Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data to construct new narratives or realities, regardless of the intent to deceive. In this paper, we delve into the concept of synthetic realities and their implications for Digital Forensics and society at large within the rapidly advancing field of AI. We highlight the crucial need for the development of forensic techniques capable of identifying harmful synthetic creations and distinguishing them from reality. This is especially important in scenarios involving the creation and dissemination of fake news, disinformation, and misinformation. Our focus extends to various forms of media, such as images, videos, audio, and text, as we examine how synthetic realities are crafted and explore approaches to detecting these malicious creations. Additionally, we shed light on the key research challenges that lie ahead in this area. This study is of paramount importance due to the rapid progress of AI generative techniques and their impact on the fundamental principles of Forensic Science.

The abundance of information on social media has increased the necessity of accurate real-time rumour detection. Manual techniques of identifying and verifying fake news generated by AI tools are impracticable and time-consuming given the enormous volume of information generated every day. This has sparked an increase in interest in creating automated systems to find fake news on the Internet. The studies in this research demonstrate that the BERT and RobertA models with fine-tuning had the best success in detecting AI generated news. With a score of 98%, tweaked RobertA in particular showed excellent precision. In conclusion, this study has shown that neural networks can be used to identify bogus news AI generation news created by ChatGPT. The RobertA and BERT models' excellent performance indicates that these models can play a critical role in the fight against misinformation.

Multimodal Sentiment Analysis (MSA) has recently become a centric research direction for many real-world applications. This proliferation is due to the fact that opinions are central to almost all human activities and are key influencers of our behaviors. In addition, the recent deployment of Deep Learning-based (DL) models has proven their high efficiency for a wide range of Western languages. In contrast, Arabic DL-based multimodal sentiment analysis (MSA) is still in its infantile stage due, mainly, to the lack of standard datasets. In this paper, our investigation is twofold. First, we design a pipeline that helps building our Arabic Multimodal dataset leveraging both state-of-the-art transformers and feature extraction tools within word alignment techniques. Thereafter, we validate our dataset using state-of-the-art transformer-based model dealing with multimodality. Despite the small size of the outcome dataset, experiments show that Arabic multimodality is very promising

Recommender systems have found significant commercial success but still struggle with integrating new users. Since users often interact with content in different domains, it is possible to leverage a user's interactions in previous domains to improve that user's recommendations in a new one (multi-domain recommendation). A separate research thread on knowledge graph enhancement uses external knowledge graphs to improve single domain recommendations (knowledge graph enhancement). Both research threads incorporate related information to improve predictions in a new domain. We propose in this work to unify these approaches: Using information from interactions in other domains as well as external knowledge graphs to make predictions in a new domain that would be impossible with either information source alone. We apply these ideas to a dataset derived from millions of users' requests for content across three domains (videos, music, and books) in a live virtual assistant application. We demonstrate the advantage of combining knowledge graph enhancement with previous multi-domain recommendation techniques to provide better overall recommendations as well as for better recommendations on new users of a domain.

Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world. We propose HYDRA - a framework for designing model-based agents operating in mixed discrete-continuous worlds, that can autonomously detect when the environment has evolved from its canonical setup, understand how it has evolved, and adapt the agents' models to perform effectively. HYDRA is based upon PDDL+, a rich modeling language for planning in mixed, discrete-continuous environments. It augments the planning module with visual reasoning, task selection, and action execution modules for closed-loop interaction with complex environments. HYDRA implements a novel meta-reasoning process that enables the agent to monitor its own behavior from a variety of aspects. The process employs a diverse set of computational methods to maintain expectations about the agent's own behavior in an environment. Divergences from those expectations are useful in detecting when the environment has evolved and identifying opportunities to adapt the underlying models. HYDRA builds upon ideas from diagnosis and repair and uses a heuristics-guided search over model changes such that they become competent in novel conditions. The HYDRA framework has been used to implement novelty-aware agents for three diverse domains - CartPole++ (a higher dimension variant of a classic control problem), Science Birds (an IJCAI competition problem), and PogoStick (a specific problem domain in Minecraft). We report empirical observations from these domains to demonstrate the efficacy of various components in the novelty meta-reasoning process.

In the evolving landscape of artificial intelligence, multimodal and Neuro-Symbolic paradigms stand at the forefront, with a particular emphasis on the identification and interaction with entities and their relations across diverse modalities. Addressing the need for complex querying and interaction in this context, we introduce SNeL (Structured Neuro-symbolic Language), a versatile query language designed to facilitate nuanced interactions with neural networks processing multimodal data. SNeL's expressive interface enables the construction of intricate queries, supporting logical and arithmetic operators, comparators, nesting, and more. This allows users to target specific entities, specify their properties, and limit results, thereby efficiently extracting information from a scene. By aligning high-level symbolic reasoning with low-level neural processing, SNeL effectively bridges the Neuro-Symbolic divide. The language's versatility extends to a variety of data types, including images, audio, and text, making it a powerful tool for multimodal scene understanding. Our evaluations demonstrate SNeL's potential to reshape the way we interact with complex neural networks, underscoring its efficacy in driving targeted information extraction and facilitating a deeper understanding of the rich semantics encapsulated in multimodal AI models.

I first become aware of A.I. in 1968, when I saw a movie that affected me deeply, 2001: A Space Odyssey, by the director Stanley Kubrick. I loved science-fiction movies, but this one had a special significance. As a person with autism, I'm more rational and fact-based than emotional and feeling-based, and my speech has been described as monotone or unmodulated. In high school, some of the kids called me "robot" and "tape recorder." That's part of why I related to HAL, the sentient computer who, with his steady voice and hyper-logic, helps the astronauts with their mission (until he doesn't).

Experts discuss what is at stake in the AI race between the United States and China, warning it could'dictate the future of humanity.' China and the U.S. have been developing artificial intelligence (AI) systems at a rapid pace that has evolved into a race for dominance, but should China surpass the U.S. in its technological capability, experts warn of dire consequences for America. If China does win an AI race, its actions would impact the U.S. societally, militarily and culturally, putting Americans at their mercy as they shape free speech and power in modern society. "If you are the one that cracks that glass ceiling, if you will, and breaks through in AI, you get to go and also set what the rules of the road look like for that technology for quite some time," James Czerniawski, a senior policy analyst at Americans for Prosperity, told Fox News Digital. "If China is the one that's able to do that, I think they've kind of made it pretty clear where they stand when it comes to AI," he added.

OneDoor Studios CMO Dan Cobb discusses the adaption of the YA series'Calculated.' A Hollywood film studio is leveraging a new real-time design and artist development process to adapt a popular young adult (YA) series, including an industry-first application of artificial intelligence (AI) that gives fans and artists active input in creating character design, sets and special effects. Dan Cobb, the Chief Marketing Officer (CMO) of OneDoor Studios, said development is underway on "Calculated," an adaption of the YA sci-fi series by Nova McBee. On a mission to become the "World's First Fan-Funded and Fan-Created Film Studio," Cobb and his team have developed a relationship with AI artists on the WeGo.One's Discord channel. The artists, who are required to have deep knowledge of the source material, liaise with investors and the author to spawn images using MidJourney V5 Pro and a combination of other similar generative image technologies to build the film's storyboard, enhance concept art and develop shot lists.

A documentary about Hideo Kojima, one of the most lauded video game designers on the planet, is on the way. A trailer offers a first peek at what's in store for those who plan to check out Hideo Kojima: Connecting Worlds. Kojima wrote on Twitter that the film will provide a behind-the-scenes look at the development of Death Stranding, as well as "shots from the early days of our independent studio, memories from my childhood and my creative journey." The trailer encapsulates all of that while suggesting the film is a celebration of Kojima and his work. Friends, collaborators and fans including Geoff Keighley, Guillermo del Toro, Norman Reedus and George Miller are shown talking up Kojima and his credentials.