DeepMind's AlphaCode model performed well against human coders in a programming competition, with a paper describing its overall performance as similar to that of a "novice programmer" with up to a year of training
AlphaCode achieved "approximately human-level performance" and solved previously unseen natural language problems by forecasting code segments and generating millions of potential solutions.
The model then winnowed them down to a maximum of 10 solutions, which the researchers said were produced "without any built-in knowledge about the structure of computer code."
Carnegie Mellon University's J. Zico Kolter wrote, "Ultimately, AlphaCode performs remarkably well on previously unseen coding challenges, regardless of the degree to which it 'truly' understands the task."
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