You can build Start-ups based on:

a) GPT-3 (Generative Pretrained Transformer 3) : A state-of-the-art language model that can generate human-like text and perform various language tasks such as translation, summarisation, and question answering. GPT-3 Small: A smaller model with a capacity of around 125 million parameters. GPT-3 Medium: A medium-sized model with a capacity of around 355 million parameters. GPT-3 Large: A large-scale model with a capacity of around 774 million parameters. GPT-3 XL: The largest GPT-3 model with a capacity of around 1.5 billion parameters. Limitations 1: the OpenAI API has a maximum length of 2048 characters for the prompt text; keep the prompt text as concise and clear as possible Limitations 2: GPT-3 has been trained on a massive amount of text data from the internet, which includes information on a wide range of topics * includes scientific papers and books, along with a variety of other text sources such as websites, news articles, and social media posts. Limitations 3: "Da Vinci" and "Codex" are custom models built using GPT-3 technology. The "003" in the name "da-vinci-003" refers to the size of the model, with larger numbers indicating larger and more capable models. Codex is also a custom model developed by OpenAI, but it is designed specifically for coding and programming-related tasks, such as code generation, code completion, and code analysis. Both Da Vinci and Codex are examples of how OpenAI is leveraging GPT-3 technology to build custom models for specific use cases and industries. Limitations 4: GPT-3 can't become more familiar with a specific subject over time in the sense that it retains long-term memory or context.

Startup: Organizations and researchers to build custom models using GPT-3 by fine-tuning the model on specific tasks or domains, or by using the model as part of larger systems or workflows. Example: "Quick-fixes for your EV" 1- Gather training data: You'll need to gather a large amount of training data that covers the quick-fixes you want to include in the app. This data could include text from car repair manuals, automotive websites, and other sources that describe the steps involved in performing quick-fixes on cars. 2- Fine-tune GPT-3: Next, you'll need to fine-tune GPT-3 using the API and the training data you gathered. This will involve sending prompts to the API that describe the quick-fixes you want the model to generate text for, and adjusting the model parameters to optimize its performance. 3.1 - This involves: Mandatory: If the language model like GPT-3 has limited knowledge about a particular subject, you can fine-tune it by providing it with additional data and adjusting the prompt to reflect this new information. However, as GPT-3 cannot be retrained from scratch, you cannot directly train the model on this new data. Instead, you need to use a process called "fine-tuning" to make the model more knowledgeable about the subject. Fine-tuning a language model involves sending specific prompts to the API that are relevant to the subject you want the model to be knowledgeable about. The API will then generate text based on these prompts and the parameters you specify. You can evaluate the generated text and adjust the prompts and parameters as needed to improve the model's performance.

3.1.1 Prompt: The prompt is the text input that you provide to the API, which the model uses to generate text. When fine-tuning, you may adjust the prompt to better reflect the specific task or domain you are trying to model. 3.1.2 Temperature: The temperature is a parameter that controls the diversity of the generated text. A lower temperature results in more conservative and repetitive text, while a higher temperature results in more diverse and creative text. 3.1.3 Top-k and Top-p sampling: These parameters control the diversity of the generated text by limiting the number of tokens (k) or the probability mass (p) of the highest-probability tokens that are considered for each step of the text generation process. 3.1.4 Max length: The maximum length parameter controls the maximum number of tokens that the API can generate for each prompt. 3.1.5 Model architecture: In some cases, you may also adjust the architecture of the model, for example by adding or removing layers, changing the size of the hidden state, or adjusting the number of attention heads.

3- Build the App: Once you have fine-tuned GPT-3, you can build the web app that provides quick-fix hints to car owners. This could involve creating a user interface that allows car owners to input the type of quick-fix they want to perform, and then using the API to generate text that provides step-by-step instructions for performing the quick-fix.

b) DALL·E: An AI model that can generate original images from textual descriptions.

NLP (Natural Language Processing) tasks such as text generation, question-answering, summarization, translation

If it's a text based OpenAI API

OpenAI API: A platform for developers to access and use the capabilities of OpenAI's language models, including GPT-3, in their own applications.

Others

OpenAI Safety: A research initiative aimed at ensuring that artificial intelligence develops in a safe and beneficial manner. OpenAI Gym: An environment for developing and comparing reinforcement learning algorithms.