Practical AI AI-generated code with OpenAI Codex
Nov 30, 2021
Natalie Pistunovich, a developer advocate at AeroSpike and an OpenAI ambassador, shares her insights on the revolutionary Codex model and GitHub Copilot. She discusses how AI tools like GPT-3 enhance coding workflows, making software development more efficient and collaborative. The conversation dives into the training challenges of AI, the importance of MLOps, and how Codex is transforming coding practices. They explore amusing anecdotes about automatic code generation and the future of AI in engineering teams.
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Codex: Language to Code
- Codex translates natural language into code, powering GitHub Copilot.
- It excels in several languages and generates syntactically correct code quickly.
Copilot for Python
- Daniel Whitenack used Copilot to generate Python code, including list completion and data frame creation.
- He also used it to automatically generate code to save a data frame to a CSV file.
Go's Advantages for AI/ML
- Go's small size, single way of doing things, and easy memory management promotes productivity and reduces errors.
- This is crucial for quickly deploying and managing AI models in production.