- March 09, 2023
Chat GPT Application In The Gambling Market
(Chat GPT's Past and Present)
Before talking about the application of Chat GPT in the gambling market, we need to first understand the history of chatbots. In this way, everyone can better understand what a chatbot is and what current intelligent chatbots are. This makes it easier to understand the application of Chat GPT in chat communication.
The history of chatbots
Chatbot concept
Alan Turing is one of the most important mathematicians and computer scientists of the 20th century. He is also considered one of the founders of artificial intelligence (AI). Alan Turing, also known as "Father of Computer Science" at that time, published an epoch-making paper in 1950 titled "Computing Machinery" and Intelligence. He proposed a very philosophical "imitation game", which is our famous "Turing test". The meaning of this test is that when you have a text chat with the other party when you are not face to face, whether you can accurately determine whether the other party is a human or a robot. If it's hard to tell, then the machine can be said to have a certain level of intelligence.
The emergence of chatbots
This "Turing test" is simple, easy to understand, specific, and has development value, so it has attracted a large number of computer scientists to attack it. At the beginning, it was just a very simple command. Through some language skills, it tried to make you feel that you were talking to a person. ELIZA is one of the first chatbots based on simple rules that can simulate rough human conversations. The emergence of ELIZA caused a sensation and became one of the iconic achievements of computer science at that time. Its developers are very smart and set ELIZA as a psychotherapist. Generally, psychological 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. For another example, when ELIZA asks the other party "How are you feeling today?", the other party will also say a lot. This creates a situation where the less you say, the less you will make mistakes. Therefore, some people mistakenly think that ELIZA is listening to you and then communicating with you, and at the same time, you think that you are chatting with a person, not a chatbot. Others are followed by some very simple syntax codes, such as "IF", "but", "then" and so on. For example, when the other party mentions the word "mom" to ELIZA, and similar keywords appear, ELIZA will say "tell me about your family", which is to take the initiative to ask. They extracted the compiled language through keywords, and then asked the other party.
Chatbot advancements
By 1995, a new descendant appeared in robot chat, A.L.I.C.E (Artificial Linguistic Internet Computer Entity). A.L.I.C.E uses natural language processing and machine learning technology to simulate human communication more naturally, making chatbots more intelligent. Although it is not comparable to the current Chat GPT, 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 with "Have you eaten?" Even now, some chatbots on shopping websites, banks, etc. are still based on this model. For example, if you ask for a return, it will send you some information about the return process, or if you ask an ATM, it will send you some location map information about ATMs near you. Although this matching mode is not yet intelligent, it also saves a lot of labor costs and repetitive answers.
But with this matching model, no matter how many, complex, or preset rules you write, it is impossible to exhaust all the answers, let alone create new answers. Therefore, it is impossible to pass the "Turing test" by relying solely on this pattern matching. 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, I will not give you artificially prescribed rules and answers. Just give you a lot of ready-made examples. It lets computers learn from data to predict unknown outcomes. In this process, the machine learning algorithm will automatically learn 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 has a wide range of technologies and applications, 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 advanced "machine learning" models to make chatting more natural. At the same time, in 2000, a lot of chat software and platforms also emerged. Then this "SmarterChild" collects these major platforms and collects a large amount of data, which allows people all over the world to have simple conversations with it. However, although this "SmarterChild" has a basic and simple dialogue, there is still a certain distance to pass the "Turing test". You only have to say a few more words to realize that this is a robot.
The new field of "machine learning" - the emergence of artificial neural networks
In 2010, a field in machine learning began to shine, called "Artificial Neural Network" (ANN). Our human brain is composed of more than 10 billion neurons, and this "artificial neural network" is to imitate The human brain is composed of a large number of interconnected nodes (neurons). These nodes are connected to simulate the interactions between neurons in the human brain. Artificial neural networks have achieved remarkable results in many fields. "Artificial neural networks" have been around for a long time, but they were limited by hardware and data requirements. It was not until the Internet era emerged in 2010 that data became available and computing power continued to improve. "Artificial neural networks" have just begun to shine and have a large number of applications. Usually used to process large amounts of complex data, such as face recognition, speech recognition, natural language processing, speech synthesis, etc.Artificial Neural Network - Rookie "Recurrent Neural Network"
"Artificial neural networks" did not fare well when returning to text. 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 large number of words at the same time. Nevertheless, "recurrent neural networks" are also used in speech recognition, machine translation, text generation and other fields.The emergence of a new mechanism in the field of machine learning text - "self-attention mechanism"
Until 2017, Google published a paper proposing a new machine learning framework called "Self-Attention Mechanism" (Transformers). This 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 single words individually, but after the emergence of the "self-attention mechanism", text learning can be carried out at the same time, so that the speed and efficiency of training are greatly improved. With the "self-attention mechanism", machine learning is like cheating in the text field. Many natural language processing models are now based on the "self-attention mechanism". The "T" in Google's BERT and ChatGPT both refer to Transformers "self-attention mechanism."
The birth of OpenAI GPT
In 2015, several big names including Musk and Peterti invested US$1 billion to establish OpenAI, a non-profit organization aimed at conducting research on artificial intelligence. OpenAI is also the parent company of Chat GPT. 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 main reason is that OpenAI is a non-profit organization and its research results are public. But other OpenAI big guys responded very quickly. In 2017, Google proposed a new machine learning framework (Transformers), and OpenAI immediately conducted research and learning on this basis.
In 2018, OpenAI published a paper and launched a new language learning model - GPT (Generative Pre-trained Transformer), which is a pre-trained language model based on the Transformer model. There is no need for specific tasks during training, and training is conducted in an unsupervised manner. Unlike previous machine learning, which required artificially preset labels or instructions and supervision. As long as there is a large amount of text data, the language model can be pre-trained. The pre-trained GPT model can be used for a variety of NLP tasks, such as text classification, named entity recognition, question and answer systems, etc.
Updates and iterations of OpenAI GPT
The GPT-1 generation was launched in June 2018, with approximately 120 million parameters. In 2019, the amount of training data was increased and the GPT-2 generation was launched, with approximately 1.5 billion parameters. In 2020, the latest version of the pre-trained language model, GPT-3 generation, was launched. The number of parameters of the GPT-3 generation reached 175 billion, which is 13 times that of the GPT-2 model. Some people may not quite understand what a model is and what parameters are. The model determines how the machine learns, which can be a learning method. The learning efficiency and effect of different models will be very different. Just like several students taking the same course and spending the same time, some will learn quickly and some will learn slowly. The parameter quantity is relatively simple. In layman's terms, it means calculation and testing of computing power. To put it bluntly, it means throwing money at money.
The OpenAI team has great confidence and hope in the GPT model, but every time GPT improves a little bit, it may require a larger order of magnitude data to support it. All of these require computing power and a large amount of capital investment. Due to financial pressure, OpenAI became a for-profit organization. At the same time, Microsoft also joined the field, investing US$1 billion. This is when the two swords of the OpenAI GPT model come together. OpenAI has this GPT model, and Microsoft gives it the fifth supercomputer in the world. This greatly improves the computing power and efficiency of GPT. Microsoft has also acquired the OpenAI team, so it is estimated that future OpenAI research will no longer be made public.
GPT-3-Addition of manual feedback mechanism
Since OpenAI received the computing power provided by Microsoft, the results have been very good, and it has become the prototype of Chat GPT, which has caused a great sensation in the industry. But this is GPT-3 trained purely by machine. There is a bit of a problem, that is, sometimes the answers are good, sometimes the performance is not good, and generally the performance is unstable. No matter how you increase the amount of data parameters, the improvement of GPT-3 is very limited. This is mainly because it does not have a feedback mechanism during machine training. No one tells it whether the answer is wrong or right. There is no way of knowing whether this answer is good or bad for you. Therefore, OpenAI added an artificial feedback mechanism during GPT-3 training, which is "artificial feedback reinforcement learning". So when you chat with Chat GPT, you will feel that sometimes the answers he gives you are very detailed, and sometimes they are very short and rough.
Chat GPT is officially born
With the addition of the manual feedback mechanism, the effect and efficiency of GPT training have been significantly improved. GPT-3.5 was launched in March 2022 to evolve the dialogue. Then Chat GPT was officially launched in November 2022. The launch of Chat GPT caused a lot of excitement. Stocks, finance, technology and other stocks or investments are all moving towards AI intelligence. Let’s first talk about the advantages and disadvantages of Chat GPT at this stage.
Disadvantages of Chat GPT
- Chat GPT may have inaccurate or confusing answers when dealing with some complex questions, and requires constant optimization and adjustment.
- Chat GPT requires more stringent protection and control when dealing with sensitive information and privacy issues to avoid information leakage and abuse.
- The self-learning and intelligent features of Chat GPT will also bring certain uncertainties and risks, requiring more detailed monitoring and management.
- When Chat GPT deals with problems in some specific fields, it may require more specialized and in-depth knowledge and skills, and requires continuous learning and improvement.
Summary
The application of Chat GPT in the gambling market is attracting more and more attention. In fact, the application of Chat GPT in the gambling field can be expanded to many different aspects. Chat GPT can simulate the interaction between neurons in the human brain, and can reply to human language very naturally and smoothly, improving the realism of the conversation. At the same time, Chat GPT can be flexibly set up and applied according to different application scenarios and needs, making it very suitable for the iGaming industry. For example, in the fields of gambling private domain conversion, group speculation, SEO writing, customer service work, emotional communication and role playing (such as baby mothers, single women, TI men, etc.). In the next article, the editor of TC-GAMING will work with you to conduct an in-depth study of how Chat GPT cooperates with the promotion and customer service of the iGaming industry. TC-GAMING has been in the industry for 16 years. It is a well-established white label iGaming company with more than a thousand iGaming platform service experience and has gained the trust of many iGaming industry bosses. TC-GAMING collects and constantly pays attention to industry trends to assist Bao.com customers in providing comprehensive white label iGaming solutions, allowing you to devote more energy to marketing and the conversion of gambling players. Choosing TC-GAMING is your best choice when choosing a white label iGaming company.
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