If you’re a computer programmer or computer engineer, then you’ve probably been in awe of the potential shown by current software.
ChatGPT was unveiled in beta form just before Christmas. It is a chatbot powered by the GPT-3 model of large-scale language (LLM) designed to use generative AI and natural language processing (NLP) to produce speech that is easily distinguishable from human input. Due to its impressive capabilities, it started to spread and till now it has gathered millions of users.
Ask them to write a poem about trees in the style of Shakespeare, or an essay about the use of AI in industry, and that’s what you’ll get.
Authors, copywriters, and journalists are confident that, although it can produce impressive results, ChatGPT has not reached the point where they are immediately worried about their jobs. The prose writings they produce are impersonal, difficult to make factual mistakes, and are created primarily with the goal of including all the essentials. This means that it doesn’t take into account whether the result is interesting, funny, scary, or capable of evoking any other emotions the author wants to convey. All of these things are important if your writing is to attract readers.
When it comes to coding, nothing is more important – what is important is that the software that is created does the job it is supposed to do. It either works, or it doesn’t. Translators who take human (or machine) code and run it as a job can’t stop reading it halfway through because it’s not fun!
Are ChatGPT and NLP dangerous for software and software engineering jobs?
Despite all of this, it seems that the current thinking is that ChatGPT and other NLP technologies available today do not make all coders, programmers, and software engineers any less.
At first, it can create simple programs. Ask for something more difficult – such as a high-level game or a business project, and it will admit its weakness and tell you that the task is currently beyond its capabilities.
Computers cannot tell us, for example, what types of code or programs are needed to accomplish what we are trying to do. Although it knows this because we say it, then ChatGPT, in particular, cannot (yet) try to create an application that gives us as users a competition with other application users.
For example, we can’t say “build me an e-commerce platform that is more efficient in terms of sales than Amazon.” If we wanted this, then we still have to spend time and effort to find out what makes the Amazon platform so great and find a way to do it right.
Because of this, ChatGPT (and other modern NLP tools) still have little power in creating programs designed to give us an edge in business or, indeed, to compete with human skills and intelligence.
One caveat here is that while we can do our best to predict what will happen in the future, in reality, no one has a crystal ball. It’s fair to say that many people who are used to AI speaking at the level of Alexa or Siri have been surprised by how good ChatGPT is.
Future developments may increase the speed at which we move to a point where human programmers – or many other experts – will no longer be needed. However, for now, we can be confident that there are a variety of skills needed to create software that computers don’t seem to be able to replicate anytime soon.
So how can ChatGPT and other NLP tools be used by developers today?
Developers I’ve spoken to about ChatGPT – and the technology’s future evolution – have told me that rather than a threat, at this point, it’s a valuable tool.
It can be used to quickly create configurations, and show how they are created, providing answers to questions such as how the data should be created and the required features.
Because of this, it can be good as “box-ticking” support – in other words, making sure that your code process covers all the bases that are needed for your project to work.
It has been written many times that as soon as ChatGPT makes job titles less, it will create new ones. ChatGPT seems to have the potential to speed up many routine tasks – writing and other responsibilities – but doing so will involve new skills. This includes developing the necessary skills to come up with guidelines that will help them do what is needed.
As mentioned above, tell them to create a complex program, and they will shrug their shoulders. But tell it to break down the tasks it needs to do into smaller parts and start working on those parts one by one, and it’s possible to start getting somewhere.
So it’s unlikely that all the years you’ve spent learning about coding and software engineering have gone to waste. You will still need knowledge and experience to help you choose the right strategy and ensure that ChatGPT’s output is successful.
As one coder I spoke to about this told me, “I see my role as probably a manager – I’ll be overseeing teams of robot programmers and doing things they can’t do. For example, coming up with basic ideas for what kind of software they need.” “
In addition to generating new code, another useful function of ChatGPT is to remove existing code (or code that has been generated automatically). Any programmer can tell you that debugging – the process of finding and fixing errors – is often a complex process that requires looking at a lot of code to see what went wrong. According to those who are already using it to help with writing, ChatGPT can completely change the process, but it can explain why the code does not work.
For the past five years or so, I’ve been talking to people about how AI will affect their jobs or industries, and the one word that comes up in almost every conversation is “augmentation.”
This means that those of us who know how to take new technology and use it to improve our skills can do better. This means using it to create low-cost, repetitive tasks that previously would have filled most of our time. At the same time, we need to think about how to make better use of the time it frees up to make better use of the skills that computers have yet to replace – creative thinking, strategic planning, problem solving, and emotional intelligence are some of the best examples. Taking this approach to the breakdown of AI tools and services in our industry – whether you’re a computer scientist or a doctor – is the best way to ensure we remain relevant and relevant in the age of AI.
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