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Sydney, March 11 (Reporters Liang Youchang and Wang Qi) Chinese Ambassador to Australia Xiao Qian said on the 11th that the current world’s major changes unseen in a century are accelerating and many challenges are emerging one after another. China-Australia cooperation is not only in line with the common interests of the two countries, but also conducive to peace, stability and prosperity in the region and even the world.

Xiao Qian delivered a speech when attending the 2024 Australia Financial Review Business Summit in Sydney that day, saying that China and Australia are both important countries in the Asia-Pacific region. The economic structures of the two countries are highly complementary and pragmatic cooperation is mutually beneficial and win-win. To achieve this goal, it is crucial to establish a correct understanding of each other, deepen practical cooperation, and properly handle differences.

Xiao Qian said that this year marks the 10th anniversary of the establishment of a comprehensive strategic partnership between China and Australia. The so-called comprehensive means that China-Australia relations are not just cooperation in the economic and trade fields, but cooperation in any potential field and level. The so-called strategy means that the importance of bilateral relations transcends bilateral relations and has regional and even global significance. The so-called partner means that China and Australia should become mutually trusting friends rather than enemies, partners rather than opponents.

Xiao Qian said that practical cooperation between China and Australia is very important to both countries. The two sides should continue to consolidate and deepen cooperation in traditional fields such as energy, mining, agriculture, education, and tourism, and expand cooperation in emerging fields such as climate change, electric vehicles, artificial intelligence, health industry, green economy, digital economy, and technological innovation. These will help China and Australia open a new chapter of broader and mutually beneficial cooperation on the basis of continuously consolidating cooperation in existing fields.

Xiao Qian pointed out that both sides should focus on the overall situation, focus on consensus, respect each other’s core interests and major concerns, and manage differences in a proper, mature and intelligent way through friendly communication and consultation.

The 2024 Australia Financial Review Business Summit will be held in Sydney from March 11 to 12. Government officials such as Australia Foreign Minister Wong Ying-hsien, Treasury Secretary Chalmers, as well as opposition leader Dutton, local business executives, experts and scholars attended the meeting to analyze and study the international situation, focus on Australia’s future development, and explore cooperative measures to respond to challenges.

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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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Houston, May 15 (Reporter Xu Jianmei) The municipal authority of Galveston, an island city in eastern Texas, said on the 15th that a barge hit a local bridge that day, causing a fuel leak and damage to the bridge.

The bridge involved was the only land route to Pelican Island north of Galveston. David Flores, head of bridge affairs for the Galveston County Navigation District, said that due to strong water and high waves, a tugboat from Texas International Port lost control of two refueling barges it was driving, one of which hit the bridge pillar.

Aerial footage showed that part of the bridge pillars collapsed, and large pieces of broken concrete and rail fragments hung on one side of the bridge and fell onto the barge.

After the accident, the bridge, which was opened in 1960, was closed. Galveston City Authority said in a statement that no injuries had been reported. The accident caused a fuel leak in the Gulf of Mexico, and the U.S. Coast Guard will determine the extent of the leak and initiate control and cleanup procedures.

Galveston County spokesman Spencer Lewis said the barge could carry 30,000 gallons of oil (about 113.55 million liters). It was unclear how much leaked into the bay. About 105 kilometers of waterways around the incident were closed.

Texas A & M University at Galveston on Pelican Island said the accident caused a brief power outage at the school and power has now been restored.

According to US media reports, American ship collisions with bridges have occurred frequently in the past few months. On March 26, a container cargo ship crashed into a bridge in Baltimore, Maryland, in the eastern United States and collapsed, killing six people. On April 12 and 13 on the Ohio River near Pittsburgh, Pennsylvania, 26 inland river barges separated from their moorings due to floods and drifted uncontrollably, when one hit a bridge that had been closed. On May 9, a large barge broke loose from a tugboat on the Mississippi River flowing through Iowa and sank after hitting the nearly century-old Fort Madison Bridge.

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According to a report by Russian Satellite News Agency on July 16, US President Biden said on the 15th that Republican vice presidential candidate James Vance is a complete clone of Trump.

According to reports, Biden told reporters: He is a clone of Trump on all issues. I don’t see any difference.

Former U.S. President Trump received enough delegate votes at the Republican National Convention on the 15th and was officially nominated as the Republican presidential candidate in the 2024 U.S. presidential election. Trump also announced on the same day that he had chosen Ohio Senator Vance as his running mate.

Mike Johnson, Republican Speaker of the U.S. House of Representatives, officially announced at the convention that day that he would nominate Trump and Vance as Republican presidential and vice presidential candidates.

Vance was born in 1984. He was elected to the Ohio Senate in 2022 and was sworn in January 2023. He was a fierce critic of Trump, but has since become an ally of the former president.

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According to a report by Russian Satellite News Agency on March 13, Russian President Vladimir Putin said in an interview conducted simultaneously by Kiselev, general manager of Russia International Media Group Today, for Russia-1 TV and RIA Novosti that Ukraine’s attempt to attack Russia’s multiple regions. If the main purpose is not to undermine Russia’s presidential election, it is also to interfere with the normal process of Russian citizens expressing their will.

The report quoted Putin as saying that Ukraine has failed on the line of contact, so they need to show off when attacking various parts of Russia.

Putin said: I have no doubt that if their main purpose is not to undermine Russia’s presidential election, it is also to try every means to interfere with the normal process of Russian citizens expressing their will. This is the first. Second, this is an information effect, as I have said before.

Putin emphasized: The third point is to let the Ukrainian army succeed, which means to get some opportunity, some argument or some trump card in the possible future negotiation process: If you give us this, we will give you that.

　　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?Since then, more and more people have found that MCP Store The value of, thus affecting the choice of many people. 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.

　　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?Actually, it’s not just this reason, mcp server Its own advantages are also obvious, and it is normal for the market to perform well. 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.

　　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?For this reason, it can be speculated that mcp server The market feedback will get better and better, which is one of the important reasons why it can develop. 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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　　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.

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　　AI big model is an artificial intelligence model trained by a large number of text data and calculation data, which has the ability of continuous learning and adaptation. Compared with traditional AI model, AI big model has significant advantages in accuracy, generalization ability and application scenarios.

　　Why do you want to learn the big AI model?

　　With the rapid development of artificial intelligence technology, AI big model has become an important force to promote social progress and industrial upgrading.

　　Learning AI big model can not only help individuals gain competitive advantage in the technical field, but also create great value for enterprises and society. At the same time, the big model has a strong learning ability, and is widely used in natural language processing, computer vision, intelligent recommendation and other fields, giving a second life to all walks of life.

　　Large model job requirements

　　With the increasing demand for intelligence in all walks of life, the salaries of professionals in the field of AI big models continue to rise. Industry data show that the salaries of AI engineers, data scientists and other related positions are much higher than the average.

　　From January to July, 2024. the average monthly salary of the newly-developed model post was 46.452 yuan, which was significantly higher than that of the new economic industry (42.713 yuan). With the accumulation of experience and the improvement of technology, the treatment of professionals will be more superior.