In this episode, Jeffrey discusses the architecture of GPT-3, the technology behind ChatGPT, and how you should think about this technology in 2023. 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. 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. Mission- Today I want to raise up the conversation a bit. 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. 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. Execution Introduction- GPT-3, or Generative Pretrained Transformer 3, is the latest language generation AI model developed by OpenAI. 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. 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. Architecture of GPT-3 The GPT-3 architecture is based on the Transformer network, which was introduced in 2017 by Vaswani et al. in their paper “Attention is All You Need”. 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. The GPT-3 model consists of multiple layers, each containing attention and feed-forward neural networks. The attention mechanism allows the model to focus on different parts of the input text, which is useful for understanding context and generating text that is coherent and relevant to the input. The feed-forward neural network is responsible for processing the information from the attention mechanism and generating the output. The output of one layer is used as the input to the next layer, allowing the model to build on its understanding of the input text and generate more complex and sophisticated text. Using GPT-3 in a C# Application To use GPT-3 in a C# application, you will need to access the OpenAI API, which provides access to the GPT-3 model. You will need to create an account with OpenAI, and then obtain an API key to use the service. Once you have access to the API, you can use it to generate text by sending a prompt, or starting text, to the API. The API will then generate text based on the input, and return the output to your application. To use the API in C#, you can use the HTTPClient class to send a request to the API and receive the response. The following code demonstrates how to send a request to the API and retrieve the generated text: ``` using System; using System.Net.Http; using System.Text; namespace GPT3Example { class Program { static void Main(string[] args) { using (var client = new HttpClient()) { client.BaseAddress = new Uri("https://api.openai.com/v1/"); var content = new StringContent("{\"prompt\":\"Write a blog post about the architecture of GPT-3\",\"model\":\"text-davinci-002\",\"temperature\":0.5}", Encoding.UTF8, "application/json"); content.Headers.Add("Authorization", "Bearer API_KEY"); var response = client.PostAsync("engines/davinci/jobs", content).Result; if (response.IsSuccessStatusCode) { var responseContent = response.Content.ReadAsStringAsync().Result; Console.WriteLine(responseContent); } } } } } ``` End of demo From the start of this explanation, the text was generated by chat.openai.com. It can be pretty impressive. But, at the same time, it's very shallow. GPT-3 is a machine learning model that has been trained with selected information up to 2021. Lots of information, but selected, nonetheless. Here is the actual ChatGPT page that generated this. Notice that it admits that it doesn't have information past 2021. Let's dig deeper, though on what GPT-3 is and how it came to be. Let's look at the theory behind it so that we can see if we should use it as an architectural element in our own software. - Let's go back to 2017. Ashish Vaswani and 7 other contributors wrote a paper called "Attention Is All You Need". In it, they proposed a new network architecture for their neural network. Simplify that and think of a machine learning model. They create a method that could be significantly trained in 3.5 days using eight GPUs and be ready for a complete transition from one spoken language to another. They tested it using English-to-French and English-to-German. Vaswani and other contributors were from Google Brain, four from Google Research, and one from the University of Toronto. - In 2018, four engineers from OpenAI wrote a paper entitled "Improving Language Understanding by Generative Pre-Training". They lean on Vaswani's paper and dozens of others. They ...
