<https://www.fast.ai/2021/07/19/copilot> [...] According to OpenAI’s paper, Codex only gives the correct answer 29% of the time. And, as we’ve seen, the code it writes is generally poorly refactored and fails to take full advantage of existing solutions (even when they’re in Python’s standard library). Copilot has read GitHub’s entire public code archive, consisting of tens of millions of repositories, including code from many of the world’s best programmers. Given this, why does Copilot write such crappy code? The reason is because of how language models work. They show how, on average, most people write. They don’t have any sense of what’s correct or what’s good. Most code on GitHub is (by software standards) pretty old, and (by definition) written by average programmers. Copilot spits out it’s best guess as to what those programmers might write if they were writing the same file that you are. OpenAI discuss this in their Codex paper: “As with other large language models trained on a next-token prediction objective, Codex will generate code that is as similar as possible to its training distribution. One consequence of this is that such models may do things that are unhelpful for the user” One important way that Copilot is worse than those average programmers is that it doesn’t even try to compile the code or check that it works or consider whether it actually does what the docs say it should do. Also, Codex was not trained on code created in the last year or two, so it’s entirely missing recent versions, libraries, and language features. For instance, prompting it to create fastai code results only in proposals that use the v1 API, rather than v2, which was released around a year ago. Complaining about the quality of the code written by Copilot feels a bit like coming across a talking dog, and complaining about its diction. The fact that it’s talking at all is impressive enough! Let’s be clear: The fact that Copilot (and Codex) writes reasonable-looking code is an amazing achievement. From a machine learning and language synthesis research point of view, it’s a big step forward. But we also need to be clear that reasonable-looking code that doesn’t work, doesn’t check edge cases, and uses obsolete methods, and is verbose and creates technical debt, can be a big problem. [...]