This month’s “Communications of the ACM” magazine (01/2023) published a rather alarmist article titled ‘The End of Programming’. While it is a well written article, it bets heavily on the future usefulness of AI models like ChatGPT to generate working code, replacing the need for programmers to write code by hand. ChatGPT is currently getting a lot of attention in the media and online right now, with people finding out that not only can you ask questions on any topic and get a believable answer, you can also ask it a more practical question like “show me C code to read lines of a file”.
Finding out that ChatGPT can be used to ‘generate’ code is prompting questions online from new developers posting questions like ‘should I start a career in software development when programmers are likely going to be replaced by ChatGPT?’
The tl;dr answer: ChatGPT is not replacing anyone any time soon.
While development and improvement of these types of AI model is going to continue, it’s worth keeping in mind that these models are only as good as the material they are trained on, which also means they’re limited by the correctness or usefulness of the material used for training. This also means they are subject to the age old problem of ‘garbage in, garbage out’. What’s not being discussed enough is that these current models do not understand the content they generate. They also have no understanding of whether any of generated content is correct, either factually correct for text, or syntactically correct for code snippets. Unlike these ML trained models, as humans we use our existing knowledge and experience to infer other missing details from what we read or hear. We’re also good at using our existing knowledge to assess how correct or realistic new information is based on what we already know to be true. AI models currently do not have this level of understanding (although research has been attempting to replicate ‘understanding’ and ability to make decisions based on existing facts for years (Google ‘expert systems’ for more info).
I’ve seen developers recently attempting to answer questions on Stack Overflow, Reddit and other sites using ChatGPT, with and without success based on whether the topic of the subject was within the scope of materials the model was trained with.
The current problem with text generation from models is that the models lack context. The current models don’t understand context, and so can attempt to generate a response based on identifying key words from the input prompt, but that doesn’t always result in an answer the same way as if a human would answer the same question. Model also don’t understand intent. A question can be asked in a number of similar but different way, and to another human you may be able to infer the intent or purpose of the question, but to a current general purpose trained ML models, that’s currently not possible.
In its current form, ChatGPT is trained on materials currently available online, websites with both static articles and reference materials, as well as question and answer discussion sites. The limitation with this approach is that if I ask a very specific question like ‘show me example code for building a REST api with Spring Boot’, there are plenty of examples online and assuming the model was trained on at least some of these, then the resulting answer could incorporate some of this material. The answer isn’t likely to be better than anything you could have found yourself online if you just Googled the same question. There could be some benefit from having an answer as a conglomeration of text from various sources, but that can also mean that the combined text ends up being syntactic gibberish (the model doesn’t currently know if what it’s returning to you is syntactically correct).
It’s clear that there is promise in this area to be used to aid and support developers, but as a complete replacement for all custom software development work in it’s current form, this seems highly unlikely, or not at least within the next 10 years, and possibly even longer.