Stay informed about the latest artificial intelligence (AI) terminology with this comprehensive glossary. From algorithm and AI ethics to generative AI and overfitting, learn the essential AI terms that will help you sound smart over drinks or impress in a job interview.
Microsoft revealed a new AI tool called Infra Copilot, which uses its existing GitHub Copilot to create infrastructure code.
Infra Copilot is designed to understand the context of infrastructure tasks and generate appropriate code suggestions based on natural language prompts.
The tool can streamline the coding process, enabling professionals to focus on higher-level tasks.
It also provides standardized code snippets for consistency across different environments.
Infra Copilot is available now to programmers with a recent Visual Studio Code version and a GitHub Copilot license.
Microsoft has also launched GitHub Copilot Enterprise, using data from a company's own code repositories to generate code and answer questions, priced at $39 per month per user.
Infra Copilot is designed to understand the context of infrastructure tasks and generate appropriate code suggestions based on natural language prompts.
The tool can streamline the coding process, enabling professionals to focus on higher-level tasks.
It also provides standardized code snippets for consistency across different environments.
Infra Copilot is available now to programmers with a recent Visual Studio Code version and a GitHub Copilot license.
Microsoft has also launched GitHub Copilot Enterprise, using data from a company's own code repositories to generate code and answer questions, priced at $39 per month per user.
Inside Phi 2: Microsoft's Small Language Model explores the development of tailored AI models that minimize resource usage, with a focus on small language models (SLMs). The article discusses Microsoft Research's approach to building generative AI models, highlighting their "textbooks are all you need" strategy used in training their Phi series SLMs. Key aspects include curating high-quality training data, generating synthetic content, and fine-tuning the model with domain-specific information. This innovative approach has resulted in surprisingly good outcomes, with some benchmarked SLMs performing similarly or even better than larger LLMs like GPT.
