Palace Jade Wine reduces the weight? OpenAI’s latest language model can work as customer service and write code, and even this article was written by it!

​Produced by Big Data Digest

Author: ChatGPT

OpenAI’s ChatGPT is really popular these days.

Let’s not look at how powerful this AI chatbot is in terms of technology. Let’s talk about its understanding. Is it clear to you?

In fact, everyone has come into contact with chatbots to some extent, such as e-commerce intelligent customer service, but these intelligent customer service The effect is obviously not as clear as ChatGPT, and it often makes you angry to death, and finally you switch to manual.

Not only is your thinking very clear, ChatGPT can even help you write code.

For example, if you want to make a lot of money in the stock market (being a leek), you can also ask ChatGPT to help you write an "effective stock market indicator framework".

Not only that, ChatGPT can also cause Digest to lose its job. If you don’t believe it, you will see it until the end.

ChatGPT’s past and present life

ChatGPT is actually the famous GPT-3, which is a language model developed by OpenAI. Its predecessor is GPT-2, which is one of the largest and most powerful language models in the field of machine learning.

The origins of ChatGPT can be traced back to 2017, when OpenAI released the first GPT model, a general language model that can predict the next word based on given text content , GPT-2 was released in 2019 and is larger, more accurate and more complex than the previous generation model.

##After GPT-2, OpenAI continued to advance the development of language models and released chatGPT (also called GPT-3). This is a larger, more accurate, and more complex model that achieves better performance in more language tasks.

ChatGPT’s technology is based on transfer learning and recurrent neural networks (RNN). It uses large amounts of pre-trained data to learn language structures and perform inference on new data. It also uses an attention mechanism to predict the next word based on context.

ChatGPT’s accuracy and versatility are outstanding in the field of machine learning. It can achieve good performance in many different language tasks, including chatbots, automatic text generation and speech recognition. It can also automatically generate high-quality text content and better predict the next word based on context. In short, ChatGPT is very powerful and can be used well in many different scenarios.

What is the technology behind ChatGPT?

ChatGPT’s technology is based on transfer learning and recurrent neural networks (RNN).

Transfer learning is a machine learning technique that allows a model to reason on new data sets without the need to retrain. Recurrent neural network (RNN) is a deep learning technology that can process sequence data and consider the historical information of the data.

Recurrent Neural Network (RNN)

Recurrent Neural Network (RNN) is a deep learning technology that Can handle sequence data and take into account historical information of the data. It processes sequence data through a loop structure and is able to remember contextual information to better predict the next value of the sequence.

The structure of RNN is as follows:

Input layer: receives input data.
• Hidden layer: Process input data and record contextual information.
• Output layer: predict the next value of the sequence.

##RNN is implemented through the following steps:

First, the input data Input to the input layer of the RNN.

• The neurons in the hidden layer then process the input data and convert it into output data through weights and biases.
• Neurons in the hidden layer record contextual information and use it to process the next input data.
• Through this loop process, RNN is able to consider the historical information of the sequence data and predict the next value of the sequence.

#RNN is very effective in processing sequence data. It can consider the historical information of the data and can remember contextual information. It has shown superior performance in many different applications, such as speech recognition, natural language processing, etc.

Transfer Learning

Transfer learning is a machine learning technique that allows a model to reason on new data sets without retraining. It enables fast learning and inference by applying pre-trained models to new tasks.

Transfer learning is achieved through the following steps:

First, for the original task, a model is trained through a large amount of data. This model can learn the characteristics and patterns of the original task through training data.

Then, apply this model to new tasks. In this new task, the pre-trained model can be used as the initial model and fine-tuned on the new dataset. This allows the model to quickly learn the characteristics and patterns of new tasks and perform inference on new data sets.

In the process of fine-tuning, the parameters of the model can be adjusted in different ways to better adapt to new data and tasks. For example, you can adjust the model's learning rate, weight initialization method, or use regularization techniques.

Transfer learning is very important in the field of machine learning because it can save a lot of time and calculations required to train the model resource. It enables fast learning and inference by leveraging pre-trained models to learn new tasks. In addition, transfer learning can also improve the accuracy and stability of the model and enable the model to be applied to more scenarios.

Will ChatGPT replace human programmers?

The advantage of ChatGPT is its ability to automatically generate natural language responses based on conversation context information without the need for human intervention. This allows it to serve human users more effectively in many conversational scenarios.

For example, it can serve as an online customer service robot that automatically responds to user queries, or as a virtual assistant that assists human users with daily tasks.

As for whether it will cause unemployment for human programmers, it is still unclear. Intelligent dialogue systems and human programmers cover different fields. The former mainly involves natural language processing and model training, while the latter involves more programming and software development. Therefore, ChatGPT may not pose an unemployment threat to human programmers.

In the future, some technologies may be developed to replace human programmers, but for now, ChatGPT cannot completely replace human programmers.

Final words

I don’t know if you have noticed... In fact, except for the beginning and the end, this article is almost entirely written by ChatGPT .

Yes, the editor just entered some tips, and ChatGPT completed the writing of all the content, just like the following:

So, I don’t know if programmers will be unemployed, but I may be unemployed...

Related reports:

https://chat.openai.com/chat

https://www.zhihu.com/question/570827092

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