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Kickstarting AI for Code: Introducing IBM's Project CodeNet

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"Software is eating the world," US entrepreneur Marc Andreessen famously wrote in 2011. Fast-forward to today – software is in financial services and healthcare, smartphones and smart homes. Such large volumes of code, however, is a challenge to debug, maintain, and update, especially as enterprises aim to modernize their aging software infrastructure. As a result, we find ourselves in a new age where it's essential to take advantage of today's powerful technologies like artificial intelligence (AI) and hybrid cloud to create new solutions that can modernize processes across the information technologies (IT) pipeline. A large dataset aimed at teaching AI to code, it consists of some 14M code samples and about 500M lines of code in more than 55 different programming languages, from modern ones like C, Java, Python, and Go to legacy languages like COBOL, Pascal, and FORTRAN.


IBM CodeNet: Artificial Intelligence That Can Program Computers And Solve A $100 Billion Legacy Code Problem

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Computer scientists have long toyed with the idea of creating computers that could write programs for other computers. Artificial intelligence is an obvious technology for the task. It has been previously used for programming on a small scale but unfortunately the results have been limited. Artificial intelligence is one of our most powerful and versatile technologies in use today. It can understand and generate speech, analyze documents, recognize images and characters, drive cars, pilot war planes, write papers, and perform thousands of other valuable operations.


What is IBM's Project CodeNet?

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At its recently concluded Think 2021 conference, IBM introduced Project CodeNet to develop machine learning models that can help in programming. The large dataset consists of 14 million code samples and 500 million lines of code in over 55 different languages, including C, Java, Go, Python, COBOL, Pascal, and Fortran. Modern computer programs have millions of lines of code and are hard to debug, maintain, update, and document. The use of artificial intelligence to write codes has been an important area of research for many years. However, it is easier said than done.


IBM's Project CodeNet will test how far you can push AI to write software

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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.


IBM's Project CodeNet will test how far you can push AI to write software

#artificialintelligence

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.