Sometimes major shifts happen virtually unnoticed.
On May 5,IBMannounced Project CodeNetto very little media or academic attention.
ImageNet is now central to theprogress of deep learning computer vision.
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These are machine learning-driven programs designed to better understand and mimic natural human language and translate between different languages.
Training machine learning systems require access to a large dataset with texts written in the desired human languages.
NLC applies all this to coding too.
It is clear that IBM was not alone in its thinking.
Soon after IBMs news, Microsoft announced it hadsecured exclusive rights to GPT-3.
Microsoft also owns GitHub, the largest collection of open source code on the internet acquired in 2018.
The company has added to GitHubs potential withGitHub Copilot, an AI assistant.
Copilot is a huge step towards NLC, but it is not there yet.
The implications are huge.
First, there are consequences for research and development.
It is argued thatthe greater the number of potential innovators, the higher the rate of innovation.
By removing barriers to coding, the potential for innovation through programming expands.
Further, academic disciplines as varied ascomputational physicsandstatistical sociologyincreasingly rely on custom computer programs to process data.
However, there are also dangers.
Ironically, one is the de-democratization of coding.
Currently, numerous coding platforms exist.
Some of these platforms offer varied features that different programmers favor, however, none offer a competitive advantage.
A new programmer could easily use a free, bare bones coding terminal and be at a little disadvantage.
This makes it harder for new competitors, even if they have a stronger or more ethical product.
