How Artificial Intelligence Improves Agricultural Productivity and Sustainability: A Global Thematic Analysis

Amidst the rising issues of food security and climate change, the agricultural sector has started deploying artificial intelligence (AI) in business operations. While many potential AI benefits are anticipated, a comprehensive understanding of the objectives motivating AI adoption and its impacts is lacking. This research attempts to fill this gap by exploring the key themes related to the use of AI in agriculture through the lens of dynamic capabilities. Using centering resonance analysis, we conduct text mining of news articles from 2014-2019 in the regions of Asia, Africa, Europe, and North America to identify how AI is addressing significant farming challenges. Globally, the results suggest that AI is primarily being applied to increase productivity and efficiency and secondarily to address labor shortages and environmental sustainability concerns. At regional level, the results reflect active AI adoption in North America and Europe with increasing efforts in Asia and Africa

Artificial Intelligence dalam Rutinitas Media Online

The development of artificial Intelligence (AI) technology has been widely applied in the world of journalism and the increasing number of digital sources of information has encouraged the media to utilize these sources in journalistic activities. The ability of artificial intelligence to carry out activities like a journalist encourages changes in routine in the newsroom. Online Media Lokadata.ID is the only media in Indonesia that introduces AI technology in the process of producing news content. This study aims to determine how AI changes media routines in the news gathering, news processing and news distribution processes. This type of research is a case study method with a qualitative descriptive approach. The data collection technique uses interviews with the parties involved and understands artificial intelligence. The results show that the use of AI changes the way the editorials process news gathering, news processing and news distribution. Changes to the news gathering process of data collection are carried out by AI by taking predetermined data. AI is used by editors in routine data collection and distribution of events. News processing led to new ways of working and the process of writing, and editing. At this stage, AI creates a hybrid way of working between AI and editor. News distribution AI can increase the number of publications. AI can publish directly. The conclusion of using AI in media routines has led to new work practices in which editors and AI can do work based on the characteristics of the processed information sources. AI is in charge of working on routine news, while journalists are working on news that is more humane and in-depth

In Platforms We Trust?Unlocking the Black-Box of News Algorithms through Interpretable AI

With the rapid increase in the use and implementation of AI in the journalism industry, the ethical issues of algorithmic journalism have grown rapidly and resulted in a large body of research that applied normative principles such as privacy, information disclosure, and data protection. Understanding how users’ information processing leads to information disclosure in platformized news contexts can be important questions to ask. We examine users’ cognitive routes leading to information disclosure by testing the effect of interpretability on privacy in algorithmic journalism. We discuss algorithmic information processing and show how the process can be utilized to improve user privacy and trust

AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images

Dynamic project selection

We study a normative model of an internal capital market that a company uses to choose between its two divisions’ projects. Each project’s value is initially unknown to all, but can be dynamically learned by the corresponding division. Learning can be suspended or resumed at any time and is costly. We characterize an internal capital market that maximizes the company’s expected cash flow