ChatGPT Meets Wolfram|Alpha: A Tale of Two AIs
Beginners can learn about combining Wolfram|Alpha and large language models (LLMs). See examples of using the Wolfram plugin for ChatGPT, calling LLM models within Wolfram Language code and the Prompt Repository. Examples include generating and fact-checking content, processing data and developing custom ChatGPT plugins.
ChatGPT and Wolfram|Alpha take very different approaches to processing natural language and answering questions. They have complimentary strengths and weaknesses. This video explains how the computation model of Wolfram|Alpha and the large language model of ChatGPT work differently. It shows how you can build your own applications combining the advantages of both models. This video assumes no prior knowledge and introduces ideas with simple, practical examples.
Presenter:
Jon McLoone, Director of Technical Communications & Strategy
0:00 Introduction
0:24 Statistical AI vs. Symbolic AI
4:12 Wolfram|Alpha's Handling of Natural Language
5:39 Complimentary Strengths and Weaknesses
17:15 Combining Computation and LLMs
23:22 Developing a Custom Plugin
29:34 Dealing with Unstructured Data and Tasks
38:09 Everyone Can Code (with Help from AI)
43:49 Prompt Engineering
48:34 Summary