ChatGPT is about to replace programmers

 

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I believe that everyone is familiar with ChatGPT, and we often use it in work and study. But ChatGPT is growing fast. The functions are also more and more comprehensive. ChatGPT's articles also appear in endless layers, and the news that ChatGPT is about to replace programmers is also overwhelming. Will ChatGPT really replace programmers? Should we be worried?

Table of contents

What is ChatGPT

The development history of chatgptEdit

Analysis of ChatGPT

The impact of ChatAI on future careers

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Let's first understand what is ChatGPT?

What is ChatGPT

ChatGPT is a dialogue generation system based on the GPT model developed by OpenAI. It is built on the basis of the GPT-3 model and its predecessor GPT-2, and is specially designed to handle dialogue tasks. ChatGPT can receive user input and generate reasonable and coherent responses, thereby simulating natural language dialogue interactions.

ChatGPT uses a large-scale pre-training dataset to learn language knowledge and language patterns. Trained by a large amount of dialogue data collected on the Internet, ChatGPT is able to understand semantics and context, and generate appropriate responses. It can have a dialogue with the user, answer questions, provide advice, provide information, etc.

ChatGPT's architecture is based on the Transformer model, which uses a self-attention mechanism to handle dependencies between text contexts. This enables ChatGPT to encode input sentences and leverage context information when generating replies. The model also trains a generator with parameters to produce an output answer given the input.

In layman's terms: ChatGPT is like a smart conversational robot that can have conversations with people. You can ask it questions, chat or seek advice, etc., and it will give reasonable answers. It is trained by learning from large amounts of conversational data so that it can understand the meaning of the language and generate appropriate responses based on the context.
It works a bit like a giant notepad filled with knowledge about the language. When you talk to it, it looks at this notebook and uses what it has learned to generate an answer. It understands your question based on previously seen conversations and tries to give the most appropriate answer.
However, ChatGPT sometimes makes mistakes or gives inaccurate answers. This could be because it doesn't have enough context to understand the question, or the knowledge in Notepad is incomplete. So, just like talking to people, sometimes ChatGPT's answers may not be perfect. But you can provide more information or redirect it to help it improve and give better answers.
In general, ChatGPT is a well-trained model that can conduct human-machine dialogue, but it should also be noted that it may sometimes have some limitations.

The development history of chatgpt

The history of ChatGPT can be traced back to the first version released by OpenAI in 2015: Text-based AI. This is a Recurrent Neural Network (RNN) based model for generating text responses. Although this model performs well on some tasks, it has some problems in handling long-term dependencies and consistency.

In order to solve these problems, OpenAI released the first version of the GPT (Generative Pre-train Transformer) model in 2018. This is a model based on the Transformer architecture, which uses a self-attention mechanism to handle long-term dependencies between text contexts. GPT is pre-trained in an unsupervised manner, and trained through a large amount of Internet text data, so that the model can learn a wealth of language knowledge.

GPT-2 is the next version of the GPT model, released in 2019. It has made some improvements on the basis of GPT and achieved significant improvements in model size and performance. With more parameters and a deeper network architecture, GPT-2 can generate longer, more coherent text responses. Due to its powerful generative capabilities, OpenAI initially decided not to release GPT-2 fully publicly, fearing that its misuse could lead to the spread of misleading and disinformation.

In 2020, OpenAI released GPT-3 again. GPT-3 is a relatively complete version, with 175 billion parameters, which is several times that of the previous version. It achieves impressive results on several natural language processing tasks and demonstrates high creativity and adaptability. The neural network size and performance of GPT-3 make it one of the most powerful natural language processing models available today.

On March 15, 2023, on Tuesday local time in the United States, the artificial intelligence research company OpenAI released its next-generation large-scale language model GPT-4, which is its latest AI large-scale language model that supports applications such as ChatGPT and the new Bing. The company said the model performed "beyond human levels" in many specialized tests. GPT-4, a qualitative leap compared to ChatGPT (GPT-3.5), this iteration speed is too fast! Generally speaking, the logical reasoning ability is stronger, the language ability is stronger, and various tests have basically exceeded 90% of human beings!

Analysis of ChatGPT

ChatGPT has significantly improved in each generation of updates:

GPT-1: This is the first generation version of GPT, which uses the Transformer architecture and self-attention mechanism. GPT-1 excels at handling long-term dependencies and contextual coherence, showing significant improvements over RNN-based models.

GPT-2: GPT-2 is the second-generation version of GPT with larger model size and capabilities. This version has more parameters, a deeper network structure, and shows greater coherence and understanding when generating text. GPT-2 has achieved excellent results on various natural language processing tasks and demonstrated strong creativity.

GPT-3: GPT-3 is the third-generation version of ChatGPT, which has billions of parameters, is larger than GPT-2, and is widely regarded as a very adaptable natural language processing model. GPT-3 has shown amazing ability to generate long text, understand complex questions and dialogues.

GPT-4: GPT-4 is the fourth-generation version, and it is also the strongest and most mainstream version at present. It uses 1.5 trillion parameters, which is 10 times more than the previous generation, and has a significant improvement compared to GPT-3. It has been upgraded from a natural language processing model to a multi-modal model. Compared with GPT-3, complex functions such as answering pictures, data reasoning, and role-playing have been added. The length of text input has also increased from the initial 3,000 words to 25,000 words. With greater creativity and adaptability.

 Through the CPU performance graph of each generation in the above figure, we can also get an interesting and terrifying phenomenon, the performance of the CPU increases exponentially, and the speed is very amazing.

Let’s talk about memory and computing power. Chatgpt’s current memory is about a few hundred G, and it has about 400 billion word storage. For computing power equivalent to more than 100 GPUs, the processing speed must not be underestimated. These are Ability that ordinary people can't imagine.

The training method of ChatGPT is divided into two parts: pre-training and fine-tuning.

Pre-training: In this stage, the model uses a large-scale text dataset for unsupervised pre-training. Typically, ChatGPT uses massive text data from the Internet to learn knowledge and patterns of language. The goal of pre-training is to let the model learn to understand language, text coherence and contextual association as much as possible. The techniques used in the training process include predicting the next word (Next Word Prediction) and mask language modeling (Masked Language Modeling).

Fine-tuning: After pre-training, ChatGPT needs to be fine-tuned to fit a specific task or domain. The fine-tuning phase uses supervised learning, where the model is fed a task-specific dataset and trained on task-specific labels. This process can be used to tune and improve the response quality of ChatGPT by providing example conversations or additional training of the model. The fine-tuning stage aims to optimize the performance of the model and make it perform better in real dialogues and tasks.

 This way of learning also gives it many advantages:

1. Unsupervised learning: ChatGPT uses unsupervised learning method for training, which means the model can learn from a large amount of unlabeled data. Compared with traditional supervised learning which requires labeled data, unsupervised learning is more scalable and cost-effective. ChatGPT is able to generate fluent and coherent responses by self-supervised learning on large amounts of text data, capturing the underlying language patterns and structures.

2. Large-scale training: ChatGPT models are usually trained using large-scale training data sets, such as a large amount of text data on the Internet. Such a training data scale can help the model learn a wider range of language knowledge and context understanding.

3. Context-aware: The ChatGPT model is trained in an autoregressive manner, allowing context to be modeled to generate context-sensitive responses. The model can understand the user's intention through the previous dialogue history, and generate corresponding answers, showing a certain logic and consistency in the dialogue.

4. Flexibility and Diversity: ChatGPT is a generative model with some creativity and diversity. It can not only generate accurate answers, but also creative text generation to a certain extent. This flexibility makes ChatGPT potential for generating conversational content, authoring stories, or producing novel texts.

In just a few years, ChatGPT has gone from being incomplete to ready to go. We should lament the speed of technological progress, and we should also be afraid of professional threats. 

 

ChatGPT is awesome, but it doesn't have to be perfect. So what's wrong with it?

1. Comprehension limitations: Although ChatGPT is good at generating answers, it doesn't really understand the meaning of language. It mainly relies on pattern recognition and statistical regularities to generate responses, but lacks a deep understanding of semantics and context. This limits its ability to deal with complex problems, resolve logical errors, or perform abstract reasoning.

2. Lack of real-time learning and continuous improvement: ChatGPT is trained on fixed training data, it cannot learn and adapt to new information or changing environments in real-time. This makes it inflexible to adapt to different scenarios and deal with new problems. In contrast, human programmers can improve and optimize solutions through continuous learning and experience. Therefore, ChatGPT has limitations in real-time problem solving and continuous improvement.

3. Data dependency: The performance and quality of ChatGPT is affected by its training data. If the training data contains bias, errors, or inaccurate information, the model may carry or transmit these problems. Furthermore, if the model is subjected to malicious training data, such as misleading or harmful input samples, it may generate wrong or harmful responses. Therefore, it is especially important to review the accuracy and quality of the training data.

4. Lack of judgment and ethical considerations: ChatGPT does not have the ability to make autonomous decisions, it only generates answers based on training data and patterns. That means it lacks the judgment and ethical considerations to independently assess which solution is best, or to identify and correct bias or inappropriate content. In some cases, it may generate inaccurate, false or harmful responses requiring human intervention and screening.

5. Security and Privacy Issues: Since ChatGPT is an open generative model, there are risks of misuse and misleading. It may be used to generate disinformation, spread misleading content or offensive language. In addition, ChatGPT may also store and process user conversation data, raising privacy and security concerns. Therefore, you need to pay special attention to security and privacy protection when using ChatGPT.

However, artificial intelligence has a history of more than 60 years since it was officially born, and it has only been 5 years since GPT started training. It is impossible to imagine what it will look like if it is given 10, 50, or 100 years.

The impact of ChatAI on future careers

Mankind has experienced three industrial revolutions, and each revolution has added color to human civilization. I believe that in the not-too-distant future, mankind will usher in the fourth industrial revolution—artificial intelligence

Human beings will definitely be replaced by artificial intelligence in certain fields, and ChatGPT is only a representative of artificial intelligence. The large-scale application of artificial intelligence will affect many industries. 

Here are some examples of possible replacements:

1. Simple customer service reps: ChatGPT can serve as an automated replacement for customer service tasks that deal with common issues and provide basic support. It can reduce the need for human customer service representatives by automatically answering frequently asked questions and providing basic guidance.

2. Certain types of data analysis and report writing: ChatGPT can be used to generate basic data analysis reports and summaries. For some routine data processing and presentation tasks, ChatGPT can assist analysts or report writers.

3. Certain content generation and authoring tasks: ChatGPT can be used to generate simple documents, newsletters, summaries, etc. ChatGPT can provide a fast generation option when a large amount of standardized or templated content needs to be generated.

4. Some translation and language processing tasks: For some simple translation tasks or general language processing needs, ChatGPT can provide preliminary automatic translation and language processing functions. In a specific field or cultural background, ChatGPT may partially replace some translation work.

Programmers are the ones we focus on.

ChatGPT and similar natural language processing models excel at certain tasks, but current technology cannot fully replace the role of programmers. Although ChatGPT has demonstrated amazing capabilities in text generation and understanding, it still has some limitations:

1. Comprehension limitations: Although ChatGPT can generate plausible text responses, it does not really understand the meaning of the language. It only generates responses through pattern recognition and training data, but lacks deep understanding of context and semantics. This means that ChatGPT may struggle when dealing with complex problems, solving logical errors, or doing abstract reasoning.

2. Lack of judgment: ChatGPT does not have the ability to make autonomous decisions, it just responds based on pre-trained and fine-tuned data. It does not have the judgment and intuition to think independently and evaluate which solution is the best, which is one of the abilities that programmers need when solving complex problems.

3. Data dependence: ChatGPT’s performance depends on the training data it is exposed to. If the training data has bias, errors, or inaccurate information, it can carry or convey these issues. Human programmers can improve quality and accuracy by reviewing and correcting errors that ChatGPT cannot automatically fix.

4. Security and ethical considerations: Open-ended large-scale language models like ChatGPT may be at risk of misuse or misleading. Models may generate false information, misleading responses, or potentially inappropriate content. Therefore, special attention needs to be paid to security and ethical considerations when applying ChatGPT.

Nevertheless, the development of ChatGPT and natural language processing technology has some influence on programmers. They can assist programmers in certain repetitive and simple coding tasks, provide automated documentation and code generation, or become a tool for dialogue and interaction with users. However, the role of programmers is still critical because of their deep technical understanding, creative problem-solving skills, and integrated thinking about system design.

The upgrade and maintenance of ChatGPT is inseparable from the help of programmers. Programmers should be the last line of defense for artificial intelligence, but survival of the fittest, artificial intelligence will greatly reduce the market demand for programmers. At present, it seems that chatgpt cannot replace the positions of programmers. They can only assist programmers in their work, but they should also have a huge impact on the IT industry in the future.