Kyle Hill on How ChatGPT works internally

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SUMMARY

Kyle Hill's discussion focuses on the internal workings of ChatGPT, specifically referencing the GPT-3.5 paper. He emphasizes that ChatGPT is not sentient and explains the extensive training and statistical analysis involved in generating responses. The conversation highlights the importance of understanding Large Language Models (LLMs) and the use of temperature settings to modify GPT outputs. Additionally, Hill critiques the appropriateness of posting pop science content in technical forums.

PREREQUISITES
  • Understanding of Large Language Models (LLMs)
  • Familiarity with the GPT-3.5 architecture
  • Knowledge of statistical analysis techniques in machine learning
  • Experience with temperature settings in AI model responses
NEXT STEPS
  • Research the GPT-3.5 paper for in-depth technical insights
  • Explore the concept of temperature settings in AI models
  • Learn about training methodologies for Large Language Models
  • Investigate tools for analyzing and modifying GPT outputs
USEFUL FOR

This discussion is beneficial for AI researchers, machine learning practitioners, and programmers interested in understanding the mechanics of ChatGPT and enhancing their knowledge of Large Language Models.

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Kyle Hill gets into the internals of how ChatGPT works:

 
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Not complaining but I thought we weren't allowed to discuss pop sci in the forum
 
Feynstein100 said:
Not complaining but I thought we weren't allowed to discuss pop sci in the forum
There are some PF forums where it may be appropriate, depending on the subject. I haven't watched the video above, but it sounds interesting. Posting videos as sources in the technical Physics and Math forums is almost never a good idea.
 
This guy is a more serious if not silly science reporter and has some really good video content. He gets into some of the conceptual and technical challenges faced by the GPT team to make it work well. He cites the GPT-3.5 paper and provides insight into how things are done.
 
It's a decent video that gently describes the basics of Large Language Models (LLMs) and how they function. It's not in the PopSci vein of "ChatGPT will end life as we know it!!!"
 
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As a programmer, this was along the lines of something I wanted to see. I posted an earlier one in another thread where the presenter went through the GPT paper looking for things. But I haven't seen a followup to it.

Kyle is very emphatic when he says GPT is not sentient and that it doesn't know what its generating and then goes to show the extensive training and statistical analysis being used to select the best choice of words which was quite enlightening for me.
 
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Here's another interesting video on using ChatGPT:



The presenter talks about using the temperature setting to change GPT responses and shows some related tools you can use to explore GPT models.
 

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