The GPT-3 model consists of multiple layers, each containing attention and feed-forward neural networks. The Transformer network is a type of neural network that is well-suited for NLP tasks due to its ability to process sequences of variable length. in their paper “Attention is All You Need”. The GPT-3 architecture is based on the Transformer network, which was introduced in 2017 by Vaswani et al. GPT-3 can generate human-like text in a variety of styles and formats, making it a powerful tool for natural language processing (NLP) tasks such as text completion, text summarization, and machine translation. It is one of the largest AI models with over 175 billion parameters, and it has been trained on a massive amount of text data. It's important that we understand the technology and how we might want to use it as an architectural element of our own software systems.Įxecution Introduction- GPT-3, or Generative Pretrained Transformer 3, is the latest language generation AI model developed by OpenAI. I want to go beyond the chatbot that has received so much press and look at the GPT-3 technology and analyze it from an architectural perspective.

Mission- Today I want to raise up the conversation a bit.

Many are pointing out the awesome and surprising capabilities it has while others are quick to point out when it provides answers that are flat-out wrong, backward, or immoral. Situation- ChatGPT is getting a lot of press because it's the first freely available implementation of GPT-3 that has captured the imagination of the masses.

In this episode, Jeffrey discusses the architecture of GPT-3, the technology behind ChatGPT, and how you should think about this technology in 2023.

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