The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform? To enable greater study of this question, we present LegalBench: a collaboratively constructed legal reasoning benchmark consisting of 162 tasks covering six different types of legal reasoning. LegalBench was built through an interdisciplinary process, in which we collected tasks designed and hand-crafted by legal professionals. Because these subject matter experts took a leading role in construction, tasks either measure legal reasoning capabilities that are practically useful, or measure reasoning skills that lawyers find interesting. To enable cross-disciplinary conversations about LLMs in the law, we additionally show how popular legal frameworks for describing legal reasoning -- which distinguish between its many forms -- correspond to LegalBench tasks, thus giving lawyers and LLM developers a common vocabulary. This paper describes LegalBench, presents an empirical evaluation of 20 open-source and commercial LLMs, and illustrates the types of research explorations LegalBench enables.

Apple boss Tim Cook announced the company was investing heavily in artificial intelligence (AI) on Thursday as the company announced its third consecutive quarter of declining revenues, the company's most prolonged sales slump since 2016. Apple's sales for the fiscal third quarter ending 1 July fell 1.4% to $81.8bn. Over the quarter the company made a profit of $19.9bn, higher than analysts had expected. IPhone sales slightly missed analyst estimates, but were made up for by strong sales in the services segment that contains Apple TV and by sales in China that grew 8% year over year. Apple shares were flat in extended trading after the results.

Nintendo just announced its highest first quarter profit ever thanks to sales of The Legend of Zelda: Breath of the Wild and The Super Mario Bros. Movie. The company earned 185.44 billion yen ($1.3 billion) on sales of 461.34 billion yen ($3.2 billion), easily battering its previous fiscal Q1 record of 144.7 billion set in 2020, the company revealed in its latest earnings report. The numbers on those two properties are impressive. Around the world, 168.10 million people watched The Super Mario Bros. Movie, netting the company $1.349 billion as of July 26th -- the highest ever for an original film based on a video game, and the second-highest for an animated film. Meanwhile, The Legend of Zelda: Tears of the Kingdom has sold 18.51 million copies since it launched in May, while Mario Kart 8 Deluxe sold 1.67 million units last quarter. "Sell-through of this one title [Zelda] constitutes approximately half of the first-party software sold this fiscal year," Nintendo said.

Due to complexity and dynamics of construction work, resource, and cash flows, poor management of them usually leads to time and cost overruns, bankruptcy, even project failure. Existing approaches in construction failed to achieve optimal control of resource flow in a dynamic environment with uncertainty. Therefore, this paper introducess a model and method to adaptive control the resource flows to optimize the work and cash flows of construction projects. First, a mathematical model based on a partially observable Markov decision process is established to formulate the complex interactions of construction work, resource, and cash flows as well as uncertainty and variability of diverse influence factors. Meanwhile, to efficiently find the optimal solutions, a deep reinforcement learning (DRL) based method is introduced to realize the continuous adaptive optimal control of labor and material flows, thereby optimizing the work and cash flows. To assist the training process of DRL, a simulator based on discrete event simulation is also developed to mimic the dynamic features and external environments of a project. Experiments in simulated scenarios illustrate that our method outperforms the vanilla empirical method and genetic algorithm, possesses remarkable capability in diverse projects and external environments, and a hybrid agent of DRL and empirical method leads to the best result. This paper contributes to adaptive control and optimization of coupled work, resource, and cash flows, and may serve as a step stone for adopting DRL technology in construction project management.

Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation and do not capture complex higher-order structures modelling relations more than two nodes. In addition, they only consider factors of technical analysis and overlook factors of fundamental analysis that can affect the stock trend significantly. Motivated by them, we propose higher-order graph attention network with joint analysis (H-GAT). H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis. Specifically, the sequential layer of H-GAT take both types of factors as the input of a long-short term memory model. The relation embedding layer of H-GAT constructs a higher-order graph and learn node embedding with GAT. We then predict the ranks of stock return. Extensive experiments demonstrate the superiority of our H-GAT method on the profitability test and Sharp ratio over both NSDAQ and NYSE datasets

PsychoGenics CEO Emer Leahy of Paramus, New Jersey, explains how the first potential AI-discovered treatment for schizophrenia was developed through machine learning. Fox News Digital spoke with her. In humanity's quest to live longer, healthier lives, technology -- particularly artificial intelligence -- is playing an ever-bigger role and expanding into more areas of health care. A California-based medical technology company named Prenuvo, for instance, offers full-body MRI scans that leverage AI to screen patients for over 500 conditions -- including tumors, aneurysms and cysts -- in less than an hour. Now, Prenuvo is announcing a partnership with Cenegenics, a Las Vegas-based company that offers "personalized performance health age management" for its patients.

This paper presents a novel study on harnessing Large Language Models' (LLMs) outstanding knowledge and reasoning abilities for explainable financial time series forecasting. The application of machine learning models to financial time series comes with several challenges, including the difficulty in cross-sequence reasoning and inference, the hurdle of incorporating multi-modal signals from historical news, financial knowledge graphs, etc., and the issue of interpreting and explaining the model results. In this paper, we focus on NASDAQ-100 stocks, making use of publicly accessible historical stock price data, company metadata, and historical economic/financial news. We conduct experiments to illustrate the potential of LLMs in offering a unified solution to the aforementioned challenges. Our experiments include trying zero-shot/few-shot inference with GPT-4 and instruction-based fine-tuning with a public LLM model Open LLaMA. We demonstrate our approach outperforms a few baselines, including the widely applied classic ARMA-GARCH model and a gradient-boosting tree model. Through the performance comparison results and a few examples, we find LLMs can make a well-thought decision by reasoning over information from both textual news and price time series and extracting insights, leveraging cross-sequence information, and utilizing the inherent knowledge embedded within the LLM. Additionally, we show that a publicly available LLM such as Open-LLaMA, after fine-tuning, can comprehend the instruction to generate explainable forecasts and achieve reasonable performance, albeit relatively inferior in comparison to GPT-4.

Nvidia saw its valuation soar to $1tn on Tuesday, making it the fifth most valuable American company and one of the first major corporate beneficiaries of the hype around AI. The chipmaker has been a major and in some cases dominant player in several industries for years. But no development has raised its profile – and its potential windfall – as much as the current excitement around generative AI. Nvidia has been around for 30 years. The company got its start in 1993 building graphics processing units (GPUs) for video games.

Nvidia Corp stock has soared about 26 percent, taking it closer to a market value of $1 trillion after the chip designer's stellar revenue forecast showed that Wall Street has yet to price in the game-changing potential of artificial intelligence. Thursday's surge added to a more than two-fold rise in the stock this year and was set to increase Nvidia's value by about $196bn to nearly $951bn, putting it on course for the largest single-day value gain for a US firm. That market capitalization makes Nvidia twice the size of the second-largest chip firm, Taiwan's TSMC. In the United States, it trails only the trillion-dollar value companies Apple Inc, Alphabet Inc, Microsoft Corp and Amazon.com The rosy earnings also sparked a rally in the chip sector and for AI-focused firms, lifting stock markets from Japan to Europe.

Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text, containing textual and tabular data, remains underexplored. In this research, we specialize in harnessing the potential of LLMs to comprehend critical information from financial reports, which are hybrid long-documents. We propose an Automated Financial Information Extraction (AFIE) framework that enhances LLMs' ability to comprehend and extract information from financial reports. To evaluate AFIE, we develop a Financial Reports Numerical Extraction (FINE) dataset and conduct an extensive experimental analysis. Our framework is effectively validated on GPT-3.5 and GPT-4, yielding average accuracy increases of 53.94% and 33.77%, respectively, compared to a naive method. These results suggest that the AFIE framework offers accuracy for automated numerical extraction from complex, hybrid documents.