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It can convert a written instruction into executable code in a number of programming languages, such as “Create an array of random variables between 1 and 100 and then return the largest of them.”Ī other iteration of the same OpenAI algorithm, known as GPT-3, is capable of producing writing that is intelligible on a particular topic, but it may also repeat offensive or prejudiced words that it has learnt from the less savory parts of the web.Ĭopilot and Codex have led some developers to wonder if AI might automate them out of work.
On top of Codex, OpenAI created its own AI coding tool that is capable of some amazing coding feats.
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The algorithm ingested billions of lines of code stored on GitHub - not all of it perfect - in order to learn how to write code. That model, called Codex, consists of a large artificial neural network trained to predict the next characters in both text and computer code. The GitHub program is built on top of an AI model developed by OpenAI, a prominent AI company doing cutting-edge work in machine learning. He says GitHub recommends that developers use Copilot together with CodeQL to ensure their work is safe. He says the percentage of flawed code cited by the NYU researchers is only relevant for a subset of code where security flaws are more likely.ĭe Moor invented CodeQL, a tool used by the NYU researchers that automatically identifies bugs in code. Oege de Moor, vice president of research at GitHub and one of the developers of Copilot, says security has been a concern from the start. These were apparently introduced to prevent the system from blurting out offensive messages or copying well-known code written by someone else. While analyzing the code made available for a Copilot plugin, Dolan-Gavitt found that it included a list of restricted phrases. “It seems like it makes errors that have a different flavor than the kind I would make.” But Copilot also highlights some of the pitfalls of today’s AI techniques. There’s growing interest in using AI to help automate more mundane work.
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“But the way Copilot was trained wasn’t actually to write good code - it was just to produce the kind of text that would follow a given prompt.”ĭespite such flaws, Copilot and similar AI-powered tools may herald a sea change in the way software developers write code. The figure “is a little bit higher than I would have expected,” says Brendan Dolan-Gavitt, a professor at NYU involved with the analysis. Researchers at NYU recently analyzed code generated by Copilot and found that, for certain tasks where security is crucial, the code contains security flaws around 40 percent of the time. The risks of AI generating faulty code may be surprisingly high. “And it can be really hard to track this down, perhaps because it seems like it makes errors that have a different flavor than the kind I would make.” “There have been times where I’ve missed some kind of subtle error when I accept one of its proposals,” he says.
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“It does feel a little like my work has shifted from being a generator of code to being a discriminator of it.”īut Naka has found that errors can creep into his code in different ways. “It lets me spend less time jumping to the browser to look up API docs or examples on Stack Overflow,” he says. Start typing a command, a database query, or a request to an API, and the program, called Copilot, will guess your intent and write the rest.Īlex Naka, a data scientist at a biotech firm who signed up to test Copilot, says the program can be very helpful, and it has changed the way he works. Last June, GitHub, a subsidiary of Microsoft that provides tools for hosting and collaborating on code, released a beta version of a program that uses AI to assist programmers.
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