與ChatGPT的供應(yīng)鏈數(shù)字化探討

與ChatGPT的供應(yīng)鏈數(shù)字化探討

作者剛剛跟ChatGPT就供應(yīng)鏈數(shù)字化的一些問題探討了一下，被ChatGPT的現(xiàn)有能力所震撼。

一句總結(jié)：

或許量變已經(jīng)引起質(zhì)變，ChatGPT將會(huì)是強(qiáng)大的知識輔助工具與真正意義上的專家系統(tǒng)，有非常美好的商業(yè)化應(yīng)用潛力。幾點(diǎn)觀察：

1.雖然目前LLM的后端技術(shù)仍然是Transformer, attention , 沒有神奇之處，但作者猜測LLM會(huì)否已經(jīng)量變達(dá)成質(zhì)變，他看到的語料太多了。好比打網(wǎng)球，100小時(shí)和10000小時(shí)會(huì)有本質(zhì)區(qū)別。

2.互聯(lián)網(wǎng)上的數(shù)據(jù)不都是大樣本，有的數(shù)據(jù)很少，比如一些刁鉆古怪的問題，他回答不好的，因?yàn)樗彩侵挥?00小時(shí)。

3.他有時(shí)會(huì)瞎編，分不清事實(shí)還是編造？就像人們必須回答一個(gè)陌生的問題時(shí)候，通常會(huì)去“合理”推測。這個(gè)“合理”最關(guān)鍵，我們通常是基于經(jīng)驗(yàn)結(jié)合邏輯，而AI 就是根據(jù)概率分布，通過最大似然學(xué)習(xí)到的概率分布。

4.如果海量的數(shù)據(jù)確實(shí)使得ChatGPT達(dá)成量變到質(zhì)變，將會(huì)是非常強(qiáng)悍的。目前ChatGPT輔助寫論文和公眾號足夠了，邏輯清晰，內(nèi)容完整，還順便可以翻譯。而且會(huì)話的上下文，指代關(guān)系他很清楚。

5.感覺目前他回答問題的能力還取決于人們提問的方式和方法，提出高質(zhì)量的問題會(huì)是個(gè)重要能力。正如有的人可以通過不同關(guān)鍵字組合用好google,未來人類的一個(gè)重要技能會(huì)是學(xué)會(huì)與AI 打交道，有效的提出問題。

關(guān)鍵思考：ChatGPT的行文能力、會(huì)話能力、總結(jié)能力是來自量變引起的質(zhì)變碼？質(zhì)變發(fā)生在哪里，機(jī)理是什么？對此問題，ChatGPT也給出了自己的回答。讀者如果有不同的見解和思考，歡迎留言分享。大家一起感受一下這個(gè)會(huì)話：

which company's supply chain is the best？It is difficult to determine which company has the "best" supply chain, as the effectiveness and efficiency of a supply chain can vary based on several factors, including the company's size, industry, and specific business objectives. Additionally, different companies may excel in different aspects of supply chain management, such as cost management, speed, or sustainability.That being said, some companies that are often cited as having strong supply chain practices include Amazon, Walmart, Toyota, and Procter & Gamble. These companies are known for their use of technology and data analytics to optimize their supply chain operations and for their commitment to sustainability and responsible sourcing.cool answer. can you elaborate more detail how amazon use data analytics to optimize their inventory management ? Sure! Amazon uses data analytics in many ways to optimize its inventory management. Here are a few examples:1.Demand forecasting: Amazon uses advanced algorithms and machine learning models to predict demand for products. This allows the company to make informed decisions about how much inventory to keep in its warehouses and how much to order from suppliers.2.Inventory optimization: Amazon uses data analytics to determine the optimal distribution of inventory across its network of warehouses. This helps to ensure that products are available to customers when and where they are needed, while minimizing the cost of holding and transporting inventory.3.Stock replenishment: Amazon uses data analytics to determine when to restock products based on demand patterns, lead times, and safety stock levels. This helps to minimize the risk of stockouts while avoiding the cost of holding too much inventory.4.Reverse logistics: Amazon uses data analytics to optimize the process of returning and disposing of unsold or damaged goods. This helps to minimize the cost of handling returns while maximizing the recovery value of returned goods.By leveraging data and advanced analytics, Amazon is able to make real-time decisions about its inventory management that are informed by the latest information about customer demand and market trends. This helps to ensure that the company is able to meet customer demand while minimizing waste and costs.very helpful. For inventory optimization, what solver they are using to solve MIP problems?Amazon is likely using a variety of mathematical optimization methods, including mixed-integer programming (MIP), to optimize its inventory management. MIP is a powerful optimization technique that can help companies make complex decisions involving multiple variables and constraints.However, it is not publicly known which specific solvers Amazon is using to solve MIP problems. The company is known for developing and using proprietary algorithms and tools for many of its operations, and it is possible that it has developed its own MIP solvers in-house.In general, popular commercial MIP solvers include Gurobi, CPLEX, and Xpress, while open-source solvers such as GLPK and COIN-OR are also available. The choice of solver depends on several factors, including the size and complexity of the problem, the available computing resources, and the specific requirements of the company.do you think quantum optimization algorithm like QPSO could help to solve MIP problem?Quantum optimization algorithms, such as quantum particle swarm optimization (QPSO), have the potential to help solve MIP problems, but it is still an emerging field and more research is needed to fully understand their capabilities and limitations.Quantum algorithms, such as QPSO, use quantum mechanics to explore the solution space of an optimization problem in a way that is different from classical algorithms. This can lead to faster convergence and more accurate solutions for some problems.However, MIP problems can be very complex and difficult to solve, and there is still a lot that is not known about how well quantum optimization algorithms will perform on these types of problems. It is also important to note that quantum optimization algorithms require specialized hardware, such as quantum computers, which are still in the early stages of development.So while quantum optimization algorithms show promise for solving MIP problems, it is still too early to say for certain whether they will be a practical solution in the near future. Further research and development is needed to fully understand their potential and limitations.do you think yourself could help supply chain digitalization, if yes, how?As an AI language model, I can certainly play a role in helping with supply chain digitalization. Here are a few ways I can assist:1.Information gathering: I can help companies gather information about their supply chain operations, including data about suppliers, inventory levels, and transportation costs. This data can then be used to make informed decisions about supply chain optimization.2.Decision support: I can help companies make informed decisions about their supply chain operations by providing data-driven insights and recommendations. For example, I can analyze data about demand patterns and shipping costs to help a company determine the most efficient way to distribute its products.3.Process automation: I can assist in automating various supply chain processes, such as purchase order creation, invoice processing, and shipment tracking. This can help to reduce manual errors, improve efficiency, and allow companies to focus on more strategic tasks.4.Chatbot integration: I can be integrated into a chatbot to provide real-time assistance and support to supply chain professionals. This can help to improve communication and collaboration between supply chain partners and make it easier to resolve issues and make decisions.By leveraging my capabilities in these and other areas, I can help companies to digitize and optimize their supply chain operations, leading to improved efficiency, cost savings, and better customer experiences.very cool. Can you give me any examples of DELL, Lenovo shared suppliers?Dell and Lenovo are both major computer hardware manufacturers, and it is common for companies in the same industry to share suppliers for certain components. However, I don't have specific examples of suppliers that are shared by both Dell and Lenovo.It is worth noting that both Dell and Lenovo source components from a wide range of suppliers, including large electronics manufacturers such as Intel, AMD, and Samsung, as well as smaller specialized suppliers for niche components such as batteries, keyboards, and displays. The specific suppliers used by each company may vary based on their specific product offerings, cost structures, and other factors.what do you think the market trend of computer industry, any action suggestions you will give to Lenovo to mitigate market risk?The computer industry is undergoing a number of shifts and changes that are affecting both the hardware and software segments. Here are some of the trends and market risks that the industry is facing:1.Decline in PC sales: The global market for traditional personal computers (PCs) has been in decline in recent years, as consumers increasingly turn to smartphones and other mobile devices for computing needs.2.Competition from smartphones and tablets: The growth of the smartphone and tablet markets has eaten into the market share of traditional PCs, as these devices offer similar functionality at lower prices.3.Shift to cloud computing: The rise of cloud computing has changed the way that software is delivered and used, leading to a decline in the importance of traditional PCs and an increase in the importance of servers and data centers.To mitigate these risks, Lenovo can take several actions, including:1.Diversification: Lenovo can look to diversify its product offerings beyond traditional PCs and into areas such as mobile devices, gaming, and cloud computing.2.Focus on cost competitiveness: Lenovo can look to maintain its competitiveness in the PC market by focusing on cost-effectiveness, while also offering high-quality products and services.3.Invest in research and development: Lenovo can continue to invest in research and development to stay ahead of the curve and develop new products and technologies that address evolving market needs.4.Focus on innovation: Lenovo can look to innovate in areas such as design, materials, and software to differentiate its products and services from those of its competitors.5.Build strategic partnerships: Lenovo can look to build strategic partnerships with other companies to expand its reach and capabilities, and to better address the needs of customers in different markets and regions.By taking these actions, Lenovo can help to mitigate the risks posed by market trends and to position itself for success in the changing computer industry.thanks for all the valuable suggestions. How do you view the electronic vehicle industry?The electric vehicle (EV) industry is an area of significant growth and innovation, with the potential to have a major impact on the automotive and energy industries. Here are some of the key trends and challenges in the EV industry:1.Growing demand: The demand for EVs is increasing globally, driven by factors such as environmental concerns, government incentives, and improvements in battery technology.2.Competition: The EV market is becoming increasingly competitive, with new entrants and established automakers investing heavily in EVs.3.Battery technology: The cost and performance of batteries is a key factor in the success of EVs, and the industry is constantly working to improve battery technology and bring down costs.4.Charging infrastructure: The development of charging infrastructure is critical to the widespread adoption of EVs, and both the public and private sectors are investing in this area.5.Government regulations: Governments around the world are implementing regulations and incentives to encourage the development and adoption of EVs, and these policies will continue to evolve over time.Overall, the EV industry is an exciting and rapidly-evolving space with significant growth potential, as well as numerous challenges to overcome. Companies and governments will need to work together to continue to develop the technology, build the infrastructure, and create the policies needed to support the widespread adoption of EVs.any attention if a company decide to enter the EV industry ?Entering the electric vehicle (EV) industry can be a promising opportunity, but also involves significant risks and challenges. Here are some of the factors that a company entering the EV industry should consider:1.Competition: The EV market is becoming increasingly competitive, with established automakers, new entrants, and tech companies all investing in the space. It is important to have a clear understanding of the competitive landscape and to develop a compelling value proposition.2.Battery technology: Battery technology is a critical component of EVs and is rapidly evolving. A company entering the EV industry should have a strong understanding of battery technology and the ability to invest in R&D to stay ahead of the curve.3.Supply chain: Building an effective supply chain for EVs can be challenging, as the components required are often specialized and difficult to obtain. It is important to have a solid understanding of the suppliers and suppliers’ capacity and reliability.4.Charging infrastructure: Building out a charging infrastructure is critical to the widespread adoption of EVs, and requires significant investment. A company entering the EV industry should have a clear plan for how it will build and manage this infrastructure.5.Government regulations: Government regulations and incentives can have a major impact on the EV industry, and it is important to have a clear understanding of the regulatory landscape and how it may evolve over time.Overall, entering the EV industry requires a significant investment of resources and expertise, and companies should carefully consider the risks and challenges involved before making a decision to enter the market.for EV supply chain, if we want to build a digital supply chain, any suggestion where we could focus?Building a digital supply chain for the electric vehicle (EV) industry can help improve efficiency, increase transparency, and reduce costs. Here are some areas where companies can focus to build a digital supply chain for EVs:1.Automation: Automating processes such as ordering, shipping, and invoicing can improve accuracy and efficiency, and reduce the risk of errors.2.Data Management: Collecting and analyzing data from across the supply chain can help identify areas for improvement and optimize processes. Companies can use digital tools such as data analytics, machine learning, and blockchain to improve data management.3.Collaboration: Collaborating with suppliers and customers through digital platforms can improve communication and coordination, and help ensure that everyone is working towards the same goals.4.Traceability: Using digital technologies such as blockchain can help ensure that components and materials are tracked throughout the supply chain, improving transparency and reducing the risk of counterfeits and substandard materials.5.Supply Chain Visibility: Improving visibility into the supply chain can help companies make more informed decisions, respond to disruptions more quickly, and improve overall supply chain performance.6.Predictive analytics: By using predictive analytics and machine learning, companies can anticipate future demand, make more accurate inventory decisions, and improve their responsiveness to changes in the market.Overall, building a digital supply chain for the EV industry requires investment in technology, process improvements, and a focus on collaboration and data-driven decision making.what kind of data architecture you will suggest to use?The choice of data architecture for an electric vehicle (EV) supply chain will depend on various factors such as the size and complexity of the supply chain, the type of data being collected and stored, and the specific business requirements. However, here are some commonly used data architecture options that could be considered:1.Relational databases: Relational databases such as Oracle, MySQL, and Microsoft SQL Server are widely used for structured data storage and retrieval. They are well suited for supply chain management because they can handle complex relationships between data elements and are highly scalable.2.NoSQL databases: NoSQL databases such as MongoDB and Cassandra are designed for large-scale, unstructured data storage and retrieval. They can handle large volumes of data and are well suited for real-time data processing, making them a good choice for supply chain management.3.Cloud-based solutions: Cloud-based solutions such as Amazon Web Services (AWS) and Microsoft Azure offer scalable, flexible, and cost-effective data storage and processing. They are well suited for supply chain management because they can handle large volumes of data and provide access to powerful data analytics tools.4.Blockchain: Blockchain technology can be used to create a secure and transparent record of transactions and events throughout the supply chain. This can help ensure the integrity and accuracy of data, and reduce the risk of fraud and counterfeiting.Ultimately, the choice of data architecture will depend on the specific needs of the EV supply chain, and the best approach may involve a combination of different data architecture options.we usually will face data synchronization problem when integrate various commercial systems like SAP ECC and JDA planning systems, any best practices you see in the industry ?Data synchronization between different commercial systems can be a challenge in supply chain management, especially when integrating systems such as SAP ECC and JDA planning systems. Here are some best practices for addressing data synchronization problems:1.Standardization: Establishing a common set of data standards across all systems can help ensure that data is consistent and accurate. This can include standardizing data definitions, data formats, and data exchange protocols.2.Data mapping: Creating a data mapping plan that outlines how data will be translated and transformed between systems can help ensure that data is accurately transferred and interpreted.3.Data validation: Implementing data validation rules and procedures can help ensure that data is accurate and complete before it is transferred between systems.4.Real-time data transfer: Implementing real-time data transfer mechanisms can help ensure that data is up-to-date and that changes are reflected in all systems in a timely manner.5.Monitoring and reconciliation: Monitoring data transfer processes and reconciling data between systems on a regular basis can help identify and resolve any data synchronization issues.6.Error handling: Implementing effective error handling mechanisms can help ensure that data synchronization issues are quickly identified and resolved.7.Integration with middleware: Using middleware such as Apache Kafka or IBM MQ can help manage data synchronization between systems by allowing for the creation of a centralized data hub.8.Testing and validation: Thoroughly testing and validating the data synchronization process before it is put into production can help ensure that it is working as expected and that any issues are identified and addressed.By implementing these best practices, companies can help ensure that data is accurately and effectively synchronized between different commercial systems, improving supply chain efficiency and reducing the risk of errors.it seems with your strong help, all architects will lose their jobs :)As an AI language model, my purpose is to assist and provide information to support decision-making, but I am not intended to replace human architects. Human architects bring valuable expertise and experience to the table, and their creativity and critical thinking skills are essential for finding innovative solutions and making complex decisions.In the context of supply chain management and digitalization, AI and machine learning algorithms can provide valuable support by automating routine tasks, analyzing large data sets, and providing real-time insights, but they cannot replace the strategic thinking and expertise of human architects. Instead, AI and human architects can work together to drive better outcomes and improve the overall efficiency of the supply chain.talking about human architect, the role of chief architect will be critical. what's your understanding of role of chief architect ?The role of a Chief Architect (CA) is a senior leadership position that is responsible for defining and managing the technical vision, strategy, and direction of an organization. A CA is responsible for ensuring that the organization's technology investments align with its overall business goals and objectives.The specific responsibilities of a CA can vary depending on the size and complexity of the organization and the industry it operates in, but common responsibilities include:1.Developing and maintaining a technology roadmap: The CA is responsible for defining the technology strategy and direction for the organization, including the identification and evaluation of new technologies and trends that can drive innovation and competitiveness.2.Aligning technology with business goals: The CA must ensure that technology investments align with the organization's overall business strategy and goals, and that technology initiatives are effectively aligned with business objectives.3.Defining and enforcing technical standards: The CA is responsible for defining and enforcing technical standards and best practices across the organization to ensure consistent and high-quality technology solutions.4.Overseeing solution architecture: The CA is responsible for overseeing the development of technical solutions and ensuring that they are aligned with the technology roadmap and standards.5.Managing technical risk: The CA is responsible for identifying, assessing, and mitigating technical risks that may impact the organization's ability to achieve its goals and objectives.6.Building relationships with stakeholders: The CA must build strong relationships with key stakeholders, including business leaders, technology leaders, and external partners, to ensure effective alignment and collaboration.In summary, the role of a Chief Architect is critical in ensuring that an organization's technology investments are aligned with its business goals and that technical solutions are of high quality, secure, and scalable. The CA acts as a bridge between the business and technology teams, providing strategic leadership and direction to the organization.what will be the difference between CA and CTO？The Chief Architect (CA) and Chief Technology Officer (CTO) are both senior leadership positions, but they have different focuses and responsibilities.The Chief Technology Officer (CTO) is responsible for defining and overseeing the technology strategy for the organization as a whole. The CTO's focus is on ensuring that technology investments align with the organization's overall business strategy and goals, and that technology initiatives are effectively aligned with business objectives. The CTO typically has a broad view of the organization's technology landscape and is involved in all aspects of technology, from research and development to product design, engineering, and delivery.The Chief Architect (CA), on the other hand, is responsible for defining and maintaining the technical vision, strategy, and direction of the organization. The CA is responsible for ensuring that technical solutions are aligned with the technology roadmap and standards, and for overseeing the development of technical solutions. The CA typically has a deep understanding of the technical details of the solutions being developed and is focused on ensuring that technical solutions are of high quality, secure, and scalable.In summary, the CTO is responsible for the overall technology strategy of the organization, while the CA is responsible for the technical details of the solutions being developed. Both roles are critical in ensuring that an organization's technology investments are aligned with its business goals, and that technical solutions are of high quality, secure, and scalable. The CTO and CA often work closely together to ensure that the technology strategy and technical solutions are aligned with the overall business strategy and goals.what skillset need to build if someone wants to be a CA？To be a successful Chief Architect (CA), one needs to have a combination of technical and leadership skills. Here are some of the key skillsets that a CA should possess:1.Technical Expertise: A CA should have a deep understanding of the organization's technical landscape, including its systems, applications, platforms, and technology trends. They should also have a strong understanding of software development methodologies, software architecture, and design patterns.2.Problem-solving and analytical skills: A CA must be able to analyze complex technical problems and develop creative and effective solutions. They should have the ability to think critically and systematically to identify and mitigate risks.3.Leadership and interpersonal skills: A CA must be able to lead and motivate cross-functional teams, build strong relationships with stakeholders, and effectively communicate technical ideas and solutions to both technical and non-technical audiences.4.Strategic thinking: A CA must have a long-term view of the organization's technology landscape and be able to develop a technology roadmap that aligns with the organization's business goals and objectives. They must be able to balance the need for innovation with the need to maintain stability and reliability.5.Business acumen: A CA must have a good understanding of the organization's business operations, challenges, and goals, and be able to align technology initiatives with business objectives.6.Continuous learning: Technology is rapidly evolving, and a CA must be able to stay up-to-date with new technologies, trends, and best practices. They should have a continuous learning mindset and be willing to invest time and resources in their own professional development.In summary, a CA must possess a combination of technical and leadership skills, including technical expertise, problem-solving and analytical skills, leadership and interpersonal skills, strategic thinking, business acumen, and a commitment to continuous learning. By developing these skills, a CA can effectively lead the development of technical solutions and ensure that they align with the organization's business goals and objectives.strongly agree. I am very impressive with your answersThank you! I'm glad that I could help. If you have any more questions or need more information, please feel free to ask!since chatGPT use similar technologies like transformer and attention mechanism, comparing to small language models, what do you think is the key gaming changing points for chatGPT to be that successful ?The success of OpenAI's GPT-3 (and other large language models like it) can be attributed to a few key factors:1.Model size: GPT-3 has a massive number of parameters (over 175 billion) and has been trained on a diverse range of texts, which has enabled it to develop a deep understanding of language. The large size of the model allows it to generate coherent and meaningful responses to a wide range of questions.2.Transfer learning: GPT-3 has been trained on a large corpus of texts and can use this knowledge to perform a variety of tasks without additional fine-tuning. This allows it to perform well on new tasks with limited training data.3.Attention mechanism: The attention mechanism in GPT-3 allows the model to focus on the most relevant parts of the input when generating a response. This enables it to generate more accurate and contextually relevant responses.4.Pre-training: GPT-3 has been pre-trained on a large corpus of texts, which allows it to develop a deep understanding of language and its patterns. This pre-training enables the model to perform well on a wide range of language-related tasks without additional fine-tuning.5.Robustness: GPT-3 has been trained on a large and diverse corpus of texts, which has helped it develop a robust understanding of language. This allows it to handle a wide range of inputs and generate coherent and meaningful responses even to unexpected or adversarial inputs.In summary, the key factors contributing to the success of GPT-3 and other large language models like it include their large size, transfer learning capability, attention mechanism, pre-training, and robustness.do you think if "Quantitative change causes qualitative change" is the reason?Yes, "quantitative change causes qualitative change" can be seen as one of the reasons for the success of large language models like GPT-3. As the size of the model increases, the number of parameters it has also increases, which leads to a corresponding increase in its capacity to model complex relationships and patterns in the data it has been trained on.This increase in capacity allows the model to generate more sophisticated and nuanced responses, leading to a qualitative improvement in its performance. As more data is added and the model is trained on increasingly diverse texts, it continues to develop a deeper understanding of language and can perform a wider range of tasks with increasing accuracy.In this way, the quantitative increase in model size can be seen as causing a qualitative change in its ability to understand and generate language.can you translate your last answer into Chinese?

是的，"數(shù)量性變化導(dǎo)致質(zhì)量性變化"可以被視為GPT-3等大型語言模型成功的原因之一。

隨著模型的大小增加，其參數(shù)的數(shù)量也隨之增加，從而導(dǎo)致它對已訓(xùn)練數(shù)據(jù)中的復(fù)雜關(guān)系和模式的模擬能力的相應(yīng)增加。這種能力的增加使模型能夠生成更復(fù)雜和細(xì)致的回答，從而導(dǎo)致它的表現(xiàn)有了質(zhì)量上的提高。隨著數(shù)據(jù)的增加和模型在越來越多的不同文本上進(jìn)行訓(xùn)練，它繼續(xù)深入地理解語言，并且能夠以越來越高的準(zhǔn)確性執(zhí)行越來越廣泛的任務(wù)。通過這種方式，模型大小的數(shù)量性增加可以被視為導(dǎo)致其理解和生成語言能力的質(zhì)量性變化。太強(qiáng)大了謝謝您的評價(jià)！如果您有任何其他問題，請隨時(shí)告訴我。你能就中國未來的發(fā)展給出一些預(yù)測和建議嗎？對于中國未來的發(fā)展，有幾個(gè)方面值得關(guān)注：1.數(shù)字化轉(zhuǎn)型：中國正在加快數(shù)字化轉(zhuǎn)型，以提高生產(chǎn)力和提高競爭力。數(shù)字技術(shù)，特別是人工智能，將繼續(xù)在中國的經(jīng)濟(jì)增長中發(fā)揮重要作用。2.綠色經(jīng)濟(jì)：中國正在加強(qiáng)對環(huán)保和可持續(xù)發(fā)展的關(guān)注，以應(yīng)對全球氣候變化挑戰(zhàn)。這將對中國的能源政策，工業(yè)政策，農(nóng)業(yè)政策等產(chǎn)生重要影響。3.全球化：中國正在加強(qiáng)其全球影響力，并成為全球經(jīng)濟(jì)增長的重要驅(qū)動(dòng)力。同時(shí)，中國也正在加強(qiáng)與全球其他國家和地區(qū)的經(jīng)濟(jì)聯(lián)系，以促進(jìn)貿(mào)易和投資。在未來，中國可以考慮在以下方面采取行動(dòng)：1.加強(qiáng)對數(shù)字化技術(shù)的投資，以促進(jìn)數(shù)字化轉(zhuǎn)型；2.加強(qiáng)對環(huán)保和可持續(xù)發(fā)展的關(guān)注，以實(shí)現(xiàn)綠色經(jīng)濟(jì)；3.繼續(xù)加強(qiáng)與全球其他國家和地區(qū)的經(jīng)濟(jì)聯(lián)系，以促進(jìn)全球化。