Everyone Can Code: The Democratization of Coding

Everyone can code

Yep. You read that right! Everyone can code, and I’m not talking about Apple’s coding curriculum introducing elementary and middle school students to the world of programming. Nope. That’s not what I’m going on about. I am explicitly referring to low code and no-code platforms and machine learning AI (artificial intelligence) tools such as GPT-3 and Deepmind that have recently come into the tech sphere.

I’ll start by explaining low code and no code tools before jumping into GPT-3 and Deepmind – to give you a small sense of how these technologies will change programming as we know it today.

Low Code vs. No Code

So what’s the difference between low code and no-code platforms?

First things first, low code platforms are typically used for rapid application development, and low code development environments allow developers to create low-cost applications. Think – low code = low cost! On the flip side, no-code platforms allow users (not just developers) to design low-cost apps by dragging and dropping components, adding images and videos.

Low-code platforms require a developer to be familiar with code syntax and documentation. No-code, as the name implies, means zero code. Instead, the developer interacts with a visual interface where they can drag and drop elements – a completely visual approach to building applications.

The low code, no-code approach to building software has gained popularity due to two main reasons:

  1. Cost Savings: Low-code and no-code platforms allow developers to work faster at a lower cost. Why? Well, low code development environments typically require less initial investment from the client/business. There’s no need to contract out or hire a dedicated team of coders if you want to build an app. In fact, low-code and no-code platforms allow a business to build a low-cost prototype – something you can show investors or release as a minimum viable product.
  2. A Shortage of Software Engineers: The digital transformation catalyzed by the COVID-19 pandemic has increased the demand for business applications. This has exacerbated the shortage of software engineers that existed even before the pandemic. For example, the latest data from the US Bureau of Labor predicts that between 2020 and 2030, there will be over 1.8 million software developer jobs, only about 400,000 software engineering graduates. This translates to 1.4 million unfilled jobs during the forecast period.

(Image source: Bls.gov)

The situation is similar globally, with most countries reporting a shortage of software engineers. Therefore, this trend has led organizations to look to low code no-code tools to address this challenge. As a result, Gartner predicts that 80% of programming will be delivered by non-techies using these tools by 2024 – everyone will be coding!

Generative Pre-trained Transformer 3 (GPT-3)

GPT-3, or GPT 3 for short, is a deep learning autoregressive language model developed by OpenAI, a San Francisco-based artificial intelligence research laboratory. OpenAI’s third-generation language prediction model in the GPT-n series (and successor to GPT-2). The complete version of GPT-3 has a memory of 175 billion machine learning variables. Pre-trained language representation systems are part of a natural language processing (NLP) trend.

GPT-3’s text quality is so high that it may be difficult to distinguish whether a person created it.

Software developers can write code using natural language via GPT-3.

What is Natural Language Processing, and Why is it Important for Software Development?

Natural Language Processing (NLP) is the process of analyzing text to extract data. GPT-3’s ability to understand natural language means that GPT-3 can be used by software developers as a GPT-3 interface lets them code using natural words instead of a programming language.

Essentially, GPT-3 makes it possible to code without knowing standard computer coding languages like Python or Java. For example, let’s assume you want to write computer code to input the number of cars in a garage. GPT-3 could understand natural language. With GPT-3, you would input the question “how many cars are there in my garage” GPT-3 will then analyze your statement and output program code that is ready to be executed.

With GPT-3’s ability to process natural language text inputs, developers can now build apps using code generation interfaces with little or no coding required! In fact, some of the programming tools running on GPT-3 have a complete suite of tools that include bug testing, GIT integration, CI/CD tools for apps with the capability of scaling them to production with high availability.

The GPT-3 Demo website showcases over 300 applications running on GPT-3.

DeepMind

If you follow the news, you’ve probably heard of Google’s DeepMind. It is a well-known AI research company most well known for developing a system that can beat human experts at the strategy game GO.

DeepMind recently developed an AI system named AlphaCode that it claims is able to write computer programs at a comparable level to humans. The Alphabet company’s program was put to the test against coding challenges popular in human competitions and achieved an “estimated rank” placing it among the top 54 percent of human coders.The end result is a significant leap forward for autonomous coding, according to DeepMind. AlphaCode is also based on an AI architecture known as a transformer, which is particularly good at analyzing sequential text, both natural language and code.

Also read: You’re Fired! How Automation and AI May Take Over Your Job

The Future of Coding

“Everyone Can Code” is no longer just a marketing slogan. It is fast becoming a reality thanks to tools like deep learning and GPT-3, which provide natural language interfaces for software development. Instead of having teams of hundreds of engineers coding apps, now just a few people will be required to build an app with little or no coding. Simply input natural language to GPT-3 and similar tools, and it will output the code you need!

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