In the previous article, we talked about the impact of ChatGPT on the gambling market and also briefly talked about the prediction of ChatGPT in the customer service system. Today we will continue to discuss the ideas of ChatGPT in customer service and the application scenarios of ChatGPT in communication and chat. At the same time, we will also evaluate the advantages and disadvantages of ChatGPT's language communication.

Application of ChatGPT in chat communication

History of Chatbots

One of the most important mathematicians and computer scientists of the 20th century is considered one of the founders of artificial intelligence (AI). Alan Turing, the "father of artificial intelligence and computer science" at that time, published an epoch-making paper in 1950 entitled "Computing Machinery and Intelligence" He proposed a very philosophical "imitation game," which is famous for the name "Turing test." This test means that you can accurately judge whether the other party is a human or a robot when you have a text chat with them when you are not talking with them face to face. If it is difficult to tell, then the machine can be said to have a certain degree of intelligence.

Advent of chatbots

This "Turing Test" is simple, understandable, specific, and has development value, so it has attracted a large number of computer scientists to attack it. In the beginning, it was just a very simple instruction. Through some language skills, try to make you feel that you are talking to a person. ELIZA was one of the first chatbots based on simple rules that could simulate rough human conversation. The appearance of ELIZA caused a sensation and became one of the iconic achievements of computer science at that time. Its developer is very smart and set ELIZA as a psychotherapist. Generally, counselors talk less and listen more. Therefore, when ELIZA asks the other party, for example: "What do you think?" the other party will say a lot.

Another example is when ELIZA asks the other party, "How are you feeling today?" the other party would also say a lot. This creates a situation where the less you say, the fewer you make mistakes. Therefore, some people mistakenly think that ELIZA is listening to you and communicating with you, while you think you are chatting with a human being, not a chatbot. Others are followed by some very simple syntax codes, such as "If," "but," "then," and so on. When the other party mentions the word "mother" to ELIZA, when keywords like this appear, ELIZA will say, "tell me about your family, " asking actively. This is because they extracted the compiled language through keywords and then asked each other.

Advancement of Chatbots

In 1995, a new descendant, A.L.I.C.E (Artificial Linguistic Internet Computer Entity), appeared in the robot chat. A.L.I.C.E uses natural language processing and machine learning technology to simulate human communication, making chatbots more intelligent. Although incomparable with the current ChatGPT, it can handle some daily conversations. But whether it is ELIZA or ALICE, they are essentially a technology called "pattern matching." When they hear a keyword, they will call up a preset plan. For example, if you ask it "Hello," it will answer you, "Have you eaten?" or something similar. Even now, some chatbots, such as shopping websites and banks, are still based on this model. For example, if you say "return," it will send you some information about the return process. If you ask the "ATM," it will send you some map information about the location of the ATM near you. Although this matching mode is not yet intelligent, it also saves a lot of manpower costs and repetitive answers.

But in this matching mode, no matter how many complex rules you write and how many presets you have, it is only possible to exhaust some of the answers and create new ones. Therefore, to pass the "Turing test," relying solely on this pattern matching is impossible. This leads to a new field in language learning - "machine learning."

The New Frontier of Chatbots - "Machine Learning"

The so-called machine learning is to let the machine learn - that is to say, it will not give you artificial rules and answers. It will just give you a bunch of ready-made examples. It lets computers learn from data to predict unknown outcomes. In this process, the machine learning algorithm will automatically learn the rules and patterns from the data and adjust its own parameters and weights accordingly to adapt to the input and output of new data. "Machine Learning” technologies and applications are wide-ranging, such as image recognition, self-driving cars, machine translation, voice assistants, and intelligent recommendation systems.

Based on this concept, by 2001, there was a once very popular chatbot called "SmarterChild." It uses some more advanced "machine learning" models to make chatting more natural. At the same time, in 2000, many chat software and platforms emerged. Then this "SmarterChild" collects these major platforms and a large amount of data, allowing people worldwide to have simple conversations with it. However, there is still a certain distance to pass the "Turing Test," although this "SmarterChild" has a basic and simple dialogue. You only need to say a few more words to determine this is a robot.

A New Field of "Machine Learning" - The Emergence of Artificial Neural Networks

In 2010, a field in machine learning began to shine - "Artificial Neural Networks" (ANNs). Our human brain is composed of more than 10 billion neurons, and this "artificial neural network" is to imitate the human brain. These nodes are connected in a way that simulates the interaction between neurons in the human brain. Artificial neural networks have achieved remarkable results in many fields.

"Artificial neural network" has been around for a long time, but the requirements of hardware and data limited it. This was only in the era of the Internet in 2010 that data and computing power continued to increase. "Artificial neural network" has only begun to shine and generate many applications. It is usually used to process a large amount of complex data, such as face recognition, speech recognition, natural language processing, speech synthesis, etc.

Artificial Neural Networks - Rookie "Recurrent Neural Networks"

However, the "artificial neural network" did not go well when it came back to the text domain. The main reason is that machine learning usually uses a method called "recurrent neural network." However, "recurrent neural networks" can only process words one by one and cannot learn a lot simultaneously. Nonetheless, "recurrent neural networks" have also been used in speech recognition, machine translation, text generation, and more.

Emergence of a new mechanism in the field of machine learning text - "Self-attention Mechanism"

In 2017, Google published a paper proposing a new machine-learning framework called "self-attention mechanism" or transformers. The "T" in Google's BERT and ChatGPT both refer to "Transformers." It is a deep learning model. The result of the self-attention mechanism is that the machine can learn a large amount of text at the same time. The original "recurrent neural network" can only learn individual words. Still, after the emergence of the "self-attention mechanism," word learning can be carried out simultaneously, greatly improving training speed and efficiency. With the "self-attention mechanism," machine learning is like opening up in the text field. Many natural language processing models are now based on the "self-attention mechanism."

Birth of OpenAI GPT

In 2015, several bigwigs, including Musk and Peter Titty, jointly invested USD 1 billion to establish the non-profit organization OpenAI, which aims to research artificial intelligence. OpenAI is also the parent company of ChatGPT. Later, in 2018, Musk found that his company also needed to invest heavily in this area, such as "autonomous driving," so he withdrew from OpenAI. The reason is that OpenAI is a non-profit organization, and its research results are public. But other OpenAI bigwigs reacted very quickly. In 2017, Google proposed a new machine learning framework, and OpenAI immediately conducted research and learned on this basis.

In 2018, OpenAI published a paper introducing a new language learning model - GPT (Generative Pre-trained Transformer), a pre-trained language model based on the Transformer model. No specific tasks are required during training, and training is performed unsupervised, unlike previous machine learning that requires artificial preset labels or instructions and supervision. Language models can be pre-trained if there is a large amount of text data. The pre-trained GPT model can be used for various NLP tasks, such as text classification, named entity recognition, question-answering system, etc.

Update iteration of OpenAI GPT

In June 2018, the GPT-1 generation was launched, with approximately 120 million parameters. In 2019, the amount of training data was increased, and the GPT-2 generation was launched, with about 1.5 billion parameters. In 2020, the latest version of the pre-trained language model, the GPT-3 generation, was launched. The number of GPT-3 generation parameters reached 175 billion, 13 times that of the GPT-2 model. Some people may not understand what a model is and what a parameter is. The model determines how the machine learns, which can be a way of learning. The learning efficiency and effect of different models will be very different, just like several students spend the same time on the same course. Some learn fast, while others learn slowly. The parameter quantity is relatively simple. The popular point is a calculation, which tests computing power. To put it bluntly, it is to invest money.

The OpenAI team has great confidence and hope in the GPT model, but every little improvement in GPT may require a larger order of magnitude of data to support it. All of these require computing power and a large amount of capital investment. Under the pressure of funds, OpenAI has become a for-profit organization. At the same time, Microsoft also joined the field, investing USD 1 billion. This is the time when OpenAI, the GPT model, combines two swords. OpenAI has this GPT model, and Microsoft has given it the fifth supercomputer in the world. This greatly improves the computing power and efficiency of GPT. Microsoft also got the OpenAI team, so it is estimated that future OpenAI research will not be made public.

Birth of ChatGPT

With the addition of the artificial feedback mechanism, the effect and efficiency of GPT training have improved significantly. In March 2022, GPT-3.5 was launched to evolve the dialogue. Then ChatGPT will be officially launched in November 2022. The launch of ChatGPT has caused a great stir. From stocks, finance, technology stocks, and investment, they are all moving towards AI intelligence.

Advantages of ChatGPT

Now, let's talk about the advantages and disadvantages of ChatGPT.

Human language

ChatGPT can respond to human language very naturally and fluently, making people feel like they are having a conversation with a real person, which improves the realism of the conversation.

Flexible

ChatGPT model can be flexibly set and applied according to different application scenarios and requirements and is very suitable for multiple fields and industries.ChatGPT model can be flexibly set and applied according to different application scenarios and requirements and is very suitable for multiple fields and industries.

Self-learning

ChatGPT can learn by itself by continuously analyzing historical dialogue and text data, improving its language processing ability, and providing more personalized services for humans.

Automation and intelligence features

The automation and intelligence features of ChatGPT can help enterprises improve efficiency and reduce the cost and workload of manual intervention. For example, it can be used in automated customer service, smart assistants, and other fields to improve the service level and efficiency of enterprises.

Disadvantages of ChatGPT

Inaccurate

ChatGPT may have inaccurate or confusing answers when dealing with some complex questions, which require constant optimization and adjustment.

Low protection level

ChatGPT needs stricter protection and control when dealing with sensitive information and privacy issues to avoid information leakage and abuse.

Uncertainty and risks

The self-learning and intelligent features of ChatGPT will also bring certain uncertainties and risks, which require more detailed monitoring and management.

Need more knowledge

ChatGPT may require more specialized and in-depth knowledge and skills when dealing with problems in some specific fields and requires continuous learning and improvement.

ChatGPT in the gambling market

The application of ChatGPT in the gambling market is getting more and more attention. In fact, ChatGPT application in the field of gambling can be expanded to many different aspects. For example, ChatGPT can simulate the interaction between neurons in the human brain and can respond to human language very naturally and smoothly, improving the realism of the dialogue.

At the same time, ChatGPT can be flexibly set and applied according to different application scenarios and needs, which is very suitable for the gaming industry. In terms of gambling private domain conversion, group speculation, search engine optimization (SEO) writing, customer service work, emotional communication, and role-playing. In the next article, the editor of Tianchengbao.com will conduct an in-depth study with you on how ChatGPT cooperates with the promotion and customer service of the gaming industry.

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