Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟰𝟱 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗜𝗕𝗠 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲𝘀 ‘𝗖𝗼𝗱𝗲𝗡𝗲𝘁’: 𝗔 𝗟𝗮𝗿𝗴𝗲-𝗦𝗰𝗮𝗹𝗲 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗔𝗶𝗺𝗲𝗱 𝗮𝘁 𝗧𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗖𝗼𝗱𝗲 𝗙𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀 Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: ‘𝗖𝗼𝗱𝗲𝗡𝗲𝘁’: 𝗔 𝗟𝗮𝗿𝗴𝗲-𝗦𝗰𝗮𝗹𝗲 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗔𝗶𝗺𝗲𝗱 𝗮𝘁 𝗧𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗖𝗼𝗱𝗲 𝗙𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀 🔸 This paper is published neurips2021. 🔸 If you have checked your bank account, used a credit card, gone to the doctor, booked a ticket, paid your taxes, or bought anything in a store, you’ve probably dealt with a design that depends on COBOL (Common Business Oriented Language) code. Even though it was initially introduced over six decades ago, it is still used in many mission-critical business systems worldwide. COBOL is believed to be used in over 80% of financial operations, while the US Social Security Administration uses approximately 60 million lines of COBOL code. 🔸 As COBOL programmers and developers began to retire, corporations battled to maintain their systems operationally, let alone upgrade them for the reality of the always-on internet. And this is just one of many languages that are still in use but do not reflect what current coders want to write in or what is best suited for modern commercial applications. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Code language translation is one of the issues attempted to be addressed with CodeNet. It has the potential to revive methodologies for updating outdated systems, assisting developers in writing better code, and even allowing AI systems to help code the computers of the future. 🔸 Eventually, allows an AI system to code a computer. It comprises around 14 million code samples, totaling 500 million lines of code from over 55 different languages. It includes examples of current languages such as C++, Java, Python, and Go and older languages like Pascal, FORTRAN, and COBOL. 🔸 On CodeNet, baseline studies for code categorization, code similarity, and code completion were run. When CodeNet users conduct their own tests, they can refer to these results as a guide. Because of CodeNet’s excellent quality, several studies show that models created from it generalize better across datasets than models developed from other datasets. 🔸 CodeNet can assist in developing systems that can detect what form of code a snippet is. They employed a variety of machine-learning approaches, including a bag of tokens, a sequence of tokens, BERT model, and graph neural networks (GNNs). ------------------------------------------------------------------- #computervision #artificialintelligence #innovation -------------------------------------------------------------------