A hyper-relational knowledge graph has been recently studied where a triplet is associated with a set of qualifiers; a qualifier is composed of a relation and an entity, providing auxiliary information for a triplet. While existing hyper-relational knowledge graph embedding methods assume that the entities are discrete objects, some information should be represented using numeric values, e.g., (J.R.R., was born in, 1892). Also, a triplet (J.R.R., educated at, Oxford Univ.) can be associated with a qualifier such as (start time, 1911). In this paper, we propose a unified framework named HyNT that learns representations of a hyper-relational knowledge graph containing numeric literals in either triplets or qualifiers. We define a context transformer and a prediction transformer to learn the representations based not only on the correlations between a triplet and its qualifiers but also on the numeric information. By learning compact representations of triplets and qualifiers and feeding them into the transformers, we reduce the computation cost of using transformers. Using HyNT, we can predict missing numeric values in addition to missing entities or relations in a hyper-relational knowledge graph. Experimental results show that HyNT significantly outperforms state-of-the-art methods on real-world datasets.

The Associated Press published standards today for generative AI use in its newsroom. The organization, which has a licensing agreement with ChatGPT maker OpenAI, listed a fairly restrictive and common-sense list of measures around the burgeoning tech while cautioning its staff not to use AI to make publishable content. Although nothing in the new guidelines is particularly controversial, less scrupulous outlets could view the AP's blessing as a license to use generative AI more excessively or underhandedly. The organization's AI manifesto underscores a belief that artificial intelligence content should be treated as the flawed tool that it is -- not a replacement for trained writers, editors and reporters exercising their best judgment. "We do not see AI as a replacement of journalists in any way," the AP's Vice President for Standards and Inclusion, Amanda Barrett, wrote in an article about its approach to AI today.

The Workshop Program of the Association of the Advancement of Artificial Intelligence's 17th Conference on Web and Social Media (ICWSM-23) was held in Limassol, Cyprus from June 5-8. There were six workshops in the program: Disrupt, Ally, Resist, Embrace (DARE): Action Items for Computational Social Scientists in a Changing World, Images in Online Political Communication (PhoMemes 2023), Data for the Wellbeing of Most Vulnerable, Novel Evaluation Approaches for Text Classification Systems (NEATCLasS), Mediate 2023: News Media and Computational Journalism, and TrueHealth 2023: Combating Health Misinformation for Social Well-being. In the past decade, many sophisticated AI-powered tools have been developed and released to the scientific community and the public at large. At the same time, the socio-technical platforms that are at the center of our observations have transformed in unanticipated ways. Many of these developments have occurred against a backdrop of political and social polarization, and, public health and macroeconomic crises, which offer multiple lenses to contextualize (or distort) scientific reflexivity.

Influencer who used AI to dupe internet into thinking she caught her boyfriend cheating reveals she was inspired to do the skit because of real artificial voice scams. BIG ADMISSION: Influencer who deep-faked her boyfriend's voice to catch him cheating tells the full story. Dio used voicemails left by her boyfriend Billy to recreate his voice using AI software. OPTING IN: As AI shows up in doctors' offices, most patients are giving permission. AI PRIORITIES: House Dem warns artificial intelligence could be a tool of'digital colonialism' without'inclusivity' guardrails.

Chris Winfield, founder of Understanding A.I., tells'Fox & Friends Weekend' host Will Cain about a study showing patients preferred medical answers from artificial intelligence over doctors. Artificial intelligence has been used "behind the scenes" in health care for decades, but with the growing popularity of new technologies such as ChatGPT, it's now playing a bigger role in patient care -- including during routine doctor's visits. Physicians may rely on AI to record conversations, manage documentation and create personalized treatment plans. And that raises the question of whether they must get patients' permission first to use the technology during appointments. "While regulations may vary by jurisdiction, obtaining informed consent for using AI is often considered best practice and aligns with the principles of medical ethics," Dr. Harvey Castro, a Dallas, Texas-based board-certified emergency medicine physician and national speaker on artificial intelligence in health care, told Fox News Digital.

The conversation about AI and the workplace is understandably dominated by the downsides – after decades of automation eliminating manufacturing jobs, people in the service sector are worried about being replaced by "robots". But every technological shift creates as well as destroys jobs. Artificial intelligence – at least in its current iteration, which uses large language datasets to create text, audio and video – is no different. What is, perhaps, surprising is the type of jobs it will create. The most visible and obvious new roles are for those with the coding and development skills to help build AI models or adapt them for particular purposes.

The proliferation of technologies, such as extended reality (XR), has increased the demand for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications encompass computer-aided design (CAD), finite element analysis (FEA), scanning, and robotics. However, current methods employed for industrial 3D representations suffer from high implementation costs and reliance on manual human input for accurate 3D modeling. To address these challenges, neural radiance fields (NeRFs) have emerged as a promising approach for learning 3D scene representations based on provided training 2D images. Despite a growing interest in NeRFs, their potential applications in various industrial subdomains are still unexplored. In this paper, we deliver a comprehensive examination of NeRF industrial applications while also providing direction for future research endeavors. We also present a series of proof-of-concept experiments that demonstrate the potential of NeRFs in the industrial domain. These experiments include NeRF-based video compression techniques and using NeRFs for 3D motion estimation in the context of collision avoidance. In the video compression experiment, our results show compression savings up to 48\% and 74\% for resolutions of 1920x1080 and 300x168, respectively. The motion estimation experiment used a 3D animation of a robotic arm to train Dynamic-NeRF (D-NeRF) and achieved an average peak signal-to-noise ratio (PSNR) of disparity map with the value of 23 dB and an structural similarity index measure (SSIM) 0.97.

In today's world, with the rise of numerous social platforms, it has become relatively easy for anyone to spread false information and lure people into traps. Fraudulent schemes and traps are growing rapidly in the investment world. Due to this, countries and individuals face huge financial risks. We present an awareness system with the use of machine learning and gamification techniques to educate the people about investment scams and traps. Our system applies machine learning techniques to provide a personalized learning experience to the user. The system chooses distinct game-design elements and scams from the knowledge pool crafted by domain experts for each individual. The objective of the research project is to reduce inequalities in all countries by educating investors via Active Learning. Our goal is to assist the regulators in assuring a conducive environment for a fair, efficient, and inclusive capital market. In the paper, we discuss the impact of the problem, provide implementation details, and showcase the potentiality of the system through preliminary experiments and results.

Instruction-tuned Language Models (LMs) are widely used by users to address various problems with task-specific prompts. Constraints associated with the context window length and computational costs encourage the development of compressed prompts. Existing methods rely heavily on training embeddings, which are designed to accommodate multiple token meanings. This presents challenges in terms of interpretability, a fixed number of embedding tokens, reusability across different LMs, and inapplicability when interacting with black-box APIs. This study proposes prompt compression with reinforcement learning (PCRL), a novel discrete prompt compression method that addresses these issues. PCRL employs a computationally efficient policy network that directly edits prompts. The PCRL training approach can be flexibly applied to various types of LMs, as well as decoder-only and encoder-decoder architecture, and can be trained without gradient access to LMs or labeled data. PCRL achieves an average reduction of 24.6% in token count across various instruction prompts while preserving performance. Further, we demonstrate that the learned policy can be transferred to larger LMs, and through various analyses, we aid the understanding of token importance within prompts.

Recommendation engine suggest content, product or services to the user by using machine learning algorithm. This paper proposed a content-based recommendation engine for providing video suggestion to the user based on their previous interests and choices. We will use TF-IDF text vectorization method to determine the relevance of words in a document. Then we will find out the similarity between each content by calculating cosine similarity between them. Finally, engine will recommend videos to the users based on the obtained similarity score value. In addition, we will measure the engine's performance by computing precision, recall, and F1 core of the proposed system.