The recent statement by Nvidia’s CEO, Jen-Hsun (Jensen) Huang shook the general public’s thoughts on an education that was just recently been promoted strongly by governments around the globe for K-12 education.
This was about getting kids to learn programming. Jensen’s statement at the World Government Summit in mid-February 2024 [1] was “It is our job to create computing technology such that nobody has to program. And that the programming language is human, everybody in the world is now a programmer. (This is the miracle) This is the miracle of artificial intelligence.”
This was interpreted in many ways, mostly on the need for programming education. Some even go as far as stating that there is no longer a need to study computer science, probably they inferred it from Jensen’s “computer science” statement just prior to the quote above.
Jensen is absolutely right in his statement. As far back as 1995, the Unified Modelling Language (UML) was created at a company called Rational Software (acquired by IBM in 2003). This UML quickly became a standard and paved the way to automated code generation and is part of the Rational Rose software application. Granted that it was initially just producing the skeleton of the code for the object-oriented programming paradigm, but it was taking structured requirements and design documentation from software engineers to produce the code skeleton.
Of more recent advancements, Microsoft introduced the Semantic Kernel, which can be used to enable natural language query for their databases. It is able to do so with integration to the technology called Large Language Models (LLM), which powers OpenAI’s ChatGPT, Google’s Gemini, Microsoft’s co-pilot and Meta’s Llama. LLMs are able to take a rather unstructured human language and provide the necessary structured query for the database engine.
We are now at a stage where technology is advancing at a rapid pace, and in my opinion, one of the two trends in the industry now is low-code platforms, the other being digital twins. Low code application development platforms, such as Oracle APEX [2] and Joget [3], allow business analysts with some programming capability in querying databases to build business applications. This will soon improve to a level where the business analyst may just provide the platform with human languages to build the business applications. Hence, Jensen is definitely right in that “nobody has to program” in computer programming languages but will do so with human languages.
Prompt engineering will become an everyday skill for everyone, much like knowing how to ask Google search appropriately to get the information that you need. Command of the human language is hence key. It does not have to be English, but a language where there are many technology tools built for it, e.g., Chinese.
It should be noted that Computer Science is not just programming. Programming is basically instructions from a human to ask a computer how to process data or information.
Programming has been democratised, in that anyone with some form of coherent thinking can learn it from numerous online sources. But computer science is NOT about programming, it is not about how many programming languages you will learn, but it is about how computers work. If you look at it from a programming language perspective, computer science is about how programming languages work.
This equates to how AI (in the current situation, Large Language Models) works to generate the necessary instructions for the computer. Hence, coming back to Jensen’s statement, “our job is to create computing technology” means that we need engineers, we need computer scientists, and we also need information technology specialists. The latter is that these machines the AI are running on, also need professionals to keep them running.
Why do we need to learn what AI can do for us?
The advancement and development of AI require humans, and before humans can do that, we need to learn the fundamentals of how machines work, and how machines receive instructions. Another way to look at it is that more than two decades ago (in 1997) AI has already beaten one of human’s greatest Chess Grandmasters, Gary Kasparov.
Yet we still get our children to learn to play chess and we are proud if our children represent their school’s chess team. Gary Kasparov explained why we still need to teach our children to play chess. He stated in a TedTalk titled “Don’t fear intelligent machines. Work with them” [4] in 2017 that “Machines have calculations. We have understanding. Machines have instructions. We have purpose. Machines have objectivity. We have passion.” Much like us still learning how to master chess, even though machines are better at it than us, is akin to why we still learn how to master computer science and engineering.
We need to understand how it works so that we can make it better. Humans have a purpose, and pursuing science is for the PURPOSE of the betterment of mankind. Most of which is to assist us in what we need to do for a better quality of life, and this is to enhance efficiency and productivity. Will AI help us code, definitely! Should we study Electronics Engineering and/or Computer Science? Absolutely! Else who is going to continue the development of AI?
Should we study Information Technology, also absolutely! Else who is going to upkeep and maintain these AI machines?
Lastly, humans have dreams! Let’s continue to build and develop these machines intelligently to turn our grandest dreams into reality. We should not grow complacent with what we have now; we need to have the passion to understand and have a purpose to succeed in our dreams.
The author, Dr. Ian Tan is an Associate Professor in Computer Science and serves as the Head of the School of Mathematical and Computer Sciences (MACS) at Heriot-Watt University Malaysia (HWUM).
