With U.S. healthcare spending approaching $5T (NHE Fact Sheet 2024), and 25% of it estimated to be wasteful (Waste in the US the health care system: estimated costs and potential for savings, n.d.), the need to better predict risk and optimal patient care is evermore important. This paper introduces the Large Medical Model (LMM), a generative pre-trained transformer (GPT) designed to guide and predict the broad facets of patient care and healthcare administration. The model is trained on medical event sequences from over 140M longitudinal patient claims records with a specialized vocabulary built from medical terminology systems and demonstrates a superior capability to forecast healthcare costs and identify potential risk factors. Through experimentation and validation, we showcase the LMM's proficiency in not only in cost and risk predictions, but also in discerning intricate patterns within complex medical conditions and an ability to identify novel relationships in patient care. The LMM is able to improve both cost prediction by 14.1% over the best commercial models and chronic conditions prediction by 1.9% over the best transformer models in research predicting a broad set of conditions. The LMM is a substantial advancement in healthcare analytics, offering the potential to significantly enhance risk assessment, cost management, and personalized medicine.

OpenAI has partnered with defense startup Anduril Industries to develop AI for the Pentagon. The companies said on Wednesday that they'll combine OpenAI's models, including GPT-4o and OpenAI o1, with Anduril's systems and software to improve the US military's defenses against unpiloted aerial attacks. The deal comes less than a year after OpenAI softened its stance on using its models for military purposes. Although the ChatGPT maker's policies still prohibit its models from developing or using weapons, it deleted a line in January that explicitly banned integrating its tech into "military and warfare" use. The company said at the time it was already working with DARPA on cybersecurity tools.

OpenAI, maker of ChatGPT and one of the most prominent artificial intelligence companies in the world, said today that it has entered a partnership with Anduril, a defense startup that makes missiles, drones, and software for the United States military. It marks the latest in a series of similar announcements made recently by major tech companies in Silicon Valley, which has warmed to forming closer ties with the defense industry. "OpenAI builds AI to benefit as many people as possible, and supports US-led efforts to ensure the technology upholds democratic values," Sam Altman, OpenAI's CEO, said in a statement Wednesday. OpenAI's AI models will be used to improve systems used for air defense, Brian Schimpf, co-founder and CEO of Anduril, said in the statement. "Together, we are committed to developing responsible solutions that enable military and intelligence operators to make faster, more accurate decisions in high-pressure situations," he said.

Today, OpenAI is announcing that its technology will be deployed directly on the battlefield. The company says it will partner with the defense-tech company Anduril, a maker of AI-powered drones, radar systems, and missiles, to help US and allied forces defend against drone attacks. OpenAI will help build AI models that "rapidly synthesize time-sensitive data, reduce the burden on human operators, and improve situational awareness" to take down enemy drones, according to the announcement. Specifics have not been released, but the program will be narrowly focused on defending US personnel and facilities from unmanned aerial threats, according to Liz Bourgeois, an OpenAI spokesperson. "This partnership is consistent with our policies and does not involve leveraging our technology to develop systems designed to harm others," she said.

China has banned the export of rare but critical earth minerals used in the manufacture of important semiconductors to the United States in the latest move in an ongoing tech war between the two superpowers. Beijing's announcement on Tuesday came just one day after the US ramped up restrictions on the export of advanced chips to China, which affects the country's ability to develop advanced weapons systems and artificial intelligence. So why is a "tech war" brewing between China and the US, and why does it matter? For months, the two countries have been involved in tit-for-tat export restrictions. The US hopes to cripple China's military and artificial intelligence (AI) advances as well as hamper its ambitions to become a global leader in clean energy and other technologies.

There's no denying that ChatGPT and other AI chatbots make impressive chat companions that can converse with you on just about anything. Their conversational powers can be extremely convincing too; if they've made you feel safe about sharing your personal details, you're not alone. Anything you tell an AI chatbot can be stored on a server and resurface later, a fact that makes them inherently risky. The problem stems from how the companies that run Large Language Models (LLMs) and their associated chatbots use your personal data -- essentially, to train better bots. Take the movie Terminator 2: Judgment Day as an example of how an LLM learns.

MLCommons, a nonprofit that helps companies measure the performance of their artificial intelligence systems, is launching a new benchmark to gauge AI's bad side too. The new benchmark, called AILuminate, assesses the responses of large language models to more than 12,000 test prompts in 12 categories including inciting violent crime, child sexual exploitation, hate speech, promoting self-harm, and intellectual property infringement. Models are given a score of "poor," "fair," "good," "very good," or "excellent," depending on how they perform. The prompts used to test the models are kept secret to prevent them from ending up as training data that would allow a model to ace the test. Peter Mattson, founder and president of MLCommons and a senior staff engineer at Google, says that measuring the potential harms of AI models is technically difficult, leading to inconsistencies across the industry.

China ratcheted up trade tensions with the United States with an export ban on several materials with high-tech and military applications, in a tit-for-tat move after U.S. President Joe Biden's administration escalated technology curbs on Beijing. Gallium, germanium, antimony and superhard materials are no longer allowed to be shipped to America, the Chinese Ministry of Commerce said in a statement Tuesday. Beijing will also place tighter controls on sales of graphite, it added. The move came after the White House on Monday slapped fresh curbs on the sale of high-bandwidth memory chips made by U.S. and foreign companies to China. The Biden administration's goal is to slow China's development of advanced semiconductors and artificial intelligence systems that may help its military.

This study explores the application of generative adversarial networks in financial market supervision, especially for solving the problem of data imbalance to improve the accuracy of risk prediction. Since financial market data are often imbalanced, especially high-risk events such as market manipulation and systemic risk occur less frequently, traditional models have difficulty effectively identifying these minority events. This study proposes to generate synthetic data with similar characteristics to these minority events through GAN to balance the dataset, thereby improving the prediction performance of the model in financial supervision. Experimental results show that compared with traditional oversampling and undersampling methods, the data generated by GAN has significant advantages in dealing with imbalance problems and improving the prediction accuracy of the model. This method has broad application potential in financial regulatory agencies such as the U.S. Securities and Exchange Commission (SEC), the Financial Industry Regulatory Authority (FINRA), the Federal Deposit Insurance Corporation (FDIC), and the Federal Reserve.

The Harmonized Tariff System (HTS) classification industry, essential to e-commerce and international trade, currently lacks standardized benchmarks for evaluating the effectiveness of classification solutions. This study establishes and tests a benchmark framework for imports to the United States, inspired by the benchmarking approaches used in language model evaluation, to systematically compare prominent HTS classification tools. The framework assesses key metrics--such as speed, accuracy, rationality, and HTS code alignment--to provide a comprehensive performance comparison. The study evaluates several industry-leading solutions, including those provided by Zonos, Tarifflo, Avalara, and WCO BACUDA, identifying each tool's strengths and limitations. Results highlight areas for industry-wide improvement and innovation, paving the way for more effective and standardized HTS classification solutions across the international trade and e-commerce sectors.