DeepMind's AlphaCode Explained: Everything You Need to Know

Programming has been for a long time a high-status, high-demand skill. Companies and businesses across industries depend at a very foundational level on the ability of human developers: People who write and understand the language of computers. Recently, with the advent of large language models, AI companies have begun to explore the possibilities of systems that can learn to code. OpenAI's Codex -- embedded into GitHub Copilot -- was the first notable example. Codex can read simple natural language commands and instructions and write code that matches the intention of the user. Yet, writing small programs and solving easy tasks is "far from the full complexity of real-world programming." AI models like Codex lack the problem-solving skills that most programmers rely on in their day-to-day jobs. That's the gap DeepMind wanted to fill with AlphaCode, an AI system that has been trained to "understand" natural language, design algorithms to solve problems, and then implement them into code. AlphaCode displays a unique skillset of natural language understanding and problem-solving ability, combined with the statistical power characteristic of large language models. The system was tested against human programmers on the popular competitive programming platform Codeforces. AlphaCode averaged a ranking of 54.3% across 10 contests, which makes it the first AI to reach the level of human programmers in competitive programming contests. I've studied the AlphaCode paper to understand what AlphaCode is and isn't, what these impressive results mean, what are the implications, and what the future holds for AI and human developers. I've also researched what AI experts and competitive programmers are saying about AlphaCode, so you have different independent perspectives to form your own. This article is a thorough review divided into 6 sections (and their respective subsections). I will include comments throughout the article to explore some questions, ideas, and results in more depth.