Kyle Hill on How ChatGPT works internally

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In summary: He also mentions the importance of using diverse training data to avoid biased responses. In summary, Kyle Hill discusses the inner workings of ChatGPT, including the challenges faced by the team and the training and statistical analysis involved in selecting the best responses. He also emphasizes the importance of diverse training data and talks about using temperature settings and related tools to explore GPT 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
 
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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.
 
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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.
 
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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.
 

FAQ: Kyle Hill on How ChatGPT works internally

1. How does ChatGPT work internally?

ChatGPT works by using a large neural network that has been trained on vast amounts of text data. This neural network processes input text and generates responses based on patterns it has learned during training.

2. What training data is used for ChatGPT?

ChatGPT is trained on a diverse dataset of text from the internet, including websites, books, and other sources. This helps the model learn a wide range of language patterns and styles.

3. How does ChatGPT generate responses?

ChatGPT generates responses by predicting the next word in a sequence of text based on the input it receives. It uses this prediction to generate coherent and contextually appropriate responses.

4. Can ChatGPT understand context and carry on a conversation?

Yes, ChatGPT is designed to understand context and carry on a conversation. It uses its training data to infer meaning and generate responses that are relevant to the ongoing dialogue.

5. What are the limitations of ChatGPT?

While ChatGPT is highly advanced, it still has limitations in understanding complex or nuanced language, as well as in maintaining long-term coherence in conversations. It may also generate responses that are inaccurate or inappropriate in certain contexts.

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