Early this week Satya Nadella, Microsoft CEO, and Eric Boyd, Corporate AI Vice President, announced Azure OpenAI Service generally available, which will soon include ChatGPT – the fine-tuned version of GPT-3.5 built upon Azure AI infrastructure gone viral in the last few weeks. But let’s take a step back. What is Azure OpenAI Service?
The Azure OpenAI Service is the result of a 4-years partnership between Microsoft and OpenAI with the mission of building an artificial general intelligence (AGI) system, democratizing AI and generating a broadly distributed economic benefits.
From a technical point of view, Azure OpenAI provides access to OpenAI’s powerful language (like GPT-3.5 and Codex) and computer vision models (like DALL•E 2), through REST APIs, Python Sdk or a web-based interface (Azure OpenAI Studio), by ensuring Azure security and responsible AI capabilities.
Key concepts of how OpenAI generative AI models work are prompt programming and in-context learning. A prompt is a text command input that is used by the model to generate a text completion. For example, if we ask Codex to generate a Python function to order a list of items, the text used as instruction is the prompt, while the output code – predicted as the most probable next piece of text - is the completion. In addition to the prompt, the user can provide examples of the expected outcome as input: this is what is defined as in-context learning.
How are we using OpenAI today?
Azure OpenAI powers several Microsoft apps and experiences today. An example is GitHub Copilot, an AI pair programmer based on OpenAI Codex model, capable of generating code from a comment, generating documentation from a piece of code or translating between a coding language to another.
Another great example is the Microsoft Designer App, currently in preview, based on DALL•E model and capable of generating images and artworks from textual input.
How can you get started?
If you want to get started generating text, artworks or code through Azure OpenAI services, you need to follow the steps below:
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