What is the AI big model?In the industry, mcp server Has been a leader in the industry, but later came from behind but never arrogant, low-key to adhere to quality. https://mcp.store

　　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.

　　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.The data shows that, MCP Store Its development potential should not be underestimated, and it is also the inevitability of its existence. 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.

　　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.In the eyes of industry experts, mcp server Indeed, it has great development potential, which makes many investors more interested. 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.

　　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:It is reported that, mcp server The data performance is getting better and better, which is of great reference value and is likely to become the vane of the industry. 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.

　　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:At first, mcp server It developed out of control and gradually opened up a sky of its own. 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.

　　First, what is AI?With the expanding influence of the industry, MCP Store Our business is also constantly spreading, and the development of the market is also gradually advancing. https://mcp.store

　　First, let’s discuss the meaning of AI. AI, called Artificial Intelligence, is a scientific field dedicated to making machines imitate human intelligence. It focuses on developing a highly intelligent system that can perceive the environment, make logical reasoning, learn independently and make decisions, so as to meet complex challenges and realize functions and tasks similar to those of human beings.

　　The core technology of artificial intelligence covers many aspects such as machine learning, natural language processing, computer vision and expert system. Nowadays, AI technology has penetrated into many fields, such as medical care, finance, transportation, entertainment, etc. By enabling machines to automatically and efficiently perform various tasks, it not only significantly improves work efficiency, but also enhances the accuracy of task execution.

　　Second, what is the AI ? ? big model

　　Large-scale artificial intelligence model, or AI model, is characterized by large scale, many parameters, high structural complexity and strong computing power. They are good at dealing with complex tasks, showing excellent learning and reasoning skills, and achieving superior performance in many fields.

　　Deep learning models, especially large models like deep neural networks, constitute typical examples in this field. Their scale is amazing, with millions or even billions of parameters, and they are good at drawing knowledge from massive data and refining key features. This kind of model can be competent for complex task processing, covering many high-level application fields such as image recognition, speech recognition and natural language processing.

　　Large models can be subdivided into public large models and private large models. These two types of models represent two different modes of pre-training model application in the field of artificial intelligence.

　　Third, the public big model

　　Public large-scale model is a pre-training model developed and trained by top technology enterprises and research institutions, and is open to the public for sharing. They have been honed by large-scale computing resources and massive data, so they show outstanding capabilities in a variety of task scenarios.

　　Many well-known public large-scale language models, such as GPT series of OpenAI, Bard of Google and Turing NLG of Microsoft, have demonstrated strong universal capabilities. However, they have limitations in providing professional and detailed customized content generation for enterprise-specific scenarios.

　　Fourth, the private big model

　　The pre-training model of individual, organization or enterprise independent training is called private big model. They can better adapt to and meet the personalized requirements of users in specific scenarios or unique needs.

　　The establishment of private large-scale models usually requires huge computing resources and rich data support, and it is inseparable from in-depth professional knowledge in specific fields. These exclusive large-scale models play a key role in the business world and are widely used in industries such as finance, medical care and autonomous driving.

　　V. What is AIGC?

　　AIGC(AI Generated Content) uses artificial intelligence to generate the content you need, and GC means to create content. Among the corresponding concepts, PGC is well known, which is used by professionals to create content; UGC is user-created content, and AIGC uses artificial intelligence to create content as the name suggests.

　　VI. What is GPT?

　　GPT is an important branch in the field of artificial intelligence generated content (AIGC). Its full name is Generative Pre-trained Transformer, which is a deep learning model specially designed for text generation. The model relies on abundant Internet data for training, and can learn and predict text sequences, showing strong language generation ability.

　　What is the AI big model?By comparison, it can be seen that mcp server It has certain advantages and great cost performance. https://mcp.store

　　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.

　　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.with mcp server For example, if it continues to develop, it will definitely become the benchmark of the industry and play an important role in leading the market. 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.

This is due to cnc turning service Its value attribute is relatively large, and it is easy to become the backbone of the industry. https://www.ultirapid.com/services/cnc-machining/

According to a report by the Russian satellite news agency on March 14, the Russian National Guard announced on its Telegraph channel that the Russian National Guards, soldiers and FSB border guards fought a battle with members of the Ukrainian sabotage and reconnaissance team that were trying to sneak into Kursk.

Reported that the Russian National Guard released the news that: the Russian National Guard forces involved in repelling the enemy sabotage and reconnaissance team in the Kursk Prefecture near the town of Chotkino attack.

The governor of Kursk state, Roman Starrovoit, reported at around 11:45 Moscow time that the saboteurs were attempting to infiltrate.

Related news recommendations:

One Russian was killed when he was attacked for three days before the election.

According to Agence France-Presse, March 14, officials said that one person was killed and six injured in a drone attack on the evening of the 13th in a region bordering Russia and Ukraine. This is the third night of drone attacks on Russian territory.

Reported that the Russian Defense Ministry said that 14 Ukrainian drones were destroyed at night, which is the latest round of attacks on Russian territory before the Russian presidential election on March 15-17. Russian President Vladimir Putin is expected to win the election again.

Viaceslav Gladkov, governor of Belgorod, wrote in the Telegraph software: a civilian was killed. The man was driving when a shell hit his engine. He died on the spot of his injuries.

Gladkov said the airstrikes also destroyed two houses and one medical facility.

On the night of the 13th, 11 drones were shot down in the Belgorod region and three in the Kursk region, the Russian Defense Ministry said in a statement. (compiled by Lu Di)

German media: Ukraine systematically attacks Russian oil facilities with drones

Ukraine is systematically attacking Russian oil facilities with drones and achieving results again, German news television channel reported on March 13.

It is reported that three drones caused a fire at an oil refinery in Liangzan prefecture southeast of Moscow. The new Shahjinsk oil refinery in Rostov-upon-Don was also attacked. The latter is reported to be the largest oil refiner in southern Russia.

A source from Ukraine’s State Security Service told the Ukrainian newspaper Truth: we are systematically implementing a well-planned strategy to weaken the economic potential of the Russian Federation. Our task is to deprive the enemy of resources. He said the military’s fuel supply and Russia’s oil revenue for the war would be affected.

Reported that Ukraine hopes that the continued crackdown on Russian oil facilities can significantly reduce Russia’s fuel production. “Spring sowing in Russia will become very difficult, which means food prices will rise sharply,” former Deputy Minister of Internal Affairs Andr é Graschenko wrote on social platform X. Fuel shortages may also push up the prices of other commodities, he said.

A total of 58 Ukrainian drones were shot down by Russian air defense systems from midnight to early morning on March 13, the Russian Defense Ministry said. So far, however, the Russian military does not seem to have found a way to deal with Ukraine’s constantly developing and upgrading drones.

Itar-Tass quoted Kremlin spokesman Peskov as saying on the 12th: our soldiers are taking all necessary measures. He said that the air defense system is operating effectively, and whether the protection of industrial targets should be strengthened is a question to be answered by the Ministry of National Defense.

Putin said in an interview that the attacks are aimed at interfering with the upcoming presidential election. (compiler Wang Ting

In combination with these conditions, cnc machining services It can still let us see good development and bring fresh vitality to the whole market. https://www.ultirapid.com/services/cnc-machining/

According to a report by the Russian satellite news agency on March 14, the Russian National Guard announced on its Telegraph channel that the Russian National Guards, soldiers and FSB border guards fought a battle with members of the Ukrainian sabotage and reconnaissance team that were trying to sneak into Kursk.

Reported that the Russian National Guard released the news that: the Russian National Guard forces involved in repelling the enemy sabotage and reconnaissance team in the Kursk Prefecture near the town of Chotkino attack.

The governor of Kursk state, Roman Starrovoit, reported at around 11:45 Moscow time that the saboteurs were attempting to infiltrate.

Related news recommendations:

One Russian was killed when he was attacked for three days before the election.

According to Agence France-Presse, March 14, officials said that one person was killed and six injured in a drone attack on the evening of the 13th in a region bordering Russia and Ukraine. This is the third night of drone attacks on Russian territory.

Reported that the Russian Defense Ministry said that 14 Ukrainian drones were destroyed at night, which is the latest round of attacks on Russian territory before the Russian presidential election on March 15-17. Russian President Vladimir Putin is expected to win the election again.

Viaceslav Gladkov, governor of Belgorod, wrote in the Telegraph software: a civilian was killed. The man was driving when a shell hit his engine. He died on the spot of his injuries.

Gladkov said the airstrikes also destroyed two houses and one medical facility.

On the night of the 13th, 11 drones were shot down in the Belgorod region and three in the Kursk region, the Russian Defense Ministry said in a statement. (compiled by Lu Di)

German media: Ukraine systematically attacks Russian oil facilities with drones

Ukraine is systematically attacking Russian oil facilities with drones and achieving results again, German news television channel reported on March 13.

It is reported that three drones caused a fire at an oil refinery in Liangzan prefecture southeast of Moscow. The new Shahjinsk oil refinery in Rostov-upon-Don was also attacked. The latter is reported to be the largest oil refiner in southern Russia.

A source from Ukraine’s State Security Service told the Ukrainian newspaper Truth: we are systematically implementing a well-planned strategy to weaken the economic potential of the Russian Federation. Our task is to deprive the enemy of resources. He said the military’s fuel supply and Russia’s oil revenue for the war would be affected.

Reported that Ukraine hopes that the continued crackdown on Russian oil facilities can significantly reduce Russia’s fuel production. “Spring sowing in Russia will become very difficult, which means food prices will rise sharply,” former Deputy Minister of Internal Affairs Andr é Graschenko wrote on social platform X. Fuel shortages may also push up the prices of other commodities, he said.

A total of 58 Ukrainian drones were shot down by Russian air defense systems from midnight to early morning on March 13, the Russian Defense Ministry said. So far, however, the Russian military does not seem to have found a way to deal with Ukraine’s constantly developing and upgrading drones.

Itar-Tass quoted Kremlin spokesman Peskov as saying on the 12th: our soldiers are taking all necessary measures. He said that the air defense system is operating effectively, and whether the protection of industrial targets should be strengthened is a question to be answered by the Ministry of National Defense.

Putin said in an interview that the attacks are aimed at interfering with the upcoming presidential election. (compiler Wang Ting