About DeepMind AlphaCode
DeepMind’s AlphaCode is a groundbreaking system that leverages machine learning to generate code that can compete in popular programming contests. It uses a transformer-based neural network model and self-supervised learning to achieve near-human performance on the Codeforces platform, a popular site for such competitions.
Here are four key features of AlphaCode
- Competitive Performance: AlphaCode can perform at a level comparable to a novice programmer with a few months to a year of training. It has achieved an average ranking in the top 54.3% in simulated evaluations on recent programming competitions on the Codeforces platform.
- Self-Supervised Learning: AlphaCode uses self-supervised learning and an encoder-decoder transformer architecture to generate code. This allows it to understand complex natural language descriptions and reason about previously unseen problems.
- Large-Scale Sampling: A key driver of AlphaCode’s performance comes from scaling the number of model samples to orders of magnitude more than previous work. The overall solve rate scaled log-linearly with the number of samples generated, even when only 10 of them were submitted.
- Diverse Code Generation: AlphaCode generates millions of diverse programs for each problem, then filters and clusters those programs to a maximum of just 10 submissions. This approach allows it to effectively explore the space of possible solutions before committing to the final submissions.