When Britain became the first country to reach a trade agreement with President Trump in May, critics warned that the terms were loose and the commitments vague. Now, the risks of that ambiguity are becoming apparent. The United States informed the British government this month that it would pause fulfilling a technology-related agreement between the two countries, which included more collaboration on artificial intelligence and nuclear energy, according to two people familiar with the decision who were not authorized to speak publicly. The move came because American officials felt that Britain wasn't making sufficient progress in lowering trade barriers, as promised in the May trade agreement, the people said. Earlier this year, when Prime Minister Keir Starmer of Britain was courting Mr. Trump to avoid punitive trade tariffs, he delivered an invitation from King Charles for a state visit.

This study investigates the effectiveness of Large Language Models (LLMs) for the extraction of structured knowledge in the form of Subject-Predicate-Object triples. We apply the setup for the domain of Economics application. The findings can be applied to a wide range of scenarios, including the creation of economic trade knowledge graphs from natural language legal trade agreement texts. As a use case, we apply the model to regional trade agreement texts to extract trade-related information triples. In particular, we explore the zero-shot, one-shot and few-shot prompting techniques, incorporating positive and negative examples, and evaluate their performance based on quantitative and qualitative metrics. Specifically, we used Llama 3.1 model to process the unstructured regional trade agreement texts and extract triples. We discuss key insights, challenges, and potential future directions, emphasizing the significance of language models in economic applications.

Understanding the complex dynamics of high-dimensional, contingent, and strongly nonlinear economic data, often shaped by multiplicative processes, poses significant challenges for traditional regression methods as such methods offer limited capacity to capture the structural changes they feature. To address this, we propose leveraging the potential of knowledge graph embeddings for economic trade data, in particular, to predict international trade relationships. We implement KonecoKG, a knowledge graph representation of economic trade data with multidimensional relationships using SDM-RDFizer, and transform the relationships into a knowledge graph embedding using AmpliGraph.

With the rapid proliferation of textual data, predicting long texts has emerged as a significant challenge in the domain of natural language processing. Traditional text prediction methods encounter substantial difficulties when grappling with long texts, primarily due to the presence of redundant and irrelevant information, which impedes the model's capacity to capture pivotal insights from the text. To address this issue, we introduce a novel approach to long-text classification and prediction. Initially, we employ embedding techniques to condense the long texts, aiming to diminish the redundancy therein. Subsequently,the Bidirectional Encoder Representations from Transformers (BERT) embedding method is utilized for text classification training. Experimental outcomes indicate that our method realizes considerable performance enhancements in classifying long texts of Preferential Trade Agreements. Furthermore, the condensation of text through embedding methods not only augments prediction accuracy but also substantially reduces computational complexity. Overall, this paper presents a strategy for long-text prediction, offering a valuable reference for researchers and engineers in the natural language processing sphere.

The EU's attempts to regulate Artificial Intelligence could be met with future challenges resulting from an agreement on e-Commerce at the level of the World Trade Organisation (WTO), according to a new study published on Tuesday (26 January). Talks have been ongoing since January 2019 between members of the WTO in a bid to agree on global rules to facilitate worldwide e-commerce transactions. However, concerns have been highlighted that the text currently backed by the EU could result in a prohibition on signatories from adopting legislation that obliges firms to provide access to the source code of their software. In this vein, a report published by the Federation of German Consumer Organisations (vzbv) says that a number of EU objectives in the field of digital policy currently on the table could be stifled by the WTO agreement. "The EU's possibility to adopt rules that, for example, mandate external audits of AI systems will be confined to the policy space that is allowed under trade law," the study notes, adding that the European Council and the Commission are responsible for ensuring that trade deals it makes are compatible with internal policy initiatives.

Jean-Francois Gagne is the CEO of Elemental AI, a Montreal-based startup which develops artificial intelligence solutions for all kinds of businesses. Elemental operates in a tough market with serious competition from tech giants such as Amazon, Google, and Microsoft. Yet thanks to its innovative training technique, which harnesses simulated data, it has created a unique proposition that has attracted an impressive roster of blue chip customers. Here Jean-Francois calls for a re-think on the assumptions built into the economic equation of international trade as well explaining why he thinks people will collaborate with machines to create new value. Elemental is a partner of the Global AI Summit, an event which will be hosted by Tortoise on May 15th 2020, that will examine the future of the world and look in depth at the role technology, and AI in particular, will play in shaping it. Which underlying assumptions about globalisation are going to withstand the pandemic, if any?

In 1985, the TV film Max Headroom: 20 Minutes into the Future presented a science fictional cyberpunk world where an evil media company tried to create an artificial intelligence based on a reporter's brain to generate content to fill airtime. There were somewhat unintended results. Replace "reporter" with "redditors," "evil media company" with "well meaning artificial intelligence researchers," and "airtime" with "a very concerned blog post," and you've got what Ars reported about last week: Generative Pre-trained Transformer-2 (GPT-2), a Franken-creation from researchers at the non-profit research organization OpenAI. Unlike some earlier text-generation systems based on a statistical analysis of text (like those using Markov chains), GPT-2 is a text-generating bot based on a model with 1.5 billion parameters. With or without guidance, GPT-2 can create blocks of text that look like they were written by humans.

International trade has dominated the global headlines recently. Much of the discussions have been focused on the threat of a trade war, the tit-for-tat tariffs, and the health of the global trade order. While extremely important, these conversations are missing a brighter side of international trade – how innovative technologies in the Fourth Industrial Revolution are transforming trade by making the processes more inclusive and efficient. The steam power revolution connected the world like never before. The invention of shipping containers laid the foundation for globalization.

People are increasingly reliant on artificial intelligence (AI) -- that is, the machines, systems or applications that are capable of performing tasks that, until recently, could only be performed by a human. Think of your morning routine: maybe a Google Assistant checks your calendar and reminds you of your meetings. Then you survey Twitter, which uses algorithms to curate what you see -- the latest about Trump, trade and technology rise to the top. And at the end of it all, when you settle in for some Netflix, your profile suggests a few thrillers you're likely to binge-watch. Marketing statistics reveal that some 57 percent of consumers expect voice-activated smart assistants to have a major or moderate impact on their daily lives by 2020.

People are increasingly reliant on artificial intelligence (AI) -- that is, the machines, systems or applications that are capable of performing tasks that, until recently, could only be performed by a human. Think of your morning routine: maybe a Google Assistant checks your calendar and reminds you of your meetings. Then you survey Twitter, which uses algorithms to curate what you see -- the latest about Trump, trade and technology rise to the top. And at the end of it all, when you settle in for some Netflix, your profile suggests a few thrillers you're likely to binge-watch. Marketing statistics reveal that some 57 percent of consumers expect voice-activated smart assistants to have a major or moderate impact on their daily lives by 2020.