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According to a report by the Russian News Agency on March 20, Russian Foreign Ministry Spokesperson Zakharova said at a press conference that NATO has chosen a new direction of confrontation with Russia.

Reported that Zakharova said: NATO’s top priority is to open up a second front against my country in the Transcaucasia and reignite the war throughout the region.

She said NATO was not satisfied with Russia’s conciliatory policies in the Transcaucasia region.

The report mentioned that NATO Secretary General Stoltenberg visited Yerevan, the capital of Armenia, on the 19th and called on Armenia and Azerbaijan to sign an agreement aimed at paving the way for the normalization of relations between the two countries.

Russian Presidential Press Secretary Peskov said that NATO’s attempts to expand its influence may not strengthen stability in the Caucasus region. (Compiled by Zheng Yu)

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NATO troops exercise near Russian border

According to a report by the Russian News Agency on March 20, the Polish Ministry of Defense released a message saying that the NATO Northeast Division, composed of multinational forces, practiced defensive operations during exercises held in imaginary areas near the Russian border.

Poland’s Ministry of Defense said: During the Loyalty Lecda 2024 exercise, soldiers of the multinational Northeast Division were given such a task to carry out defensive operations in an imaginary area similar to the situation in northeastern Poland.

Reported that the command department of NATO’s Northeast Division refused to disclose details of the exercise content.

According to reports, the NATO Northeast Division headquarters is located in Elblong, Poland, and its mission is to coordinate the actions of NATO troops deployed in Poland, Lithuania, Latvia and Estonia. (Compiled by Liu Yang)

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According to a BBC report on the 20th, researchers at the University of Amsterdam in the Netherlands said at a medical conference held on the 18th that they successfully removed the HIV virus (HIV) from infected cells using CRISPR gene editing technology.

Screenshots of related reports

It is reported that although existing drugs can prevent the HIV virus from further penetrating into the human body, they cannot clear the existing virus in the patient’s body. Even if patients receive effective treatment and the HIV virus in their bodies enters a dormant or latent state, they still carry HIV DNA or genetic material.

According to the BBC, the CRISPR gene editing technology used by the researchers is similar in principle to scissors, which can remove or inactivate HIV-infected cells by cutting DNA. This gene editing technology won the 2020 Nobel Prize in Chemistry.

At present, the scientific research process at the University of Amsterdam is still in the proof-of-concept stage, and it will take a long time for it to be truly applied to AIDS treatment.

Scientists believe that if this new technology can be further developed, it will be expected to eliminate all HIV in the human body, thereby curing AIDS. However, some virologists also said that the possible off-target effects and long-term side effects of this technology are worrying.

Wen| Reporter Leng Shuang

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According to the Associated Press reported on March 17, U.S. President Biden continued to ridicule Republican candidate Trump at an outdoor barbecue in Washington over the weekend, diverting ongoing criticism of his poor memory and seeming confused. He emphasized that 77-year-old Trump also makes mistakes.

Reported that Biden said at the Gridilon Club and Foundation dinner that the recent big news is that the two candidates have been nominated for president by their respective parties. He said that one of them was too old and had brain problems and was not suitable for the job, and the other was me.

Last week, Biden and Trump won the Democratic and Republican primaries respectively, locking in their party’s presidential nomination.

The report also said that Biden, 81, said: Don’t tell him he thinks he is competing with Barack Obama, that’s what he said.

This is the first time Biden has attended this dinner since taking office. The 2024 U.S. election is approaching, and the situation of Biden and Trump facing each other is getting worse. (Compiled by Yang Xinpeng)

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According to a report by the Russian News Agency on March 20, Russian law enforcement sources revealed that a man was sentenced and expelled from Russian citizenship by the Krasnodar District Court for spreading military-related rumors.

Reports said that in July 2023, Russian Alexander Somryakov was sentenced to six years in prison for deliberately spreading false information about the Russian army. The man posted a post on social networks about Russian troops suspected of committing crimes in Mariupol and other places.

Somryakov pleaded guilty to some facts at a trial in July last year, saying he knew that the information released was false. But he wanted the information to be tried by the public and to attract traffic.

The report pointed out that before this, there had been no cases of convictions and revocation of citizenship in Russia under the Criminal Code.

In October 2023, the “The Russian Federation Citizenship Law” came into effect, which includes a system of cancellation of nationality. Offences against public and personal security are one of the grounds for being convicted of revocation of nationality. At the same time, the decision to revoke nationality is not affected by the time of the crime, the date of sentence and the date of naturalization. (Compiled by Li Ran)

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Cairo, March 18 (Reporters Zhang Jian and Yao Bing) Egyptian President Sisi said on the 17th that the European Union agreed to provide Egypt with a financial support package of approximately 7.4 billion euros on the same day to boost the Egyptian economy.

Sisi announced the plan at a joint press conference with visiting European Commission President Von der Leyen and other senior EU officials. He said that the financial package mainly involves three aspects of the Egyptian economy, namely preferential financing, investment guarantees, and technical support for the implementation of bilateral cooperation projects.

We discussed naming energy as a key area of cooperation, especially the interconnection of natural gas and electricity. Sisi said that the two sides have agreed to cooperate in green hydrogen production.

According to a statement issued by the Egyptian Presidential Palace, Egypt and the European Union are also preparing to hold a joint investment meeting in the second half of 2024, welcoming more European participation in developing the Egyptian market.

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According to Bloomberg News reported on May 20, copper prices soared to their highest level in history, continuing their months-long rally. The rally was driven by financial investors who poured into the market amid predictions that supply shortages would intensify.

Copper futures prices on the London Metal Exchange rose more than 4%. Copper prices topped US$110,000 a tonne for the first time, but gave up some of their gains in afternoon trading.

For months, banks, mining and investment funds have been touting copper’s bright long-term prospects. In the past few weeks, investment has poured into the market.

Several developments in 2024 have emboldened copper bulls and attracted more and more speculative funds. Rumors are rampant that tight supply of copper ore has caused smelters to cut production. Investors predict that a surge in copper use in fast-growing sectors such as electric vehicles, renewable energy and artificial intelligence will offset the drag caused by traditional industries such as construction.

In early April, copper prices began to rise. Last week, a short squeeze on the New York futures market triggered a global rush to buy copper, and copper prices rose into overdrive.

Investors, traders and mining executives have been warning for years that the world will face severe copper shortages as demand for green industries surges.

However, many people involved in physical transactions warn that copper prices are outperforming reality. Demand remains relatively tepid, especially in China, the largest buyer, where inventory levels remain high and suppliers of copper wire and rod have been cutting production.

Since the beginning of this year, copper prices have risen by more than a quarter, leading the overall increase in major industrial metals. Like copper, gold prices also rose to record levels. Both metals are supported by optimism that the Federal Reserve will start cutting interest rates this year. (Compiled by Qiu Fang)

　　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:contemporaneity mcp server Our competitors have not made large-scale improvements, so we should get ahead of everyone in the project. 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.

　　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.Only by working together can we turn mcp server The value of the play out, the development of the supply market needs. 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:know MCP Store Our growth has to go through many hardships, but entrepreneurs are never afraid and boldly move forward. 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.

　　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.among mcp server It has given great spiritual support to entrepreneurs, and more entrepreneurs will contribute to this industry in the future. 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.