This site may earn affiliate commissions from the links on this page. Terms of use.

(Credit: Google)
Google’s DeepMind AI division has already built machines that can wreck you in StarCraft II and predict millions of protein structures, but now it’s taking on an even harder task: writing coherent code. In an apparent effort to put themselves out of a job, DeepMind researchers have created an AI that can write code, and it performs surprisingly well in programming challenges. It’s neither the best nor the worst when compared with humans participating in the same challenges, but the StarCraft AI was only a middling player at first.

While machine learning has advanced by leaps and bounds, it’s hard to create an AI that’s good at more than one thing. So, a machine could be trained with data to handle one class of programming challenges, but it would fail when given a different problem to tackle. So, the team decided to skip all the training on algorithms and code structure, instead treating it more like a translation problem.

Programming challenges usually include a description of the task, and the resulting code submitted by a human participant is technically just an expression of the description. The AI ​​works in two phases: It takes the description and converts it to an internal representation. Then, it uses representation to generate functional code based on the data shown in training. And there was a lot of data.

DeepMind fed the AI ​​700GB of code on GitHub, complete with the comments that explain the code. As Ars Technica points out, that’s a huge amount of text data. With the essence of programming internalized, DeepMind sets up its own programming contests and feeds the results into the AI ​​to fine-tune the model’s performance. The team says this is an order of magnitude more training data than past coding machines have gotten, and that made all the difference.

The researchers found that AlphaCode was able to generate a huge number of potential answers to a coding problem, but about 40 percent of them would run through all the available system memory or fail to reach the answer in a reasonable amount of time. The data needs to be filtered to find the one percent of solutions that are actually good code. DeepMind found that clusters of similar codes indicated better answers, whereas the wrong ones were randomly distributed. By focusing on those answers, AlphaCode was able to correctly answer about one-third of coding challenges. It turns out a lot of human programmers are a little better, so AlphaCode placed in the top 54 percent. It’s not about taking jobs from DeepMind engineers, but give it time.

Now read:

Leave a Reply

Your email address will not be published. Required fields are marked *

Explore More

Been exposed or tested positive to COVID-19?

November 15, 2022 0 Comments 4 tags

Department of Health and Social Care The universal health coverage improved from a global average of 45 out of 100 in 2000 to 64 in 2015 and then 67 in

One of the world’s most popular programming languages ​​is coming to Linux

August 30, 2023 0 Comments 5 tags

The next version of the Linux kernel will include support for popular programming language Rust, it has been confirmed. As reported by The Register (opens in new tab)Linus Torvalds, the

Get 2022 health coverage Health Insurance Marketplace®

October 21, 2023 0 Comments 4 tags

You can contribute to groundbreaking studies by sharing health data. With the Medications app on Apple Watch, you can get convenient and discreet reminders so you can quickly log medications.