"AI and machine–learning tools excel at extracting complex information from data and will help bolster the accuracy of our climate simulations and …

Potts is a doctoral candidate in the Department of Mathematical Sciences who studies the development of machine learning and data science …

… manufacturing (DFM) signoff with machine learning (ML) prediction capabilities. As part of the collaboration, the Cadence® Litho Physical Analyzer, …

Deep learning learns through an artificial network that acts like the human brain and enables the machine to analyze data in a structured manner.

Strauts: Quantitative Rating is a machine–learning model, which is meant to mimic how our analysts would rate a fund if they're actually covering that …

Developers at DeepMind, an artificial intelligence startup acquired by Google, had fed 30 million Go moves into a deep neural network.

Interactive online platforms have become an integral part of our daily lives. While user-generated content, free from traditional editorial constraints, has spurred vibrant online communications, improved business processes and expanded access to information, it has also raised complex questions regarding how to moderate harmful online content. As the volume of user-generated content continues to grow, it has become increasingly difficult for internet and social media companies to keep pace with the moderation needs of the information posted on their platforms. Content moderation measures supported by artificial intelligence (AI) have emerged as important tools to address this challenge. Whether you are managing a social media platform or an e-commerce site, minimizing harmful content is critical to the user experience. Such harmful content can include everything from posts promoting violence to child abuse.

This paper focuses on the first. It encompasses knowledge representation and reasoning, with a focus here on (non-classical) reasoning (a second companion paper will focus on representation). The focus is on the act of reasoning that determines if some data can be seen (or interpreted) as belonging to a particular class, not on long chains of reasoning using diverse knowledge. A significant weakness of Artificial Intelligence (AI) systems relative to humans is the inability to apply existing knowledge to a new problem, or to a situation that varies from what they were programmed for or trained for (also called transfer ability in some contexts). This causes systems to fail to recognise objects or activities in new settings, or to fail to adapt skills to variations (Davis and Marcus, 2015; Ersen et al., 2017).

This article describes research on claim verification carried out using a multiple GAN-based model. The proposed model consists of three pairs of generators and discriminators. The generator and discriminator pairs are responsible for generating synthetic data for supported and refuted claims and claim labels. A theoretical discussion about the proposed model is provided to validate the equilibrium state of the model. The proposed model is applied to the FEVER dataset, and a pre-trained language model is used for the input text data. The synthetically generated data helps to gain information which helps the model to perform better than state of the art models and other standard classifiers.

For the last few years, I have been learning and practicing Deep Learning. Participated in several Kaggle competitions and won few medals. During all these years, I tried several cloud platforms and on premise systems. Some of them offered simplicity, flexibility, and affordability. But very few to none offered all of these in one platform.