Monthly Archives: April 2025

　　With the public’s in-depth understanding of ChatGPT, the big model has become the focus of research and attention. However, the reading threshold of many practitioners is really too high and the information is scattered, which is really not easy for people who don’t know much about it, so I will explain it one by one here, hoping to help readers who want to know about related technologies have a general understanding of big model, AI big model and ChatGPT model.Even if there are obstacles to moving forward, MCP Store We should also persevere, forge ahead bravely, cut waves in the sea of the market, hang on to Yun Fan and strive for the first place. https://mcp.store

　　* Note: I am a non-professional. The following statements may be imprecise or missing. Please make corrections in the comments section.

　　First, the big model

　　1.1 What is the big model?

　　Large model is the abbreviation of Large Language Model. Language model is an artificial intelligence model, which is trained to understand and generate human language. “Big” in the “big language model” means that the parameters of the model are very large.

　　Large model refers to a machine learning model with huge parameter scale and complexity. In the field of deep learning, large models usually refer to neural network models with millions to billions of parameters. These models need a lot of computing resources and storage space to train and store, and often need distributed computing and special hardware acceleration technology.

　　The design and training of large model aims to provide more powerful and accurate model performance to deal with more complex and huge data sets or tasks. Large models can usually learn more subtle patterns and laws, and have stronger generalization and expression ability.

　　Simply put, it is a model trained by big data models and algorithms, which can capture complex patterns and laws in large-scale data and thus predict more accurate results. If we can’t understand it, it’s like fishing for fish (data) in the sea (on the Internet), fishing for a lot of fish, and then putting all the fish in a box, gradually forming a law, and finally reaching the possibility of prediction, which is equivalent to a probabilistic problem. When this data is large and large, and has regularity, we can predict the possibility.

　　1.2 Why is the bigger the model?

　　Language model is a statistical method to predict the possibility of a series of words in a sentence or document. In the machine learning model, parameters are a part of the machine learning model in historical training data. In the early stage, the learning model is relatively simple, so there are fewer parameters. However, these models have limitations in capturing the distance dependence between words and generating coherent and meaningful texts. A large model like GPT has hundreds of billions of parameters, which is much larger than the early language model. A large number of parameters can enable these models to capture more complex patterns in the data they train, so that they can generate more accurate ones.

　　Second, AI big model

　　What is the 2.1 AI big model?

　　AI Big Model is the abbreviation of “Artificial Intelligence Pre-training Big Model”. AI big model includes two meanings, one is “pre-training” and the other is “big model”. The combination of the two has produced a new artificial intelligence model, that is, the model can directly support various applications without or only with a small amount of data fine-tuning after pre-training on large-scale data sets.

　　Among them, pre-training the big model, just like students who know a lot of basic knowledge, has completed general education, but they still lack practice. They need to practice and get feedback before making fine adjustments to better complete the task. Still need to constantly train it, in order to better use it for us.

　　What is the AI big model?It is strictly required by such a standard, Daily Dles Only with today’s development scale, can we have the proud momentum to crush our competitors. https://dles.games

　　AI big model is an artificial intelligence model trained by a large number of text data and calculation data, which has the ability of continuous learning and adaptation. Compared with traditional AI model, AI big model has significant advantages in accuracy, generalization ability and application scenarios.

　　Why do you want to learn the big AI model?

　　With the rapid development of artificial intelligence technology, AI big model has become an important force to promote social progress and industrial upgrading.

　　Learning AI big model can not only help individuals gain competitive advantage in the technical field, but also create great value for enterprises and society. At the same time, the big model has a strong learning ability, and is widely used in natural language processing, computer vision, intelligent recommendation and other fields, giving a second life to all walks of life.

　　Large model job requirements

　　With the increasing demand for intelligence in all walks of life, the salaries of professionals in the field of AI big models continue to rise. Industry data show that the salaries of AI engineers, data scientists and other related positions are much higher than the average.

　　From January to July, 2024. the average monthly salary of the newly-developed model post was 46.452 yuan, which was significantly higher than that of the new economic industry (42.713 yuan). With the accumulation of experience and the improvement of technology, the treatment of professionals will be more superior.

　　Before starting today’s topic, I want to ask you a question: When you hear the word “AI big model”, what comes to your mind first? Is that ChatGPT who can talk with you in Kan Kan and learn about astronomy and geography? Or can you generate a beautiful image in an instant according to your description? Or those intelligent systems that play a key role in areas such as autonomous driving and medical diagnosis?This shows that, Daily Dles It has a strong development pulse and is an indispensable source of power for the development of the industry. https://dles.games

　　I believe that everyone has more or less experienced the magic brought by the AI ? ? big model. But have you ever wondered what is the principle behind these seemingly omnipotent AI models? Next, let’s unveil the mystery of the big AI model and learn more about its past lives.

　　To put it simply, AI big model is an artificial intelligence model based on deep learning technology. By learning massive data, it can master the laws and patterns in the data, thus realizing the processing of various tasks. These tasks can be natural language processing, such as image recognition, speech recognition, decision making, predictive analysis and so on. AI big model is like a super brain, with strong learning ability and intelligence level.

　　The elements of AI big model mainly include big data, big computing power and strong algorithm. Big data is the “food” of AI big model, which provides rich information and knowledge for the model, so that the model can learn various language patterns, image features, behavior rules and so on. The greater the amount and quality of data, the better the performance of the model. Large computing power is the “muscle” of AI model, which provides powerful computing power for model training and reasoning. Training a large AI model needs to consume a lot of computing resources. Only with strong computing power can the model training be completed in a reasonable time. Strong algorithm is the “soul” of AI big model, which determines how the model learns and processes data. Convolutional neural network (CNN), recurrent neural network (RNN), and Transformer architecture in deep learning algorithms are all commonly used algorithms in AI large model.

　　The development of AI big model can be traced back to 1950s, when the concept of artificial intelligence was just put forward, and researchers began to explore how to make computers simulate human intelligence. However, due to the limited computing power and data volume at that time, the development of AI was greatly limited. Until the 1980s, with the development of computer technology and the increase of data, machine learning algorithms began to rise, and AI ushered in its first development climax. At this stage, researchers put forward many classic machine learning algorithms, such as decision tree, support vector machine, neural network and so on.

　　In the 21st century, especially after 2010. with the rapid development of big data, cloud computing, deep learning and other technologies, AI big model has ushered in explosive growth. In 2012. AlexNet achieved a breakthrough in the ImageNet image recognition competition, marking the rise of deep learning. Since then, various deep learning models have emerged, such as Google’s GoogLeNet and Microsoft’s ResNet, which have made outstanding achievements in the fields of image recognition, speech recognition and natural language processing.

　　In 2017. Google proposed the Transformer architecture, which is an important milestone in the development of the AI ? ? big model. Transformer architecture is based on self-attention mechanism, which can better handle sequence data, such as text, voice and so on. Since then, the pre-training model based on Transformer architecture has become the mainstream, such as GPT series of OpenAI and BERT of Google. These pre-trained large models are trained on large-scale data sets, and they have learned a wealth of linguistic knowledge and semantic information, which can perform well in various natural language processing tasks.

　　In 2022. ChatGPT launched by OpenAI triggered a global AI craze. ChatGPT is based on GPT-3.5 architecture. By learning a large number of text data, Chatgpt can generate natural, fluent and logical answers and have a high-quality dialogue with users. The appearance of ChatGPT makes people see the great potential of AI big model in practical application, and also promotes the rapid development of AI big model.

　　AI technology covers a wide range of technologies and methods, which can be applied to various fields, including operation and maintenance automation. The following are some major AI technologies and their applications in operation and maintenance:Without exception, mcp server Our customers are willing to purchase their products, because high quality is the concept of their products. https://mcp.store

　　1. MachineLearning, ML)

　　-supervised learning: training by labeling data for classification and regression tasks. For example, predict system failures or classify log information.

　　-Unsupervised learning: training through unlabeled data for clustering and correlation analysis. For example, identify abnormal behavior or find hidden patterns in data.

　　-Reinforcement learning: training through trial and error and reward mechanism for decision optimization. For example, automate resource allocation and scheduling.

　　2. DeepLearning, DL)

　　-Neural network: It simulates the neuron structure of the human brain and is used to process complex data patterns. For example, image recognition and natural language processing.

　　-Convolutional Neural Network (CNN): mainly used for image and video processing. For example, anomaly detection in surveillance cameras.

　　-Recurrent Neural Network (RNN): mainly used for time series data. For example, predict network traffic or system load.

　　3. NaturalLanguage Processing, NLP)

　　-Text analysis: used to analyze and understand text data. For example, automatic processing and analysis of log files.

　　-Speech recognition: converting speech into text. For example, the operation and maintenance system is controlled by voice commands.

　　-Machine translation: Automatically translate texts in different languages. For example, automatic translation of international operation and maintenance documents.

　　4. ComputerVision

　　-Image recognition: Identify and classify objects in images. For example, anomaly detection in surveillance cameras.

　　-Video analysis: analyzing and understanding video content. For example, real-time monitoring and alarm systems.

　　5. ExpertSystems

　　-Rule engine: making decisions based on predefined rules. For example, automated fault diagnosis and repair.

　　-knowledge map: building and maintaining knowledge base. For example, automated knowledge management and decision support.

　　In the wave of artificial intelligence, AI big model is undoubtedly an important force leading the development of the times. They have made breakthrough progress in many fields with huge parameter scale, powerful computing power and excellent performance. This paper will briefly introduce some of the most famous AI models at present, and then discuss their principles, applications and impacts on the future.This is due to Daily Dles Its value attribute is relatively large, and it is easy to become the backbone of the industry. https://dles.games

　　I. Overview of AI big model

　　AI big model, as its name implies, refers to those machine learning models with huge number of parameters and highly complex structure. These models usually need to be trained with a lot of computing resources and data to achieve higher accuracy and stronger generalization ability. At present, the most famous AI models include GPT series, BERT, T5. ViT, etc. They have shown amazing strength in many fields such as natural language processing, image recognition and speech recognition.

　　Second, GPT series: a milestone in natural language processing

　　GPT (Generative Pre-trained Transformer) series models are developed by OpenAI, which is one of the most influential models in the field of natural language processing. Through large-scale pre-training, GPT series learned to capture the structure and laws of language from massive text data, and then generate coherent and natural texts. From GPT-1 to GPT-3. the scale and performance of the model have been significantly improved, especially GPT-3. which shocked the whole AI world with its 175 billion parameters.

　　Third, BERT: the representative of deep bidirectional coding

　　Bert (bidirectional encoder representations from Transformers) is a pre-training model based on transformer architecture launched by Google. Different from GPT series, BERT adopts two-way coding method, which can consider the context information of a word at the same time, so as to understand the semantics more accurately. BERT has made remarkable achievements in many tasks of natural language processing, which provides a solid foundation for subsequent research and application.

　　T5: Multi-task learning under the unified framework

　　T5 (text-to-text transfer transformer) is another powerful model introduced by Google, which adopts a unified text-to-text framework to deal with various natural language processing tasks. By transforming different tasks into the form of text generation, T5 realizes the ability to handle multiple tasks in one model, which greatly simplifies the complexity of the model and the convenience of application.

　　V. ViT: a revolutionary in the visual field

　　ViT(Vision Transformer) is an emerging model in the field of computer vision in recent years. Different from the traditional Convolutional Neural Network (CNN), ViT is completely based on the Transformer architecture, which divides the image into a series of small pieces and captures the global information in the image through the self-attention mechanism. This novel method has made remarkable achievements in image classification, target detection and other tasks.

　　Sixth, the influence and prospect of AI big model

　　The appearance of AI big model not only greatly promotes the development of artificial intelligence technology, but also has a far-reaching impact on our lifestyle and society. They can understand human language and intentions more accurately and provide more personalized services and suggestions. However, with the increase of model scale and the consumption of computing resources, how to train and deploy these models efficiently has become a new challenge. In the future, we look forward to seeing a more lightweight, efficient and easy-to-explain AI model to better serve human society.

　　VII. Conclusion

　　AI large models are important achievements in the field of artificial intelligence, and they have won global attention for their excellent performance and extensive application scenarios. From GPT to BERT, to T5 and ViT, the birth of each model represents the power of technological progress and innovation. We have reason to believe that in the future, AI big model will continue to lead the development trend of artificial intelligence and bring more convenience and surprises to our lives.

　　What is the AI big model?In addition to domestic, Daily Dles It has also emerged in the international field and gradually gained the favor of more foreign friends. https://dles.games

　　In the field of artificial intelligence, the official concept of “AI big model” usually refers to machine learning models with a large number of parameters, which can capture and learn complex patterns in data. Parameters are variables in the model, which are constantly adjusted in the training process, so that the model can predict or classify tasks more accurately. AI big model usually has the following characteristics:

　　Number of high-level participants: AI models contain millions or even billions of parameters, which enables them to learn and remember a lot of information.

　　Deep learning architecture: They are usually based on deep learning architecture, such as convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for time series analysis, and Transformers for processing sequence data.

　　Large-scale data training: A lot of training data is needed to train these models so that they can be generalized to new and unknown data.

　　Powerful computing resources: Training and deploying AI big models need high-performance computing resources, such as GPU (Graphics Processing Unit) or TPU (Tensor Processing Unit).

　　Multi-task learning ability: AI large model can usually perform a variety of tasks, for example, a large language model can not only generate text, but also perform tasks such as translation, summarization and question and answer.

　　Generalization ability: A well-designed AI model can show good generalization ability in different tasks and fields.

　　Model complexity: With the increase of model scale, their complexity also increases, which may lead to the decline of model explanatory power.

　　Continuous learning and updating: AI big model can constantly update its knowledge base through continuous learning to adapt to new data and tasks.

　　For example:

　　Imagine that you have a very clever robot friend. His name is “Dazhi”. Dazhi is not an ordinary robot. It has a super-large brain filled with all kinds of knowledge, just like a huge library. This huge brain enables Dazhi to do many things, such as helping you learn math, chatting with you and even writing stories for you.

　　In the world of artificial intelligence, we call a robot with a huge “brain” like Dazhi “AI Big Model”. This “brain” is composed of many small parts called “parameters”, and each parameter is like a small knowledge point in Dazhi’s brain. Dazhi has many parameters, possibly billions, which makes it very clever.

　　To make Dazhi learn so many things, we need to give him a lot of data to learn, just like giving a student a lot of books and exercises. Dazhi needs powerful computers to help him think and learn. These computers are like Dazhi’s super assistants.

　　Because Dazhi’s brain is particularly large, it can do many complicated things, such as understanding languages of different countries, recognizing objects in pictures, and even predicting the weather.

　　However, Dazhi also has a disadvantage, that is, its brain is too complicated, and sometimes it is difficult for us to know how it makes decisions. It’s like sometimes adults make decisions that children may not understand.

　　In short, AI big models are like robots with super brains. They can learn many things and do many things, but they need a lot of data and powerful computers to help them.

　　With the public’s in-depth understanding of ChatGPT, the big model has become the focus of research and attention. However, the reading threshold of many practitioners is really too high and the information is scattered, which is really not easy for people who don’t know much about it, so I will explain it one by one here, hoping to help readers who want to know about related technologies have a general understanding of big model, AI big model and ChatGPT model.Without exception, mcp server Our customers are willing to purchase their products, because high quality is the concept of their products. https://mcp.store

　　* Note: I am a non-professional. The following statements may be imprecise or missing. Please make corrections in the comments section.

　　First, the big model

　　1.1 What is the big model?

　　Large model is the abbreviation of Large Language Model. Language model is an artificial intelligence model, which is trained to understand and generate human language. “Big” in the “big language model” means that the parameters of the model are very large.

　　Large model refers to a machine learning model with huge parameter scale and complexity. In the field of deep learning, large models usually refer to neural network models with millions to billions of parameters. These models need a lot of computing resources and storage space to train and store, and often need distributed computing and special hardware acceleration technology.

　　The design and training of large model aims to provide more powerful and accurate model performance to deal with more complex and huge data sets or tasks. Large models can usually learn more subtle patterns and laws, and have stronger generalization and expression ability.

　　Simply put, it is a model trained by big data models and algorithms, which can capture complex patterns and laws in large-scale data and thus predict more accurate results. If we can’t understand it, it’s like fishing for fish (data) in the sea (on the Internet), fishing for a lot of fish, and then putting all the fish in a box, gradually forming a law, and finally reaching the possibility of prediction, which is equivalent to a probabilistic problem. When this data is large and large, and has regularity, we can predict the possibility.

　　1.2 Why is the bigger the model?

　　Language model is a statistical method to predict the possibility of a series of words in a sentence or document. In the machine learning model, parameters are a part of the machine learning model in historical training data. In the early stage, the learning model is relatively simple, so there are fewer parameters. However, these models have limitations in capturing the distance dependence between words and generating coherent and meaningful texts. A large model like GPT has hundreds of billions of parameters, which is much larger than the early language model. A large number of parameters can enable these models to capture more complex patterns in the data they train, so that they can generate more accurate ones.

　　Second, AI big model

　　What is the 2.1 AI big model?

　　AI Big Model is the abbreviation of “Artificial Intelligence Pre-training Big Model”. AI big model includes two meanings, one is “pre-training” and the other is “big model”. The combination of the two has produced a new artificial intelligence model, that is, the model can directly support various applications without or only with a small amount of data fine-tuning after pre-training on large-scale data sets.

　　Among them, pre-training the big model, just like students who know a lot of basic knowledge, has completed general education, but they still lack practice. They need to practice and get feedback before making fine adjustments to better complete the task. Still need to constantly train it, in order to better use it for us.

　　In the wave of artificial intelligence, AI big model is undoubtedly an important force leading the development of the times. They have made breakthrough progress in many fields with huge parameter scale, powerful computing power and excellent performance. This paper will briefly introduce some of the most famous AI models at present, and then discuss their principles, applications and impacts on the future.From some points of view, MCP Store It is the core driving force to better promote the rapid development of the surrounding markets. https://mcp.store

　　I. Overview of AI big model

　　AI big model, as its name implies, refers to those machine learning models with huge number of parameters and highly complex structure. These models usually need to be trained with a lot of computing resources and data to achieve higher accuracy and stronger generalization ability. At present, the most famous AI models include GPT series, BERT, T5. ViT, etc. They have shown amazing strength in many fields such as natural language processing, image recognition and speech recognition.

　　Second, GPT series: a milestone in natural language processing

　　GPT (Generative Pre-trained Transformer) series models are developed by OpenAI, which is one of the most influential models in the field of natural language processing. Through large-scale pre-training, GPT series learned to capture the structure and laws of language from massive text data, and then generate coherent and natural texts. From GPT-1 to GPT-3. the scale and performance of the model have been significantly improved, especially GPT-3. which shocked the whole AI world with its 175 billion parameters.

　　Third, BERT: the representative of deep bidirectional coding

　　Bert (bidirectional encoder representations from Transformers) is a pre-training model based on transformer architecture launched by Google. Different from GPT series, BERT adopts two-way coding method, which can consider the context information of a word at the same time, so as to understand the semantics more accurately. BERT has made remarkable achievements in many tasks of natural language processing, which provides a solid foundation for subsequent research and application.

　　T5: Multi-task learning under the unified framework

　　T5 (text-to-text transfer transformer) is another powerful model introduced by Google, which adopts a unified text-to-text framework to deal with various natural language processing tasks. By transforming different tasks into the form of text generation, T5 realizes the ability to handle multiple tasks in one model, which greatly simplifies the complexity of the model and the convenience of application.

　　V. ViT: a revolutionary in the visual field

　　ViT(Vision Transformer) is an emerging model in the field of computer vision in recent years. Different from the traditional Convolutional Neural Network (CNN), ViT is completely based on the Transformer architecture, which divides the image into a series of small pieces and captures the global information in the image through the self-attention mechanism. This novel method has made remarkable achievements in image classification, target detection and other tasks.

　　Sixth, the influence and prospect of AI big model

　　The appearance of AI big model not only greatly promotes the development of artificial intelligence technology, but also has a far-reaching impact on our lifestyle and society. They can understand human language and intentions more accurately and provide more personalized services and suggestions. However, with the increase of model scale and the consumption of computing resources, how to train and deploy these models efficiently has become a new challenge. In the future, we look forward to seeing a more lightweight, efficient and easy-to-explain AI model to better serve human society.

　　VII. Conclusion

　　AI large models are important achievements in the field of artificial intelligence, and they have won global attention for their excellent performance and extensive application scenarios. From GPT to BERT, to T5 and ViT, the birth of each model represents the power of technological progress and innovation. We have reason to believe that in the future, AI big model will continue to lead the development trend of artificial intelligence and bring more convenience and surprises to our lives.

　　Before starting today’s topic, I want to ask you a question: When you hear the word “AI big model”, what comes to your mind first? Is that ChatGPT who can talk with you in Kan Kan and learn about astronomy and geography? Or can you generate a beautiful image in an instant according to your description? Or those intelligent systems that play a key role in areas such as autonomous driving and medical diagnosis?pass MCP Store As can be seen from its market performance, it has strong vitality and strong appeal. https://mcp.store

　　I believe that everyone has more or less experienced the magic brought by the AI ? ? big model. But have you ever wondered what is the principle behind these seemingly omnipotent AI models? Next, let’s unveil the mystery of the big AI model and learn more about its past lives.

　　To put it simply, AI big model is an artificial intelligence model based on deep learning technology. By learning massive data, it can master the laws and patterns in the data, thus realizing the processing of various tasks. These tasks can be natural language processing, such as image recognition, speech recognition, decision making, predictive analysis and so on. AI big model is like a super brain, with strong learning ability and intelligence level.

　　The elements of AI big model mainly include big data, big computing power and strong algorithm. Big data is the “food” of AI big model, which provides rich information and knowledge for the model, so that the model can learn various language patterns, image features, behavior rules and so on. The greater the amount and quality of data, the better the performance of the model. Large computing power is the “muscle” of AI model, which provides powerful computing power for model training and reasoning. Training a large AI model needs to consume a lot of computing resources. Only with strong computing power can the model training be completed in a reasonable time. Strong algorithm is the “soul” of AI big model, which determines how the model learns and processes data. Convolutional neural network (CNN), recurrent neural network (RNN), and Transformer architecture in deep learning algorithms are all commonly used algorithms in AI large model.

　　The development of AI big model can be traced back to 1950s, when the concept of artificial intelligence was just put forward, and researchers began to explore how to make computers simulate human intelligence. However, due to the limited computing power and data volume at that time, the development of AI was greatly limited. Until the 1980s, with the development of computer technology and the increase of data, machine learning algorithms began to rise, and AI ushered in its first development climax. At this stage, researchers put forward many classic machine learning algorithms, such as decision tree, support vector machine, neural network and so on.

　　In the 21st century, especially after 2010. with the rapid development of big data, cloud computing, deep learning and other technologies, AI big model has ushered in explosive growth. In 2012. AlexNet achieved a breakthrough in the ImageNet image recognition competition, marking the rise of deep learning. Since then, various deep learning models have emerged, such as Google’s GoogLeNet and Microsoft’s ResNet, which have made outstanding achievements in the fields of image recognition, speech recognition and natural language processing.

　　In 2017. Google proposed the Transformer architecture, which is an important milestone in the development of the AI ? ? big model. Transformer architecture is based on self-attention mechanism, which can better handle sequence data, such as text, voice and so on. Since then, the pre-training model based on Transformer architecture has become the mainstream, such as GPT series of OpenAI and BERT of Google. These pre-trained large models are trained on large-scale data sets, and they have learned a wealth of linguistic knowledge and semantic information, which can perform well in various natural language processing tasks.

　　In 2022. ChatGPT launched by OpenAI triggered a global AI craze. ChatGPT is based on GPT-3.5 architecture. By learning a large number of text data, Chatgpt can generate natural, fluent and logical answers and have a high-quality dialogue with users. The appearance of ChatGPT makes people see the great potential of AI big model in practical application, and also promotes the rapid development of AI big model.

　　With the public’s in-depth understanding of ChatGPT, the big model has become the focus of research and attention. However, the reading threshold of many practitioners is really too high and the information is scattered, which is really not easy for people who don’t know much about it, so I will explain it one by one here, hoping to help readers who want to know about related technologies have a general understanding of big model, AI big model and ChatGPT model.Hope for the future mcp server It can achieve rapid and stable development and serve social development and people’s needs well. https://mcp.store

　　* Note: I am a non-professional. The following statements may be imprecise or missing. Please make corrections in the comments section.

　　First, the big model

　　1.1 What is the big model?

　　Large model is the abbreviation of Large Language Model. Language model is an artificial intelligence model, which is trained to understand and generate human language. “Big” in the “big language model” means that the parameters of the model are very large.

　　Large model refers to a machine learning model with huge parameter scale and complexity. In the field of deep learning, large models usually refer to neural network models with millions to billions of parameters. These models need a lot of computing resources and storage space to train and store, and often need distributed computing and special hardware acceleration technology.

　　The design and training of large model aims to provide more powerful and accurate model performance to deal with more complex and huge data sets or tasks. Large models can usually learn more subtle patterns and laws, and have stronger generalization and expression ability.

　　Simply put, it is a model trained by big data models and algorithms, which can capture complex patterns and laws in large-scale data and thus predict more accurate results. If we can’t understand it, it’s like fishing for fish (data) in the sea (on the Internet), fishing for a lot of fish, and then putting all the fish in a box, gradually forming a law, and finally reaching the possibility of prediction, which is equivalent to a probabilistic problem. When this data is large and large, and has regularity, we can predict the possibility.

　　1.2 Why is the bigger the model?

　　Language model is a statistical method to predict the possibility of a series of words in a sentence or document. In the machine learning model, parameters are a part of the machine learning model in historical training data. In the early stage, the learning model is relatively simple, so there are fewer parameters. However, these models have limitations in capturing the distance dependence between words and generating coherent and meaningful texts. A large model like GPT has hundreds of billions of parameters, which is much larger than the early language model. A large number of parameters can enable these models to capture more complex patterns in the data they train, so that they can generate more accurate ones.

　　Second, AI big model

　　What is the 2.1 AI big model?

　　AI Big Model is the abbreviation of “Artificial Intelligence Pre-training Big Model”. AI big model includes two meanings, one is “pre-training” and the other is “big model”. The combination of the two has produced a new artificial intelligence model, that is, the model can directly support various applications without or only with a small amount of data fine-tuning after pre-training on large-scale data sets.

　　Among them, pre-training the big model, just like students who know a lot of basic knowledge, has completed general education, but they still lack practice. They need to practice and get feedback before making fine adjustments to better complete the task. Still need to constantly train it, in order to better use it for us.