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It is argued that the greater the number of potential innovators, the higher the rate of innovation. The implications are huge.įirst, there are consequences for research and development. Consequences of natural language codingĪlthough NLC is not yet fully feasible, we are moving quickly towards a future where coding is much more accessible to the average person. Copilot is a huge step towards NLC, but it is not there yet. The programmer can then accept the AI-generated sample, edit it or reject it, drastically simplifying the coding process. When the programmer inputs the action they want to code, Copilot generates a coding sample that could achieve what they specified. The company has added to GitHub’s potential with GitHub Copilot, an AI assistant.
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Microsoft also owns GitHub, - the largest collection of open source code on the internet - acquired in 2018. Soon after IBM’s news, Microsoft announced it had secured exclusive rights to GPT-3. GPT-3, OpenAI’s industry-leading NLP model, has been used to allow coding a website or app by writing a description of what you want. It is clear that IBM was not alone in its thinking. It could make tasks like designing a website as simple as typing “make a red background with an image of an airplane on it, my company logo in the middle and a contact me button underneath,” and that exact website would spring into existence, the result of automatic translation of natural language to code. NLC, in contrast, leverages NLP technologies and a vast database such as CodeNet to enable anyone to use English, or ultimately French or Chinese or any other natural language, to code. NLC applies all this to coding too.Ĭoding is a difficult skill to learn let alone master and an experienced coder would be expected to be proficient in multiple programming languages.
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Training machine learning systems requires access to a large dataset with texts written in the desired human languages. These are machine learning-driven programs designed to better understand and mimic natural human language and translate between different languages.
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In recent years, companies such as OpenAI and Google have been rapidly improving Natural Language Processing (NLP) technologies.
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IBM’s own stated rationale for CodeNet is that it is designed to swiftly update legacy systems programmed in outdated code, a development long-awaited since the Y2K panic over 20 years ago, when many believed that undocumented legacy systems could fail with disastrous consequences.Īn IBM-produced video looks at the quest to produce an AI that can understand human language.
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The dataset also contains numerous additional data, such as the amount of memory required for software to run and log outputs of running code. ImageNet is now central to the progress of deep learning computer vision.ĬodeNet is an attempt to do for Artifical Intelligence (AI) coding what ImageNet did for computer vision: it is a dataset of over 14 million code samples, covering 50 programming languages, intended to solve 4,000 coding problems.
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On May 5, IBM announced Project CodeNet to very little media or academic attention.ĬodeNet is a follow-up to ImageNet, a large-scale dataset of images and their descriptions the images are free for non-commercial uses. Sometimes major shifts happen virtually unnoticed.