Course: Talking to Information

Concentrating on large environmental information, this module teaches techniques for data mirroring and management using AI. Participants will learning to handle and interpret extensive environmental information effectively.

Good morning, I’m Lee Mallon, and welcome to the fourth module of the AI for Environmental Educators course. In this session, we’ll delve into the significance of 'talking to information' and the mechanisms that enable us to do so efficiently.

Key Concepts in Talking to Information

Understanding AI requires familiarity with several key terms:

  • Context Window: The amount of information an AI model can consider at one time. It’s essential to manage the context window effectively, balancing the depth of analysis with computational cost.
  • RAG (Retrieval-Augmented Generation): This technique involves retrieving the most relevant chunks of data to answer a question, reducing computational load and focusing the model’s attention.
  • Analysis: Differentiating between creative responses and factual accuracy in AI responses is crucial, especially when dealing with empirical data.
  • Custom Models (Doppelgängers): Creating tailored AI models or 'doppelgängers' to interact with a specific set of information, enabling focused conversations on particular subjects.

Demonstrating AI’s Capabilities with ChatGPT

In a live demonstration, we’ll interact with ChatGPT to show you how it can answer straightforward questions, analyse data, and even write code to generate outputs, such as CSV files. The demo will reveal how we can use AI to interpret complex documents, like a UN report, and extract salient points to engage in an informed dialogue about environmental issues.

Crafting Custom AI Models

The customisation aspect of AI models allows us to encapsulate vast amounts of data within a conversational model that can interact with users in a personalised manner. We’ll walk through creating a custom GPT model centred around a UN report on equality, showing how it can converse and provide insights in a structured, coherent way.

The Path Ahead

As we conclude Module 4, we set the stage for our next module on 'Generating Text'. This upcoming module will explore transforming complex reports and data sets into a diverse array of content formats.

Thank you for participating in this module. I hope the insights and skills you’ve gained here will prove invaluable as we continue our journey through AI's applications in environmental education.