ChatGPT vs Developers?

The natural language chatbot ChatGPT has been developed by OpenAI and has recently been released, gaining popularity as people use it to write emails, essays, and even Python code. While it has sparked hope among those eager for practical applications of AI, there are concerns that it may displace writers and developers in the same way that robots and computers have replaced cashiers and assembly-line workers.

It is difficult to predict the level of sophistication that AI text-creation will attain in the future, as it continues to learn from online writing examples. However, I believe that its programming capabilities will remain limited. At most, it may become another tool for developers to handle simpler tasks that don't require the critical thinking skills of a software engineer.

ChatGPT has garnered widespread admiration for its ability to mimic human conversation and convey knowledge. Created by OpenAI, the same team behind the popular DALL-E text-to-image AI engine, it relies on a large language model trained on massive amounts of text sourced from the internet, including code repositories. The system employs algorithms to analyze text, with human trainers fine-tuning its responses to user queries, resulting in full sentences that sound like they were written by a human.

Although ChatGPT has been praised for its ability to simulate human conversation and sound knowledgeable, it has limitations that make it unreliable for generating code. Unlike human intelligence, ChatGPT is based on data and can produce sentences that appear coherent but lack critical thinking skills. It can also reuse offensive language, and its responses may sound reasonable but be highly inaccurate. For instance, ChatGPT may confidently provide an incorrect answer when asked which of two numbers is larger.

OpenAI has provided an example of using ChatGPT to assist in code debugging, but this approach is not foolproof. The responses generated by ChatGPT are based on prior code and do not replicate human-based quality assurance. As a result, ChatGPT can generate faulty code with errors and bugs. OpenAI has acknowledged that ChatGPT sometimes produces incorrect or nonsensical responses, which is why it should not be used directly in the production of any programs.

The reliability issues of ChatGPT are causing problems for developers. The question-and-answer site Stack Overflow temporarily banned the use of ChatGPT due to the high volume of responses it generated, which made it difficult for humans to maintain quality control. According to Stack Overflow, ChatGPT's ability to provide correct answers is low, and its use is detrimental to users looking for accurate responses.

Aside from coding errors, ChatGPT, like all machine learning tools, is trained to generate text, which limits its understanding of the human context of programming. To create good software, developers must understand the software's intended purpose and its users, and it cannot be done by simply piecing together code generated by an algorithm.

Despite the rapid advancements in machine learning systems, they still lack the ability to mimic human thinking. This has been the reality of artificial intelligence research for more than four decades. Although these systems can improve productivity by identifying patterns, they may not be as efficient as humans in generating code. It may be necessary to ensure that machine learning systems, such as AlphaCode, can rank in the top 75% of participants on a platform like Codeforces before allowing them to generate code on a large scale, although this might be too challenging for such a system. Nonetheless, machine learning can assist with simple programming tasks in the future, freeing up developers to focus on more complex challenges.

ChatGPT's current capabilities are unlikely to disrupt the field of software engineering, and fears of robots replacing programmers are exaggerated. There will always be tasks that require the cognitive abilities of human developers that machines will never be able to replicate.