In the last 5 years, the availability of large audio datasets in African countries has opened unlimited opportunities to build machine intelligence (MI) technologies that are closer to the people and speak, learn, understand, and do businesses in local languages, including for those who cannot read and write. Unfortunately, these audio datasets are not fully exploited by current MI tools, leaving several Africans out of MI business opportunities. Additionally, many state-of-the-art MI models are not culture-aware, and the ethics of their adoption indexes are questionable. The lack thereof is a major drawback in many applications in Africa. This paper summarizes recent developments in machine intelligence in Africa from a multi-layer multiscale and culture-aware ethics perspective, showcasing MI use cases in 54 African countries through 400 articles on MI research, industry, government actions, as well as uses in art, music, the informal economy, and small businesses in Africa. The survey also opens discussions on the reliability of MI rankings and indexes in the African continent as well as algorithmic definitions of unclear terms used in MI.

Data science has been described as the fourth paradigm for scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges - our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI have sparked debates about the socio-technical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for "data science for social good" (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of socio-technical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the special issue. We hope that this article and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are garnering proportionately less attention with each passing day.

The ACL Anthology is an online repository that serves as a comprehensive collection of publications in the field of natural language processing (NLP) and computational linguistics (CL). This paper presents a tool called ``ACL Anthology Helper''. It automates the process of parsing and downloading papers along with their meta-information, which are then stored in a local MySQL database. This allows for efficient management of the local papers using a wide range of operations, including "where," "group," "order," and more. By providing over 20 operations, this tool significantly enhances the retrieval of literature based on specific conditions. Notably, this tool has been successfully utilised in writing a survey paper (Tang et al.,2022a). By introducing the ACL Anthology Helper, we aim to enhance researchers' ability to effectively access and organise literature from the ACL Anthology. This tool offers a convenient solution for researchers seeking to explore the ACL Anthology's vast collection of publications while allowing for more targeted and efficient literature retrieval.

Table of contents (ToC) extraction centres on structuring documents in a hierarchical manner. In this paper, we propose a new dataset, ESGDoc, comprising 1,093 ESG annual reports from 563 companies spanning from 2001 to 2022. These reports pose significant challenges due to their diverse structures and extensive length. To address these challenges, we propose a new framework for Toc extraction, consisting of three steps: (1) Constructing an initial tree of text blocks based on reading order and font sizes; (2) Modelling each tree node (or text block) independently by considering its contextual information captured in node-centric subtree; (3) Modifying the original tree by taking appropriate action on each tree node (Keep, Delete, or Move). This construction-modelling-modification (CMM) process offers several benefits. It eliminates the need for pairwise modelling of section headings as in previous approaches, making document segmentation practically feasible. By incorporating structured information, each section heading can leverage both local and long-distance context relevant to itself. Experimental results show that our approach outperforms the previous state-of-the-art baseline with a fraction of running time. Our framework proves its scalability by effectively handling documents of any length.

The sixth AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) took place in Montreal, Canada, from 8-10 August 2023. The three-day event included keynote talks, contributed talks and poster sessions. There were also two panel discussions. The session was moderated by Alex John London (Carnegie Mellon University), and the panellists were: Roxana Daneshjou (Stanford), Atoosa Kasirzadeh (University of Edinburgh), Kate Larson (University of Waterloo) and Gary Marchant (Arizona State University). The panellists began by talking about some of their hopes for large languages models.

Welcome to the special section highlighting cutting-edge research and innovation emerging from East Asia and Oceania. Our region encompasses Southeast Asia, Oceania, and Asia-Pacific countries, including Japan and Korea. The articles in this section--designated as "Hot Topics" and "Big Trends"--aim to not only showcase technological advancements from this region, but also to strengthen research collaboration and communication with regions worldwide. This special section brings together some of the most innovative research in computer science and technology from this flourishing region. The articles cover a wide range of topics, from state-of-the-art developments in learning analytics, AI and machine learning, education, Big Data, neuromorphic computing, and blockchain technology, to applications in disease prediction and assistive devices.

For COOs, CIOs and IT leadership, EmTech Next uncovers the opportunities exposed by cutting-edge technologies that are reshaping the way business innovates, operates and grows. Our agenda for this 6th edition of our signature digital transformation event covers generative AI, web3, metaverses, leadership strategies for the digital workforce, technology and industry 4.0, and the emerging technologies transforming the customer experience.

In July 2022, the Center for Security and Emerging Technology (CSET) at Georgetown University and the Program on Geopolitics, Technology, and Governance at the Stanford Cyber Policy Center convened a workshop of experts to examine the relationship between vulnerabilities in artificial intelligence systems and more traditional types of software vulnerabilities. Topics discussed included the extent to which AI vulnerabilities can be handled under standard cybersecurity processes, the barriers currently preventing the accurate sharing of information about AI vulnerabilities, legal issues associated with adversarial attacks on AI systems, and potential areas where government support could improve AI vulnerability management and mitigation. Attendees at the workshop included industry representatives in both cybersecurity and AI red-teaming roles; academics with experience conducting adversarial machine learning research; legal specialists in cybersecurity regulation, AI liability, and computer-related criminal law; and government representatives with significant AI oversight responsibilities. This report is meant to accomplish two things. First, it provides a high-level discussion of AI vulnerabilities, including the ways in which they are disanalogous to other types of vulnerabilities, and the current state of affairs regarding information sharing and legal oversight of AI vulnerabilities. Second, it attempts to articulate broad recommendations as endorsed by the majority of participants at the workshop. These recommendations, categorized under four high-level topics, are as follows: 1. Topic: Extending Traditional Cybersecurity for AI Vulnerabilities 1.1. Recommendation: Organizations building or deploying AI models should use a risk management framework that addresses security throughout the AI system life cycle.

All Indian Reprints of O'Reilly are printed in Grayscale Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python.

As methodologies for machine learning become more widely used, it is crucial for the creators of machine learning applications to understand what the underlying algorithms are learning and, more importantly, how the different algorithms are deriving patterns from the original information in order to maximize their efficiency. The target audience for this book is data scientists and analysts who are curious about the inner workings of different machine learning algorithms. The knowledge and abilities you get from this book will help you construct the most important predictive models for machine learning and evaluate models that are given to you. This book considers an AI & ML book which is one of the General books. We first develop the algorithms in Excel so that we may take a peep inside the procedures' mysterious black box in order to understand what the machine learning algorithms are learning and how they are learning it.