Programming in ‘natural’ language is coming sooner than you think
Accelerating machine learning
IBM’s own stated rationale for CodeNet is that it is designed toswiftly update legacy systems programmed in outdated code, a development long-awaited sincethe Y2K panic over 20 years ago, when many believed that undocumented legacy systems could fail with disastrous consequences.
However, as security researchers, we believe the most important implication of CodeNet — and similar projects — is the potential for lowering barriers, and the possibility of Natural Language Coding (NLC).
In recent years, companies such asOpenAIand Googlehave been rapidly improving Natural Language Processing (NLP) technologies. 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.
Coding is a difficult skill to learn let alone master and an experienced coder would be expected to be proficient in multiple programming languages. 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. 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.
It is clear that IBM was not alone in its thinking. GPT-3, OpenAI’s industry-leading NLP model, has been used to allowcoding a website or app by writing a description of what you want. Soon after IBM’s 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 GitHub’s potential withGitHub Copilot, an AI assistant. When the programmer inputs the action they want to code, Copilot generates a coding sample that could achieve what they specified. The programmer can then accept the AI-generated sample, edit it or reject it, drastically simplifying the coding process. Copilot is a huge step towards NLC, but it is not there yet.
Consequences of natural language coding
Although NLC is not yet fully feasible, we are moving quickly towards a future where coding is much more accessible to the average person. 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. Decreasing the skill required to create these programs would increase the ability of researchers in specialized fields outside computer sciences to deploy such methods and make new discoveries.
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.
However, AI at the level required for NLC is not cheap to develop or deploy and is likely to be monopolized by major platform corporations such as Microsoft, Google or IBM. The service may be offered for a fee or, like most social media services, for free but with unfavorable or exploitative conditions for its use.
There is also reason to believe that such technologies will be dominated by platform corporations due to the way machine learning works. Theoretically, programs such as Copilot improve when introduced to new data: the more they are used, the better they become. This makes it harder for new competitors, even if they have a stronger or more ethical product.
Unless there is a serious counter effort, it seems likely that large capitalist conglomerates will be the gatekeepers of the next coding revolution.
Article byDavid Murakami Wood, Associate Professor in Sociology,Queen’s University, OntarioandDavid Eliot, Masters Student, Surveillance Studies,Queen’s University, Ontario
This article is republished fromThe Conversationunder a Creative Commons license. Read theoriginal article.
Story byThe Conversation
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