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Wednesday, December 21, 2022

AI learns to write computer code in ‘stunning’ advance

I learned to write computer code in ‘stunning’ advance

DeepMind’s AlphaCode outperforms many human programmers in tricky software challenges

By Matthew Hutson

Conceptual illustration of code on vertical stacks

Snippets of code in white come from the AlphaCode artificial intelligence system, whereas the purple code snippets were written by humans trying to solve similar problems.   DEEPMIND

Software runs the world. It controls smartphones, nuclear weapons, and car engines. But there’s a global shortage of programmers. Wouldn’t it be nice if anyone could explain what they want a program to do, and a computer could translate that into lines of code?

A new artificial intelligence (AI) system called AlphaCode is bringing humanity one step closer to that vision, according to a new study. Researchers say the system—from the research lab DeepMind, a subsidiary of Alphabet (Google’s parent company)—might one day assist experienced coders, but probably cannot replace them.

“It’s very impressive, the performance they’re able to achieve on some pretty challenging problems,” says Armando Solar-Lezama, head of the computer assisted programming group at the Massachusetts Institute of Technology.

AlphaCode goes beyond the previous standard-bearer in AI code writing: Codex, a system released in 2021 by the nonprofit research lab OpenAI. The lab had already developed GPT-3, a “large language model” that is adept at imitating and interpreting human text after being trained on billions of words from digital books, Wikipedia articles, and other pages of internet text. By fine-tuning GPT-3 on more than 100 gigabytes of code from Github, an online software repository, OpenAI came up with Codex. The software can write code when prompted with an everyday description of what it’s supposed to do—for instance counting the vowels in a string of text. But it performs poorly when tasked with tricky problems.

AlphaCode’s creators focused on solving those difficult problems. Like the Codex researchers, they started by feeding a large language model many gigabytes of code from GitHub, just to familiarize it with coding syntax and conventions. Then, they trained it to translate problem descriptions into code, using thousands of problems collected from programming competitions. For example, a problem might ask for a program to determine the number of binary strings (sequences of zeroes and ones) of length n that don’t have any consecutive zeroes.

When presented with a fresh problem, AlphaCode generates candidate code solutions (in Python or C++) and filters out the bad ones. But whereas researchers had previously used models like Codex to generate tens or hundreds of candidates, DeepMind had AlphaCode generate up to more than 1 million. ... ' 

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