5 Must-Know AI Concepts In 2021

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Prompt programming — Human communication

Low-code and no-code initiatives appeared a few decades ago as a reaction to the increasingly large skill gap in the coding world. The technical ability to create good code and know how to handle tasks at different points in the design-production pipeline was expensive. As software products got more complex, so did the programming languages. No-code aims at solving this gap for non-technical business people. It’s an approach that bypasses coding to make the results accessible to anyone.

Knowing how to code is arguably as important as speaking English was a few years ago. You either knew or you were missing a lot. Job opportunities, books and articles, papers, and other technical work… In the future, the percentage of smart houses — domotics — will increase. Technical software skills may be as important then as now is it knowing how to fix a pipe or a broken light.

At the intersection of no-code initiatives and the future of AI, we have prompt programming. GPT-3 is the best-known AI system that uses prompts. OpenAI released the API last year, and people soon recognized the uniqueness of prompting. It was something different; neither talking to a human nor programming in the formal sense. Prompt programming, as Gwern calls it, can be understood as a new form of programming. It isn’t as superficial as no-code, because we communicate with the system — we program it — in natural language. And it isn’t as highly technical as programming in C or Python.

GPT-3 caught the attention of researchers and developers, and many were motivated to find its shortcomings. Some found that GPT-3 failed where it should have succeeded. However, Gwern proved them wrong. He argued we should approach GPT-3 as if we were programming it in English. We have to do it right, not everything goes. He repeated the tests tweaking the prompts and succeeded in teaching GPT-3 to do the tasks correctly. He said:

“[Prompting] is a rather different way of using a DL [deep learning] model, and it’s better to think of it as a new kind of programming, where the prompt is now a “program” which programs GPT-3 to do new things.”

GPT-3 sparked the possibility of programming a system by writing in English. The system could understand our intentions and translate them to the computer in a way it could interpret them without uncertainty.

A month ago, Microsoft — who partnered with OpenAI last year — and GitHub released GitHub Copilot. The system, fueled by a descendent of GPT-3 called Codex, was created to be a powerful code autocomplete. Microsoft saw the potential of GPT-3 in creating code and how it could understand English and transform it into well-written, functional programs. Copilot can, among other things, read a comment that describes a function in English, interpret it, and write down the function.

GPT-3 and GitHub Copilot combine the promises of no-code and the potential of prompt programming into a new era that will allow non-technical people access to the world of coding.

The main advantage of prompt programming and the reason why it’ll be successful is that we humans have evolved to communicate in natural language, not in formal languages. English has a series of rules that we intuitively know. We learn to speak correctly way before we understand the rules we’re using. We don’t invent the rules and then stick to them. We discover the rules we’re already following.

Writing Python or C is different. We call them languages but they are distinct from English in significant ways. Computers need unambiguous, uninterpretable commands to know what to do. Programming languages have strict syntax rules that can’t be broken or the program won’t run. There aren’t shortcuts to this. Without prompt programming, if you want to communicate with a computer, you have to learn its language. Even high-level languages such as Python require a notable degree of technical expertise that most people don’t have.

Prompt programming is the future of coding: We’ll be able to program most things in natural language. There will be intermediate systems tackling the translation between our inexact, nuanced, and context-filled thoughts and the formal set of instructions computers need to work.

AI/ML

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