Monthly Archives: April 2025

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Webb Auction House, which is in charge of the auction, said that a feather of the long-extinct new Zealand gray butterfly bird (H) set a record price of 46521 new Zealand dollars (about 205874 yuan), making it the most expensive feather ever in the world.

H was last confirmed to be seen in the early 20th century, and its feathers previously sold for as much as 8400 new Zealand dollars (approximately 37173 yuan).

As a member of the wybird family, H was cherished by many and ultimately proved fatal to the species.

For Maori, the bird’s feathers are a symbol of lofty status, and its unique white feathers are used for ceremonial headdresses. Only those in the main ranks are allowed to wear H feathers on their hair, or entire skins on their ears, according to the new Zealand Museum.

The auction house said the feathers were often exchanged for other valuables or given as gifts to show friendship and respect.

European new Zealand people have also begun to regard H as a symbol of prestige. According to the museum, they use the animal’s feathers as fashion accessories and install padded H’s as decoration in wealthy homes.

The museum explained that in the 19th century, Maori and European hunters killed the bird in large numbers and sold its skin to collectors and fashion merchants.

According to reports, in 1901, the Duke and Duchess of York were photographed wearing H feathers in their hats while traveling in new Zealand, and the bird’s deadly popularity further increased.

People are a little crazy, everyone wants an H feather.

In the early 20th century, scientists tried to protect the remaining H, but failed. According to the museum, the government planned to transport the birds to offshore islands, but those who collected the birds sold them as dead specimens, adding that it was more valuable than keeping them alive.

The auction house said all potential buyers must provide a permit from the new Zealand Department of Culture and Heritage before Monday’s auction. As an important national item, feathers can only be purchased by registered collectors and cannot be exported without permission from the Ministry of Health. (Reporter Wang Yuehang)

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According to CCTV news client, on the 12th local time, the Sudanese Armed Forces and the Rapid Support Force, both sides of the Sudanese armed conflict, continued to exchange fire in various areas of the capital Khartoum. The emergency room of the Sudanese humanitarian aid group says six people have been killed when shells hit a residential area in southern Khartoum.

The Rapid Support Force said on the same day that the Sudanese Armed Forces used heavy artillery to bomb the Presidential Palace in the center of the city, causing a fire to the Old Republican Palace in the Presidential Palace. Rapid Support Force soldiers who controlled the presidential palace then extinguished the fire. The Sudanese Armed Forces have not responded to this.

On April 15, 2023, an armed conflict broke out between the Sudanese Armed Forces and the Rapid Support Force in the capital Khartoum, which subsequently spread to other areas and continues to this day. The presidential palace was controlled by the Rapid Support Force at the beginning of the conflict and has been bombarded and shelled many times.

The two sides in the Sudanese conflict recently exchanged fire in the western part of the country

27 civilians have been killed

On the 12th local time, the United Nations Office for the Coordination of Humanitarian Affairs issued a statement saying that the Sudanese Armed Forces and the Rapid Support Force have exchanged fire again in El Fasher, the capital of Northern Darfur State in western Sudan, since May 10, killing at least 27 civilians. About 130 people were injured.

The statement said that only one hospital is still operating in El Fasher, but there is a serious shortage of medicines and medical supplies. In addition, the conflict caused a large number of local civilians to flee southward from the east and northwest of El Fasher city. Since early April this year, more than 40,000 people have been displaced in the city.

According to local media reports, the two parties to the conflict exchanged fire in various areas in the east, south and north of El Fasher since the early morning of the 12th, and the fighting continued into the evening. Many residential areas in the city were hit by air strikes and shelling, causing civilian casualties. El Fasher Children’s Hospital in the south of the city was forced to evacuate.

According to reports, recently, the two parties to the conflict have fought fiercely for control of Northern Darfur.

Photo source: CCTV News Client

A market attacked in the capital of North Kordofan State in Sudan

At least 15 civilians were killed

According to Sudanese media reports on the 12th local time, the Sudanese Rapid Support Force launched an attack on a market west of Obayid City, the capital of Northern Kordofan State in the west of the country, and clashed with people in the market, killing at least 15 civilians. Death.

Recently, there have been many incidents of armed elements attacking villages and towns in Gezira and Sennar states in central Sudan, as well as in the Kordofan region and Darfur region in western Sudan. Statistics from the Sudanese civil organization Resistance Committee show that the attack has killed hundreds of civilians. The Resistance Council said the perpetrators of these attacks were members of the Rapid Support Force. The Rapid Support Force denied this.

China News Service integrated from CCTV News Client

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According to a report by the Tass news agency on July 17, Alexei Polisyuk, Director of the Second Department of CIS Affairs of the Russian Ministry of Foreign Affairs, told the Tass news agency that the United States has authorized Ukraine President Zelensky to launch a new strike on Russian territory.

Polisiuk pointed out: Washington is still fueling the escalation of the situation. They tried to justify their crimes while defending Kiev. Not only that, the Zelensky regime was also granted full powers to use American weapons to carry out new strikes on Russian territory.

He emphasized that the United States is still indulging in the dream of its own superior position, hoping that if Russia suffers strategic failure, it can safely hide across the ocean. He warned: Few among the American elite are aware of the risks of such arrogant and underestimating opponents ‘self-deception. For the United States and the world, the consequences may be unpredictable.

Polisiuk emphasized that behind the missile attack on Sevastopol, there was no doubt that U.S. military experts were providing various support services. They used the Pentagon’s drones circling over the area to target targets. (Russia) has made the most solemn representations to the U.S. ambassador to Moscow and the U.S. authorities in this regard, sending the clearest signal that terrorist attacks will lead to inevitable retaliation. As for the safety of the Crimean Peninsula and local residents, this is an absolute priority, and (Russia) is doing its best to provide reliable security. (Compiled by Tong Shiqun)

　　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.However, in other words, we should know more about it. MCP Store The law of development has brought new vitality to the whole industry and revitalized the market. https://mcp.store

　　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.

　　What is the AI big model?Even so, Daily Dles We must also adhere to the quality of the industry and create unique products for the company. 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.

　　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:In order to achieve the goal, Daily Dles Turn cocoon into butterfly, constantly polish product quality, improve business ability, and finally have a place in the market. https://dles.games

　　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.In the past ten years, MCP Store Defeated many competitors, courageously advanced in the struggle, and polished many good products for customers. 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.

　　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:If we can practice these points, Daily Dles Will be unique, become a leader in the industry, and keep moving forward. 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.

　　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.Now, everyone is right mcp server Are more concerned, hoping to get more benefits from it. https://mcp.store

　　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.In today’s market background, Daily Dles Still maintain a strong sales data, and constantly beat the competitors in front of us. 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.