DeepMind’s latest real-world application of machine learning is AlphaCode. Alphabet’s AI lab today announced a system that “writes computer programs at a competitive level.”

DeepMind has a mission to “solve intelligence” and its latest effort is meant to move beyond solving “relatively simple maths and programming problems.”  

For artificial intelligence to help humanity, our systems need to be able to develop problem-solving capabilities.

AlphaCode leverages deep learning models to excel at tasks that require “critical thinking, logic, algorithms, coding, and natural language understanding” to excel at competitive programming, something that DeepMind says is “beyond the capabilities of existing AI systems.”

Competitive programming is a popular and challenging activity; hundreds of thousands of programmers participate in coding competitions to gain experience and showcase their skills in fun and collaborative ways. During competitions, participants receive a series of long problem descriptions and a few hours to write programs to solve them. Typical problems include finding ways to place roads and buildings within certain constraints, or creating strategies to win custom board games. 

The system specifically uses “large-scale transformer models (that have recently shown promising abilities to generate code) with large-scale sampling and filtering.”

Human participants in these challenges are “ranked mainly based on how many problems they solve.” DeepMind used 10 existing competitions hosted on Codeforces to test AlphaCode. It achieved an “estimated rank within the top 54%” of human participants, or the level of a median competitor.

Although far from winning competitions, this result represents a substantial leap in AI problem-solving capabilities and we hope that our results will inspire the competitive programming community.

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Abner Li

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