Introduction
Dear Reader, I hope you are doing well and not too stressed about the impacts of AI evolution in our lives. In my previous posts chatGPT Wrote me a Christmas Poem and Python coding with chatGPT, I covered various topics related to using chatGPT for writing poems and learning Python coding. Today, I want to share my latest insights on utilising chatGPT in my blog posts and coding endeavours and discuss whether we should be concerned about the changes needed for programmer jobs.
In this post, I delve into the practical considerations of adapting to the new coding age. I highlight the tremendous opportunities that GPT technology brings, such as quicker product releases, a focus on user requirements, access to well-tested code examples, fast learning to code, and a shift towards effective coding practices. We’re already witnessing the emergence of new start-ups leveraging these advancements.
However, I also want to note the challenges we must prepare for. Some low-coding jobs may be delegated to AI, potentially impacting entry-level developer positions. New skills for AI-assisted programming will need to be developed, and there might be hidden knowledge and know-how accessible only to select individuals. Additionally, affordability issues may arise for small companies or individual developers seeking access to sophisticated AI models.
Adapting and preparing for the changes that AI evolution brings to the programming landscape is crucial. We can successfully navigate this evolving field by embracing AI-assisted programming, developing the necessary skills, and finding solutions to the critical challenges.
GTP: Opportunities and Challenges
In the past decades, we have seen programming shift from Machine code, Assembly language, Punch cards, functional, OOP, and AI-assisted code generation. Programming with tools such as GPT will undoubtedly change how we develop. First of all, I want to mention the few but tremendous opportunities that the GPT technology brings:
- Quicker product releases and new startups will come. This is happening. Think about a large number of AI startups that use GPT technology for generating images or content)
- Focus on the user requirements rather than re-inventing code wheels. We might aim to domain specialisations for creating applications that fit user requirements the best,
- Using state-of-the-art and well-tested code examples. The benefits, such as time and resource savings, should be considered.
- Fast learning how to code and improve an existing code with the help of GPT bots.
- Moving from how to do it to how to do it effectively.
There are also challenges to being ready for GPT-assisted coding:
- Some low-coding jobs will be delegated to AI, and some starting developer positions will not be available to humans.
- New skills in using AI-assisted programming will be developed, and some hidden knowledge and know-how might not be available to the common public.
- Access to the GPT knowledge base may not be affordable for small companies or sole developers who want access to sophisticated models requiring payment.
Personal Experience
GPT tools such as chatGPT have yet to be ready to bring tangible benefits for writing well-optimised code. Based on my experience of using chatGPT for the few months since chatGPT was released, the GPT model will help you to learn how to do coding. However, it is only useful when you know how things should be realised in practice.
We need expert knowledge of what is required and how things are done to get accurate and practical results with assisted programming. Programming is more than coding; it also requires a vast amount of background knowledge and experience. As you might realise from my post Python coding with chatGPT, wherein I have implemented a neural network, accepting the Python code generated by chatGPT on the first try would be useless. It is still necessary to understand how neural networks are created and what are the backpropagation and activation functions.
I have briefly explained neuron activation and the activation functions in the post Artificial Neural Networks.
Conclusion
The evolution of AI, particularly the use of GPT technology, introduces both exciting opportunities and significant challenges in the field of programming. While AI-assisted code generation enables quicker releases, a focus on user requirements, and access to well-tested code examples, it also poses risks to specific job positions and the accessibility of knowledge. Adapting to the new coding age requires developing skills in AI-assisted programming and finding ways to overcome affordability barriers. Programmers must embrace the changes and leverage the benefits while navigating the challenges to stay relevant and thrive in this evolving landscape.
Did you like this post? Please let me know if you have any comments or suggestions.
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About Elena Elena, a PhD in Computer Science, simplifies AI concepts and helps you use machine learning.
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