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Part I- Applying Natural Intelligence (NI) to Artificial Intelligence (AI): Understanding ‘Why’ Training ChatGPT Transcends the Contours of Copyright
- Shivam Kaushik delves into the technical details and copyright implications of training Large Language Models like ChatGPT in this insightful post.
- Large Language Models (LLMs) use language modeling to predict the next word in a text using conditional generation based on probability and statistics.
- LLMs, such as ChatGPT, consist of neural network models with input, hidden, and output layers dedicated to predicting and generating text coherently.
- Transformers, introduced in 2017, enable LLMs like ChatGPT to capture contextual information using attention scores to understand word relationships.
- Training of ChatGPT involves pre-training on a large dataset of public information to adapt general language patterns for specific tasks like chatbot responses.
- Data preparation, tokenization, and conversion of tokens to numerical vectors in high-dimensional spaces contribute to the 'intelligence' in LLMs.
- The Word2Vec technique encodes syntactic and semantic information in word embeddings, allowing LLMs to understand conceptual relationships beyond keyword matching.
- Pre-training processes, token assignments, and word embeddings form the basis for LLMs like ChatGPT to predict and generate text by filling in the blanks.
- Shivam Kaushik's analysis sheds light on the inner workings of LLMs and the legal implications surrounding the training process, particularly focusing on copyright concerns.
- The article provides a detailed overview of how training ChatGPT involves processing and analyzing data, tokenizing text, and transforming tokens into numerical representations.
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