Umpire Dan Bellino's baffling foul tip call on Seiya Suzuki renews calls for robot review in MLB Dakich: sports media has created an'industry' out of complaining about white athletes like Caitlin Clark Greg Sankey insists SEC is'strongest league' despite Big Ten winning three straight national championships Phillies look to upset Dodgers behind Zack Wheeler as Philadelphia's turnaround continues in LA Joey McGuire calls Steve Sarkisian's bluff, dares Texas to play Texas Tech in Week 1 Rams troublemaker WR Puka Nacua says he's a changed man after biting incident and stint in rehab Chiefs have no plans to release Rashee Rice and see jail time as a'life lesson' opportunity Diamondbacks fans catch same player's home run on back-to-back nights after showing up on the wrong date Dr Oz: Is this a flaw or a feature? Father Mike Schmitz: Pope Leo XIV wants this world view in line with humanity's good Pompeo warns Iran will rebuild nuclear facilities'the moment' it gets the chance Purple Heart recipient speaks out after Graham Platner's controversial remarks'Chipotle Karen' caught hurling burrito bowl at worker's face The Carolina Hurricanes are in the Eastern Conference Final for the second straight season and the fourth of Rod Brind'Amour's tenure behind the bench, and they've got the chance to close things out in Game 5 against the Montreal Canadiens. Of course, teams coming into Game 5 with a 3-1 lead are historically almost guaranteed to move on to the Stanley Cup Final; the Canes are not going to get ahead of their skis. Hurricanes forward Jackson Blake, who scored the OT-winner to sweep the Philadelphia Flyers and send Carolina to the conference final, talked about the need to focus on the game tonight and not start thinking ahead to the Western Conference Champion Vegas Golden Knights. It's exciting for sure, Blake said.

This work presents a custom approach to developing a domain specific knowledge assistant for sustainability reporting using the International Financial Reporting Standards (IFRS). In this domain, there is no publicly available question-answer dataset, which has impeded the development of a high-quality chatbot to support companies with IFRS reporting. The two key contributions of this project therefore are: (1) A high-quality synthetic question-answer (QA) dataset based on IFRS sustainability standards, created using a novel generation and evaluation pipeline leveraging Large Language Models (LLMs). This comprises 1,063 diverse QA pairs that address a wide spectrum of potential user queries in sustainability reporting. Various LLM-based techniques are employed to create the dataset, including chain-of-thought reasoning and few-shot prompting. A custom evaluation framework is developed to assess question and answer quality across multiple dimensions, including faithfulness, relevance, and domain specificity. The dataset averages a score range of 8.16 out of 10 on these metrics. (2) Two architectures for question-answering in the sustainability reporting domain - a RAG pipeline and a fully LLM-based pipeline. The architectures are developed by experimenting, fine-tuning, and training on the QA dataset. The final pipelines feature an LLM fine-tuned on domain specific data and an industry classification component to improve the handling of complex queries. The RAG architecture achieves an accuracy of 85.32% on single-industry and 72.15% on cross-industry multiple-choice questions, outperforming the baseline approach by 4.67 and 19.21 percentage points, respectively. The LLM-based pipeline achieves an accuracy of 93.45% on single-industry and 80.30% on cross-industry multiple-choice questions, an improvement of 12.80 and 27.36 percentage points over the baseline, respectively.

Over the last decade, several regulatory bodies have started requiring the disclosure of non-financial information from publicly listed companies, in light of the investors' increasing attention to Environmental, Social, and Governance (ESG) issues. Publicly released information on sustainability practices is often disclosed in diverse, unstructured, and multi-modal documentation. This poses a challenge in efficiently gathering and aligning the data into a unified framework to derive insights related to Corporate Social Responsibility (CSR). Thus, using Information Extraction (IE) methods becomes an intuitive choice for delivering insightful and actionable data to stakeholders. In this study, we employ Large Language Models (LLMs), In-Context Learning, and the Retrieval-Augmented Generation (RAG) paradigm to extract structured insights related to ESG aspects from companies' sustainability reports. We then leverage graph-based representations to conduct statistical analyses concerning the extracted insights. These analyses revealed that ESG criteria cover a wide range of topics, exceeding 500, often beyond those considered in existing categorizations, and are addressed by companies through a variety of initiatives. Moreover, disclosure similarities emerged among companies from the same region or sector, validating ongoing hypotheses in the ESG literature. Lastly, by incorporating additional company attributes into our analyses, we investigated which factors impact the most on companies' ESG ratings, showing that ESG disclosure affects the obtained ratings more than other financial or company data.

Financial experts and analysts seek to predict the variability of financial markets. In particular, the correct prediction of this variability ensures investors successful investments. However, there has been a big trend in finance in the last years, which are the ESG criteria. Concretely, ESG (Economic, Social and Governance) criteria have become more significant in finance due to the growing importance of investments being socially responsible, and because of the financial impact companies suffer when not complying with them. Consequently, creating a stock portfolio should not only take into account its performance but compliance with ESG criteria. Hence, this paper combines mathematical modelling, with ESG and finance. In more detail, we use Bayesian optimization (BO), a sequential state-of-the-art design strategy to optimize black-boxes with unknown analytical and costly-to compute expressions, to maximize the performance of a stock portfolio under the presence of ESG criteria soft constraints incorporated to the objective function. In an illustrative experiment, we use the Sharpe ratio, that takes into consideration the portfolio returns and its variance, in other words, it balances the trade-off between maximizing returns and minimizing risks. In the present work, ESG criteria have been divided into fourteen independent categories used in a linear combination to estimate a firm total ESG score. Most importantly, our presented approach would scale to alternative black-box methods of estimating the performance and ESG compliance of the stock portfolio. In particular, this research has opened the door to many new research lines, as it has proved that a portfolio can be optimized using a BO that takes into consideration financial performance and the accomplishment of ESG criteria.

Alternative credit options can mean the difference between financial well-being and financial hardship for many borrowers. Fintech advancements such as buy-now-pay-later, plus the combination of credit models driven by artificial intelligence and machine learning, may pave the way for a fairer and more inclusive future of credit. But lessons from the financial crisis ring clear: When only one part of the market is required to comply with regulations, the other will compete by offering disadvantageous and risky products. Regulators are now faced with how to advance a regulatory framework that encourages innovation while protecting consumers. Buy now/pay later options spurred marked industry growth, as well as artificial intelligence and machine learning advances during the pandemic, with implications and improved assistance for underserved communities.

All tools can be used for good or ill. Even a broom can be used to sweep the floor or hit someone over the head. The more powerful the tool, the greater the benefit or damage it can cause. The last few months have brought this into stark relief when it comes to computer-assisted facial recognition – the ability of a computer to recognize people's faces from a photo or through a camera. This technology can catalog your photos, help reunite families or potentially be misused and abused by private companies and public authorities alike. Facial recognition technology raises issues that go to the heart of fundamental human rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products.

America's largest tech companies face a growing backlash over the potentially negative impacts of their strategic decisions and innovations. For example, companies like Apple, Facebook, Google and Microsoft are investing in artificial intelligence (AI) technologies and product roadmaps that will replace millions of jobs during the coming years. Experts in marketing, technology and social awareness say it's time for technology providers to assume greater responsibility for the personal pain that comes along with the collective gain. Emerging technology is at almost perpetual odds with the status quo, but U.S. society is coming to realize that dynamic can lead to job losses, unfair treatment of social services and a stain on civic engagement. The power and influence that some tech companies command is being reevaluated in light of the myriad ways people are being disenfranchised in some way by their actions.

Products using artificial intelligence (AI) are creeping into our lives: in the home, online, at work, in the marketplace, in the doctor's office. What if AI gets carried away, if it hasn't already? Plenty of movies and books that contemplate this. While those scenarios may be easy to dismiss, the consequences of what could happen are not. Unless it's fully grasped for its benefits, companies that use AI are putting their brands at risk if society doesn't adequately understand how it benefits from the technology.

Tricentis Accelerate 2016 is a must-attend conference for businesses that value enterprise software test automation solutions. This two-day event provides exciting opportunities to network, discuss, and learn best practices from industry leaders. Infosys is a Platinum sponsor at the event. Our leaders will be conducting a session to highlight the benefits of test automation for modern enterprises. What's more, we will also be available at our booth to give you details about our solutions in this space and outline how we have enabled companies using software testing, automation, and Zero Distance to improve efficiencies and reduce costs.

Infosys Foundation, the philanthropic arm of the IT services company, said it will give a Rs 24-crore grant over the next three years to Delhi-based Indraprastha Institute of Information Technology (IIIT) to set up a research centre for artificial intelligence (AI). For Infosys, which carries out its Corporate Social Responsibility through the foundation, supporting educational institutions has been a fundamental part of its CSR strategy. The grant comes at a time when Infosys chief executive Vishal Sikka, who has a doctorate degree in artificial intelligence from Stanford University, has been trying to increasingly build out capabilities around the strategic focal points of automation and artificial intelligence in the company. The proposed Infosys Center for Artificial Intelligence will facilitate work on both fundamental and applied aspects of AI and focus on areas such as robotics, machine learning, computer vision, AI for software systems, large-scale data analytics. The research will draw on real-time data to develop a deeper understanding of AI for social benefits, and the application of AI in education and related areas, said the release.