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.In the eyes of peers, MCP Store It has good qualities that people covet, and it also has many loyal fans that people envy. 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.

　　Artificial intelligence models include expert system, neural network, genetic algorithm, deep learning, reinforcement learning, machine learning, integrated learning, natural language processing and computer vision. ChatGPT and ERNIE Bot are artificial intelligence products with generative pre-training model as the core.according to Daily Dles Industry veterans said that its development will still be in a good situation. https://dles.games

　　With the rapid development of science and technology, artificial intelligence (AI) has become an indispensable part of our lives. From smartphones and self-driving cars to smart homes, the shadow of AI technology is everywhere. Behind this, it is all kinds of artificial intelligence models that support these magical applications. Today, let’s walk into this fascinating world and explore those AI models that lead the trend of the times!

　　1. Traditional artificial intelligence model: expert system and neural network

　　Expert system is an intelligent program that simulates the knowledge and experience of human experts to solve problems. Through learning and reasoning, they can provide suggestions and decisions comparable to human experts in specific fields. Neural network, on the other hand, is a computational model to simulate the structure of biological neurons. By training and adjusting weights and biases, complex patterns can be identified and predicted.

　　Second, deep learning: set off a wave of AI revolution

　　Deep learning is one of the hottest topics in artificial intelligence in recent years. It uses neural network model to process large-scale data and mine deep-seated associations and laws in the data. Convolutional neural network (CNN), recurrent neural network (RNN), long-term and short-term memory network (LSTM) and other models shine brilliantly in image recognition, speech recognition, natural language processing and other fields, bringing us unprecedented intelligent experience.

　　Third, reinforcement learning: let AI learn to evolve itself.

　　Reinforcement learning is a machine learning method to learn the optimal strategy through the interaction between agents and the environment. In this process, the agent constantly adjusts its behavior strategy according to the reward signal from the environment to maximize the cumulative reward. Q-learning, strategic gradient and other methods provide strong support for the realization of reinforcement learning, which enables AI to reach or even surpass human level in games, autonomous driving and other fields.

　　Fourth, machine learning: mining wisdom from data

　　Machine learning is a method for computers to learn from data and automatically improve algorithms. Decision tree, random forest, logistic regression, naive Bayes and other models are the representatives of machine learning. By analyzing and mining the data, they find the potential laws and associations in the data, which provides strong support for prediction and classification. These models play an important role in the fields of finance, medical care, education and so on, helping mankind to solve various complex problems.

　　Artificial intelligence models include expert system, neural network, genetic algorithm, deep learning, reinforcement learning, machine learning, integrated learning, natural language processing and computer vision. ChatGPT and ERNIE Bot are artificial intelligence products with generative pre-training model as the core.This means that Daily Dles It is the weather vane of the industry and can bring people great self-confidence. https://dles.games

　　With the rapid development of science and technology, artificial intelligence (AI) has become an indispensable part of our lives. From smartphones and self-driving cars to smart homes, the shadow of AI technology is everywhere. Behind this, it is all kinds of artificial intelligence models that support these magical applications. Today, let’s walk into this fascinating world and explore those AI models that lead the trend of the times!

　　1. Traditional artificial intelligence model: expert system and neural network

　　Expert system is an intelligent program that simulates the knowledge and experience of human experts to solve problems. Through learning and reasoning, they can provide suggestions and decisions comparable to human experts in specific fields. Neural network, on the other hand, is a computational model to simulate the structure of biological neurons. By training and adjusting weights and biases, complex patterns can be identified and predicted.

　　Second, deep learning: set off a wave of AI revolution

　　Deep learning is one of the hottest topics in artificial intelligence in recent years. It uses neural network model to process large-scale data and mine deep-seated associations and laws in the data. Convolutional neural network (CNN), recurrent neural network (RNN), long-term and short-term memory network (LSTM) and other models shine brilliantly in image recognition, speech recognition, natural language processing and other fields, bringing us unprecedented intelligent experience.

　　Third, reinforcement learning: let AI learn to evolve itself.

　　Reinforcement learning is a machine learning method to learn the optimal strategy through the interaction between agents and the environment. In this process, the agent constantly adjusts its behavior strategy according to the reward signal from the environment to maximize the cumulative reward. Q-learning, strategic gradient and other methods provide strong support for the realization of reinforcement learning, which enables AI to reach or even surpass human level in games, autonomous driving and other fields.

　　Fourth, machine learning: mining wisdom from data

　　Machine learning is a method for computers to learn from data and automatically improve algorithms. Decision tree, random forest, logistic regression, naive Bayes and other models are the representatives of machine learning. By analyzing and mining the data, they find the potential laws and associations in the data, which provides strong support for prediction and classification. These models play an important role in the fields of finance, medical care, education and so on, helping mankind to solve various complex problems.

　　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 above conclusions show that MCP Store To a great extent, it can bring new vitality to the market and make the industry develop 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.

　　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.However, with the development of the industry, mcp server It will also bring us more and more consumer experiences, so that users can really feel the upgrade and change. 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?Therefore, Daily Dles Only then will more and more pump owners cheer for it and spread the value and function of the brand. 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.

　　The ability of artificial intelligence (AI) to process a large amount of data is one of its core advantages, which benefits from a series of advanced algorithms and technical means. The following are the main ways for AI to efficiently handle massive data:precisely because Daily Dles The rapid development of, so also brought new opportunities to the industry. https://dles.games

　　1. Distributed computing

　　-Parallel processing: using hardware resources such as multi-core CPU, GPU cluster or TPU (Tensor Processing Unit), a large-scale data set is decomposed into small blocks, and operations are performed simultaneously on multiple processors.

　　-Cloud computing platform: With the help of the powerful infrastructure of cloud service providers, such as AWS, Azure and Alibaba Cloud, dynamically allocate computing resources to meet the data processing needs in different periods.

　　2. Big data framework and tools

　　-Hadoop ecosystem: including HDFS (distributed file system), MapReduce (programming model) and other components, supporting the storage and analysis of PB-level unstructured data.

　　-Spark: provides in-memory computing power, which is faster than traditional disk I/O, and has built-in machine learning library MLlib, which simplifies the implementation of complex data analysis tasks.

　　-Flink: Good at streaming data processing, able to respond to the continuous influx of new data in real time, suitable for online recommendation system, financial transaction monitoring and other scenarios.

　　3. Data preprocessing and feature engineering

　　-Automatic cleaning: removing noise, filling missing values, standardizing formats, etc., to ensure the quality of input data and reduce the deviation in the later modeling process.

　　-Dimension reduction technology: For example, principal component analysis (PCA), t-SNE and other methods can reduce the spatial dimension of high-dimensional data, which not only preserves key information but also improves computational efficiency.

　　-Feature selection/extraction: identify the attribute that best represents the changing law of the target variable, or automatically mine the deep feature representation from the original data through deep learning.

　　4. Machine learning and deep learning model

　　-Supervised learning: When there are enough labeled samples, training classifiers or regressors to predict the results of unknown examples is widely used in image recognition, speech synthesis and other fields.

　　-Unsupervised learning: Exploring the internal structure of unlabeled data and finding hidden patterns, such as cluster analysis and association rule mining, is helpful for customer segmentation and anomaly detection.

　　-Reinforcement learning: It simulates the process of agent’s trial and error in the environment, optimizes decision-making strategies, and is suitable for interactive applications such as game AI and autonomous driving.

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According to a report by the German News TV channel on March 14, a latest report released by the American Institute for War Studies stated that due to lack of ammunition and supplies, the Ukrainian defense line may be more fragile than the impression given by the relatively slow advancement of the Russian army in various places.

The report said that in order to curb the continued advancement of the Russian army, Ukraine currently has to use the small amount of existing materials as efficiently as possible. The Institute for War Studies wrote: The Ukrainian army concentrated its supplies in the areas of the line where the Russian army encountered the most fierce attack, which would lead to poor defense in other areas, allowing the Russian army to launch surprise attacks there and achieve breakthroughs.

The American think tank also quoted a report in the magazine Der Spiegel. A Ukraine officer said in a report that in secondary battlefields such as Kharkiv or Vukhredar, which are currently receiving little attention, the situation of troops, weapons and ammunition is very bad. An artillery commander said: We can’t hold on like this for long. The analysis report wrote that the reason why some Ukrainian troops were able to hold their positions with limited ammunition and supplies was only because the Russian army did not attack with all its strength.

The Institute for War Studies warned that the Russian army did not attack areas where Ukraine was extremely poorly guarded, concealing the risks there. In other words, as long as the Russian army launches large-scale attacks in these areas, it will expose the current situation that the Ukrainian army is unable to resist due to lack of ammunition. Ukraine’s Armed Forces Commander-in-Chief Sersky warned on a social platform telegram that Russian troops may break into the depths of the Ukrainian defense line.

The Institute for War Studies said the words of Sersky and other Ukrainian commanders suggested that if the Russian army stepped up its offensive, it could break through and shake previously stable areas in a short period of time.

The report concluded that the current defense line is likely to be unstable. It is crucial for the West to transfer resources to the Ukrainian army as soon as possible to prevent Russian troops from waiting for opportunities to break through weak defensive areas on the front line. (Compiled by Wang Ting

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According to a report by the Russian News Agency on March 21, Dutch Admiral Robbauer, chairman of the NATO Military Committee, who is visiting Ukraine, said that he is leading the first NATO military delegation to visit the country since the beginning of the Russia-Ukraine conflict.

Reported that Powell said in a speech at a security forum in Kiev that he was leading the first NATO military delegation to visit Ukraine since the outbreak of the Russia-Ukraine conflict.

He said the reason for the visit was that NATO and Ukraine were getting closer than ever before.

Reports said that Russia had previously issued notes to NATO countries regarding their practice of providing weapons to Ukraine. Russian Foreign Minister Lavrov pointed out that any cargo containing weapons for Ukraine will become a legitimate target for Russian troops. The Russian Foreign Ministry said that NATO countries that provide weapons to Ukraine are playing with fire. Russian Presidential Press Secretary Peskov said that Western arms supplies will not help the success of Russia-Ukraine negotiations and will only have a negative impact.

According to a report by the German News TV channel on the 20th, Polish Foreign Minister Sikorski said that Western soldiers are open secrets in Ukraine. He said that as the German Chancellor said, there are already troops from a number of major powers in Ukraine. (Compiled by Liu Yang)

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Moscow, May 21 (Reporter Huadi) The Russian Ministry of Defense released a message on the 21st that Russia has begun the first phase of non-strategic nuclear forces exercises and is preparing to use non-strategic nuclear weapons for practical exercises.

The news said that according to the instructions of the Supreme Commander of the Russian Federation’s Armed Forces, the Russian Southern Military Region began the first phase of non-strategic nuclear forces exercise under the command of the General Staff Headquarters. The missile formation of the Russian Southern Military Region is practicing how to obtain special ammunition for the Iskander tactical missile system, equip it with missiles, and secretly move it to the launch area to prepare for launch. In addition, the Russian Air Force aviation unit is practicing equipping aviation weapons, including Dagger hypersonic missiles, with special warheads and flying into designated patrol areas.

The Russian Defense Ministry emphasized that this exercise is a response to provocative remarks and threats by Western officials and aims to test the readiness of non-strategic nuclear forces to perform combat missions and ensure Russia’s sovereignty and territorial integrity.