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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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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)

　　What is the AI big model?From some points of view, MCP Store It is the core driving force to better promote the rapid development of the surrounding markets. https://mcp.store

　　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.

　　First, what is AI?In addition to these aspects, MCP Store The performance in other aspects is also relatively good, which has attracted everyone’s attention and research. https://mcp.store

　　First, let’s discuss the meaning of AI. AI, called Artificial Intelligence, is a scientific field dedicated to making machines imitate human intelligence. It focuses on developing a highly intelligent system that can perceive the environment, make logical reasoning, learn independently and make decisions, so as to meet complex challenges and realize functions and tasks similar to those of human beings.

　　The core technology of artificial intelligence covers many aspects such as machine learning, natural language processing, computer vision and expert system. Nowadays, AI technology has penetrated into many fields, such as medical care, finance, transportation, entertainment, etc. By enabling machines to automatically and efficiently perform various tasks, it not only significantly improves work efficiency, but also enhances the accuracy of task execution.

　　Second, what is the AI ? ? big model

　　Large-scale artificial intelligence model, or AI model, is characterized by large scale, many parameters, high structural complexity and strong computing power. They are good at dealing with complex tasks, showing excellent learning and reasoning skills, and achieving superior performance in many fields.

　　Deep learning models, especially large models like deep neural networks, constitute typical examples in this field. Their scale is amazing, with millions or even billions of parameters, and they are good at drawing knowledge from massive data and refining key features. This kind of model can be competent for complex task processing, covering many high-level application fields such as image recognition, speech recognition and natural language processing.

　　Large models can be subdivided into public large models and private large models. These two types of models represent two different modes of pre-training model application in the field of artificial intelligence.

　　Third, the public big model

　　Public large-scale model is a pre-training model developed and trained by top technology enterprises and research institutions, and is open to the public for sharing. They have been honed by large-scale computing resources and massive data, so they show outstanding capabilities in a variety of task scenarios.

　　Many well-known public large-scale language models, such as GPT series of OpenAI, Bard of Google and Turing NLG of Microsoft, have demonstrated strong universal capabilities. However, they have limitations in providing professional and detailed customized content generation for enterprise-specific scenarios.

　　Fourth, the private big model

　　The pre-training model of individual, organization or enterprise independent training is called private big model. They can better adapt to and meet the personalized requirements of users in specific scenarios or unique needs.

　　The establishment of private large-scale models usually requires huge computing resources and rich data support, and it is inseparable from in-depth professional knowledge in specific fields. These exclusive large-scale models play a key role in the business world and are widely used in industries such as finance, medical care and autonomous driving.

　　V. What is AIGC?

　　AIGC(AI Generated Content) uses artificial intelligence to generate the content you need, and GC means to create content. Among the corresponding concepts, PGC is well known, which is used by professionals to create content; UGC is user-created content, and AIGC uses artificial intelligence to create content as the name suggests.

　　VI. What is GPT?

　　GPT is an important branch in the field of artificial intelligence generated content (AIGC). Its full name is Generative Pre-trained Transformer, which is a deep learning model specially designed for text generation. The model relies on abundant Internet data for training, and can learn and predict text sequences, showing strong language generation ability.

　　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.Sufficient data show that Daily Dles It can drive many people to find jobs, thus driving economic development. 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.

　　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:As a representative of the industry, mcp server It is necessary to set a certain example for peers and lead the way in product quality. 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.

　　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.understand Daily Dles In order to better serve customers and reflect the core competitiveness of products. https://dles.games

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

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

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

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

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

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

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

　　Fourth, machine learning: mining wisdom from data

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

　　With the public’s in-depth understanding of ChatGPT, the big model has become the focus of research and attention. However, the reading threshold of many practitioners is really too high and the information is scattered, which is really not easy for people who don’t know much about it, so I will explain it one by one here, hoping to help readers who want to know about related technologies have a general understanding of big model, AI big model and ChatGPT model.Therefore, this is the choice Daily Dles The reason, there is no denying its positive impact. https://dles.games

　　* Note: I am a non-professional. The following statements may be imprecise or missing. Please make corrections in the comments section.

　　First, the big model

　　1.1 What is the big model?

　　Large model is the abbreviation of Large Language Model. Language model is an artificial intelligence model, which is trained to understand and generate human language. “Big” in the “big language model” means that the parameters of the model are very large.

　　Large model refers to a machine learning model with huge parameter scale and complexity. In the field of deep learning, large models usually refer to neural network models with millions to billions of parameters. These models need a lot of computing resources and storage space to train and store, and often need distributed computing and special hardware acceleration technology.

　　The design and training of large model aims to provide more powerful and accurate model performance to deal with more complex and huge data sets or tasks. Large models can usually learn more subtle patterns and laws, and have stronger generalization and expression ability.

　　Simply put, it is a model trained by big data models and algorithms, which can capture complex patterns and laws in large-scale data and thus predict more accurate results. If we can’t understand it, it’s like fishing for fish (data) in the sea (on the Internet), fishing for a lot of fish, and then putting all the fish in a box, gradually forming a law, and finally reaching the possibility of prediction, which is equivalent to a probabilistic problem. When this data is large and large, and has regularity, we can predict the possibility.

　　1.2 Why is the bigger the model?

　　Language model is a statistical method to predict the possibility of a series of words in a sentence or document. In the machine learning model, parameters are a part of the machine learning model in historical training data. In the early stage, the learning model is relatively simple, so there are fewer parameters. However, these models have limitations in capturing the distance dependence between words and generating coherent and meaningful texts. A large model like GPT has hundreds of billions of parameters, which is much larger than the early language model. A large number of parameters can enable these models to capture more complex patterns in the data they train, so that they can generate more accurate ones.

　　Second, AI big model

　　What is the 2.1 AI big model?

　　AI Big Model is the abbreviation of “Artificial Intelligence Pre-training Big Model”. AI big model includes two meanings, one is “pre-training” and the other is “big model”. The combination of the two has produced a new artificial intelligence model, that is, the model can directly support various applications without or only with a small amount of data fine-tuning after pre-training on large-scale data sets.

　　Among them, pre-training the big model, just like students who know a lot of basic knowledge, has completed general education, but they still lack practice. They need to practice and get feedback before making fine adjustments to better complete the task. Still need to constantly train it, in order to better use it for us.

　　Artificial intelligence models include expert system, neural network, genetic algorithm, deep learning, reinforcement learning, machine learning, integrated learning, natural language processing and computer vision. ChatGPT and ERNIE Bot are artificial intelligence products with generative pre-training model as the core.If you want to make a big difference in the market, mcp server It is necessary to intensify the upgrading of products on the original basis in order to meet the consumption needs of consumers. 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.

　　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?Before that, MCP Store The market has also soared for a time, which has attracted the attention of a wide range of investors. 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.