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ChatGPT, Copilot and Programmers: Collaboration or Substitution?

Mai 16, às 17:30

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2 min de leitura

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1 leituras

Generative neural networks such as ChatGPT can create text, code, and images based on given queries. ChatGPT, developed by OpenAI, has gained enormous popularity and attracted one million users in just 5...
ChatGPT, Copilot and Programmers: Collaboration or Substitution?

Generative neural networks such as ChatGPT can create text, code, and images based on given queries. ChatGPT, developed by OpenAI, has gained enormous popularity and attracted one million users in just 5 days. It stands out for its ability to understand context and engage in meaningful dialogues.

The success of ChatGPT led Microsoft to invest $10 billion in OpenAI, recognizing the future potential of generative neural networks. However, while these networks have proven their usefulness in writing code, they are not expected to completely replace programmers. Programmers still play a critical role in formulating tasks, testing code, and ensuring maintainability.

Programmers must create error-free and readable code because changes may be needed at any time. They must also understand the project's context, including existing code, business logic, code design standards, and interactions with other services and APIs. Training a neural network to fully understand these aspects requires a significant amount of time, specific context, and a lot of data.

Generative neural networks such as Copilot have the potential to change the programming profession. They force programmers to break down tasks into smaller components for which code can be generated by a neural network, and programmers are responsible for testing and combining those pieces of code. Copilot has already attracted more than a million programmers and improved their efficiency.

The introduction of Copilot and similar tools may raise concerns about job cuts in programming. However, history shows that increases in programmer efficiency caused by advances such as high-level programming languages have not led to massive job losses. The demand for code continues to grow, and there are still many unresolved problems that require human expertise.

While Copilot can be a useful tool for automating routine tasks in development, its use as a mandatory tool can also make it difficult to get started in the profession. Automating routine tasks reduces their volume, but more complex tasks require greater understanding and expertise.

In conclusion, the interaction between generative neural networks such as ChatGPT and Copilot and programmers is a collaboration, not a complete replacement. Programmers will continue to play a key role in development because they have the necessary contextual understanding of the project and can provide flexibility and development.


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