Monthly Archives: April 2025

　　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 some cases, mcp server The advantages will become more and more obvious, and it will be able to develop indomitable after market tests. 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.

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

　　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: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

　　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.

　　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.Therefore, we should understand mcp server Many benefits, absorb and summarize, and use them. 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.

　　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?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

　　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.

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Gaza, July 16 (Reporter Huang Zemin) The media office of the Palestinian Islamic Resistance Movement (Hamas) issued a statement on the 16th saying that the Israeli Air Force airstrikes a school in the Nusayat refugee camp in the central Gaza Strip that day, killing at least 23 people and injuring 73 people.

The statement said the school is affiliated with the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA). There are more than 10 UNRWA schools in the Nusayrat refugee camp, housing more than 80,000 displaced refugees.

The statement said that the Israeli Air Force also carried out air strikes in the Mawasi area of Khan Younis in the southern Gaza Strip that day, killing at least 17 people and injuring 26 others.

The Israel Defense Forces issued a statement on the 16th, saying that the Israeli Air Force attacked terrorists in an UNRWA school in Nusayriat. These terrorists had planned many attacks against the Israeli army. In addition, the Israeli Air Force also attacked a Palestinian Islamic Jihad officer in western Khan Younis.

Since the outbreak of a new round of Palestinian-Israeli conflict on October 7 last year, Israeli military operations in the Gaza Strip have killed more than 380,000 Palestinians and injured more than 890,000 others.

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According to a report by Singapore’s Lianhe Zaobao on March 21, there are still a few months before the 2024 U.S. presidential election. A number of polls show that Democratic President Biden’s approval rating is slightly ahead of former Republican President Trump.

Reported that Biden and Trump have recently locked in the Democratic and Republican presidential candidacies. If nothing unexpected, the two will face off again in the presidential election.

According to reports, according to the Economist Weekly poll average, as of March 19, Biden led Trump slightly (44%) with a 45% approval rating. At least three polls conducted in March showed Biden would defeat Trump in the November presidential election.

The report also said that Reuters Ipsos conducted a survey of 3356 registered voters from March 7 to 13. The poll results showed that Biden would receive 39% of the votes, and Trump’s vote rate would be 38%.

Another poll, conducted from March 9 to 12, showed Biden would receive 45% of the vote, compared with Trump’s 44%.

The report pointed out that a number of previous polls have shown that most Americans do not want to see Biden and Trump rematch.

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According to a report by the Russian newspaper on May 23, the Russian armed forces regained control of the village of Klesheevka in the Donetsk region through hard work. This settlement and the village of Rabodino, also recaptured by Russian troops, are symbols of the results of last year’s Ukraine counterattack.

On May 22, the Russian Ministry of Defense confirmed the news of the recapture of the village of Kresheevka. The Russian Ministry of Defense stated that under the active action of the southern military cluster forces, the Klesheevka settlement in Donetsk was liberated.

According to reports, in addition, the southern military cluster also attacked Ukrainian troops in three residential areas of Georgievka, Ostroye, and Konstantinovka.

The protracted battle for Klesheevka begins in 2023. Control of the village changed hands several times. The main target for contention is adjacent highlands, from which military activity throughout the village can be monitored. (Compiled by He Yingjun)

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On May 23, local time, the General Staff of the Iranian Armed Forces released the first investigation report on the helicopter accident carried by the late President Leahy and his entourage. It is reported that a professional technical team dispatched from Tehran arrived at the helicopter crash site at 9 a.m. local time on the 20th to collect information.

The report shows:

The helicopter flew according to the planned route and did not leave the designated route.

About a minute and a half before the helicopter accident, the pilot of the helicopter that crashed communicated with the other two helicopters of the flight crew.

No bullet marks or similar conditions were found in the remaining parts of the crashed helicopter.

The crashed helicopter caught fire after reaching high altitude.

Due to complex regional conditions, heavy fog and low temperatures, the search and rescue operation was extended and continued throughout the night. At 5 a.m. on May 20, with the help of Iranian drones, the location of the accident was accurately located.

No suspicious circumstances were found in the tower’s conversations with the crew.

The report also mentioned that most of the information related to the helicopter accident has been collected, and that some content needs more time for Iran to review. (General reporter Li Shuangxi)

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Beijing, March 18: South Korea’s Deputy Minister of Health and Welfare Park Min-soo accused medical university professors of preparing to resign collectively as coercion on the 17th, and reiterated that the government will not give in to the expansion plan of medical universities.

The South Korean government announced an expansion plan for medical universities in early February, which was strongly opposed by the medical community. Nearly 10,000 interns and residents submitted their resignations and went on strike, causing confusion in diagnosis and treatment. Medical university students also collectively applied for suspension of school in protest. The Emergency Response Committee for Professors of the National Medical College of Korea announced on the 15th that professors from 16 university medical schools will collectively resign on the 25th of this month.

On October 18, 2023, tourists visited Gwanghwamun, Seoul, South Korea. Photo by reporter Wang Yiliang

Park Min-soo said in a speech on Yonhap News Agency TV on the 17th that the government will never adjust the plan to expand enrollment by 2000 people. The collective resignation of medical university professors is a threat to the public, and collective protests in the medical community must be stopped. Professors ‘claim that if students are at a disadvantage, they will not sit idly by is a challenge to the law.

Chu Young-soo, president of the National Central Medical Hospital of South Korea, said at a press conference that the medical university professor planned to resign in protest, threatening the patient’s health and even life. It is really desperate that a medical professor with an important position in the medical world should say such a thing.

Chu Young-soo also apologized for the hospital’s doctors ‘previous statement in support of the strike, saying that the statement did not represent the position of the National Central Medical Center, and urged the striking doctors to return to work as soon as possible.

As aging intensifies, Korean society will have an increasing demand for medical resources. According to estimates by the South Korean health department, if the current enrollment scale is maintained, the shortage of doctors in South Korea will reach 150,000 by 2035.

South Korean people generally welcome the medical university’s expansion plan. The medical community expressed opposition. They believed that the government’s expansion plan would address the symptoms rather than the root cause, and would not solve the problems of shortage of medical personnel and uneven resource allocation. Moreover, blind expansion of enrollment may lead to excessive medical care, thereby increasing the financial burden on the medical insurance system, and may also reduce the quality of teaching in medical schools. Critics say some in the medical profession are actually worried that expansion will lead to a reduction in their income. (Li Yannan)