Project CodeNet, an IBM initiative, will see how far AI can be pushed.
This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence.
IBM’s AI research division has released a 14-million-sample dataset to develop machine learning models that can help in programming tasks. Called Project CodeNet, the dataset takes its name after ImageNet, the famous repository of labeled photos that triggered a revolution in computer vision and deep learning.
While there’s a scant chance that machine learning models built on the CodeNet dataset will make human programmers redundant, there’s reason to be hopeful that they will make developers more productive.
In the early 2010s, impressive advances in machine learning triggered excitement (and fear) about artificial intelligence soon automating many tasks, including programming. But AI’s penetration in software development has been extremely limited.
Human programmers discover new problems and explore different solutions using a plethora of conscious and subconscious thinking mechanisms. In contrast, most machine learning algorithms require well-defined problems and a lot of annotated data to develop models that can solve the same problems.
