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Meaning of artificial intelligence
Artificial intelligence (AI) is a field in computer science dealing with the study and design of systems that exhibit intelligent behavior. An artificial intelligence system is a machine that perceives its environment and takes appropriate action. It is the intelligence exhibited by machines, which may be either narrow or general or even super AI.
It uses some form of learning to improve its performance. AI can be used in a number of ways. For example, a smart chatbot could use AI to answer questions, or a self-driving car can use it to decide which path to take.
Machine learning (ML) is an area of artificial intelligence that deals with the design of algorithms that can learn from and make predictions about data. ML is often used in the context of data analytics, but it is also applicable to areas like natural language processing, robotics, medicine, finance, and computer vision.
The impact of AI on economic growth and international trade
Artificial intelligence will have a significant positive impact on global GDP and trade in the next decades. This includes the development of more efficient technology, more productive labor force, and improved management of the global value chain.
Business leaders are investing heavily in artificial intelligence (AI) technologies. This is a good investment, but it requires time to incorporate and make effective use of these technologies in a way that requires significant investment, access to skilled people, and a transformation in business practices.
AI is likely to accelerate the replacement of blue-collar workers in the manufacturing sector due to advancement of automation. At the same time, AI may be used to automate tasks and processes so that a much wider range of people are able to work more efficiently, and that jobs in low skill, low-wage positions disappear.
We’re also seeing the need for different worker skills as AI is deployed to add value to products and production.
Through AI, automation will further expand the share of services in production as well as international trade, accelerating the transition toward services based economies.
AI applications to international trade
AI and Supply Chains
Global value chains, otherwise known as supply chains, are complex networks of actors that form links between the production of a product or service and its sale. Global value chains have been growing in complexity and breadth over time, in response to changing consumer preferences, technological developments, and political shifts. In many cases, this complexity has created problems, as it is difficult to predict how products and services are created and sold, especially when their production involves many different locations and firms.
Predicting The Future
AI is being used to predict the future and manage the logistics of global supply chains. It is using data analytics and machine learning to develop ways to forecast demand and supply and to automate processes in the logistics industry. With the rise of the internet and other data sources, it is also used to track consumer behavior and improve retail operations. Robotics can be used to automate warehouse operations and inventory management.
International Trade negotiations
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AI could help improve outcomes in international trade negotiations. AI could be used to help analyze how each party's interests change if a particular scenario plays out differently. For example, imagine that the United States and China are engaged in a trade dispute. The U.S. government might try to negotiate with China to see if the country would be willing to make concessions. But it is also possible that China may hold out for higher tariffs on American products, which will harm its economy and hurt its citizens. If this scenario plays out, the U.S. government might want to use AI to assess whether or not it is better to give up on the trade dispute.
Brazil has a well-established program to help its companies become more competitive on the world stage. Its trade initiatives include the Brazilian Government’s use of AI to improve trade negotiations.
Enhancing Business Processes
With the growth of technology, businesses have started to implement AI into their operations, to handle the growing complexities of global value chain. Some companies use AI to track consumer trends, to predict customer behavior, to determine supply chain problems, to help with the development of new products, to improve manufacturing processes, to detect fraud, to assist in the collection and analysis of data, and much more.
Specifically, AI is being used to enhance the decision making process of organizations. AI can be used in multiple ways such as forecasting, logistics, retail and banking, and others. Artificial intelligence can help organizations to identify the right products or services to sell, to reduce operating costs, to find new customers and to identify the best way to deliver the right products and services to the right location. AI can help organizations to predict the future trends and market changes. AI can also help organizations to reduce risks along the supply chain. It is already being used to manage the warehouses. AI can also help to better manage the stocks and inventories. It can be used to predict the demand of goods or services and to plan and optimize the production of goods or services. For example, AI can help businesses to better manage the delivery of goods and services to the right location.
Smart manufacturing uses sensors and connected machines, materials, and supplies to make predictive, self-repair, and quick-adaptation possible.
As IoT continues to grow, many companies are integrating their devices into manufacturing processes. The connected product is becoming increasingly intelligent, and we’ll continue to see these technologies permeate the industry.
The U.S. Government is working on an initiative called the Manufacturing Extension Partnership (MEP), which aims to bring together small and medium-sized manufacturers in a collaborative network to help them grow and innovate. It’s an idea that resonates with the smart manufacturing vision of making factories more flexible and adaptable to changing market conditions.
AI is used in digital platforms
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With the help of AI, eBay has developed new ways to help small business owners around the world to grow their businesses and sell their products online. One of the biggest innovations is the eBay Global Trade Manager. This allows sellers to access eBay’s network of more than 250 marketplaces, helping them to get more buyers from different parts of the world. This innovation has allowed eBay to connect buyers and sellers globally.
eBay uses AI to track the data of millions of buyers and sellers. They use these data to build an algorithm that provides accurate estimates for each product on the platform. These estimates are used to help sellers find potential customers and identify the best prices for their items. eBay is also using AI to predict which auctions might result in the highest sales, allowing them to move auctions forward quickly. They are also using AI to match buyers with sellers based on the location of both parties.
eBay is also using AI to automatically find similar items for sellers. They are able to scan more than five million products and suggest similar items. This helps small business owners to be able to sell their products at higher prices. They are also using AI to monitor eBay stores and alert sellers when inventory falls below minimum levels. The company claims that they use this technology to protect the livelihoods of thousands of small business sellers across the world.
Amazon, eBay, and Google are all using AI to enhance their customer experience.
Challenges facing AI development
Interdependent Technologies
Digital technologies do not function in isolation. They depend on each other. AI is no exception. It relies on digital technologies like, among others, big data, internet of things and cloud computing. These technologies need global data flow to function effectively. Data localization laws put in place by some countries impose constraints on this data flow impacting the unfettered development of AI.
Access to global data
One challenge that exists in AI research is access to large amounts of global data. We need to build algorithms that can generalize well, that are scalable, and that can work across multiple domains. Take the example of one specific domain: medical images. Medical images are a very complex type of data, since they are both highly structured (they have lots of different features), and very high-dimensional (the images themselves are usually 3D). The main problem with medical images is that we don’t have access to enough of them, sourced from beyond the boundaries of a nation, to train a deep learning algorithm from scratch.
Privacy and AI
Governments around the world are increasingly trying to reduce the flow of personal data across borders by imposing new privacy standards, which is known as “data localization”.
The EU General Data Protection Regulation (GDPR) is a set of regulations that protects the personal information of EU citizens and residents. They prohibit the transfer of their personal data to countries that haven’t been approved by the European Commission. With the new GDPR rules, we might see a slowdown in the development of artificial intelligence capabilities.
Under the GDPR, personal data can only be used for the purpose for which it was collected. Personal data collected as part of a transaction cannot then be used for any other purpose including training AI.
Balancing the need for privacy protection and the need for AI learning will be the key challenge of the policy makers in the future.
Access to source code
Many AI researchers believe that access to the source code of machine learning algorithms is necessary for reproducibility and transparency of AI research. However, many companies and governments have policies preventing the release of source code. There are many reasons for restricting the release of AI source code. Some companies may believe that it could give competitors an advantage, while other governments may believe that releasing the code could lead to the discovery of security vulnerabilities. Regardless they have an adverse effect on free international trade and the diffusion of AI globally.
Impact of AI On Industrial Standards
We already seeing the impact of AI on industrial standards, and we can expect to see AI’s influence on standards even greater as the technology matures. Differing industrial standards across the countries stand in the way of foreign manufacturers as they now have to retool their machines in order to export.
Impact of Tariffs on AI
Tariffs on trade can impact access to goods needed for the development of AI, which in turn could impact negatively the development and diffusion of AI globally and international trade.
Copyright protection and AI
Copyrights laws are not standardized across the countries. As training data is often copied and edited for use, this could well result in infringement of copyrights. Fair use exceptions to copyright may give protection is some instances but fair use exceptions themselves have no globally accepted rules and in some countries they don't even exist. This lack of uniformity in law creates uncertainty and would create barriers to unfettered deployment of AI globally and international trade.
Conclusion
As AI develops from narrow AI to general AI and possibly to super AI, its footprint on international trade would only get larger and larger. These developments are bound create newer opportunities and challenges, foreseeable and unforeseeable. Whatever they may be we can expect governments and the businesses to seize the opportunities and meet the challenges presented, as they have always done.