
  • Cost Efficiency: Exploring the possibility of using a single LLM model for multiple tasks to save costs.
  • Experimentation: Encouraging experimentation with various approaches to refine the integration between code and textual representations.

Conclusion

The session offered deep insights into the evolving landscape of generative AI and LLMs. Greg’s presentation touched on:

  • The transformative potential of advanced AI research and hardware investments.
  • Practical challenges and strategies in using LLMs for data processing and application development.
  • The integration of coding assistance tools to enhance productivity and support learning.
  • The importance of iterative experimentation—both in software deployment and conceptual understanding.

Students are urged to follow the next steps, engage actively in the upcoming lab session, and continue exploring these cutting-edge topics via the provided examples and resources.

References