Fox News contributor Leo Terrell joined'America's Newsroom' to discuss why crime is surging nationwide and how'parental involvement' can reverse the dangerous trend. An Indiana man was slapped with 10 charges after he allegedly fatally shot his father and dismembered his corpse after believing him to be a robot. Shawn Hays, 53, of Lawrence County, Indiana, was arrested Dec. 20 after deputies responded to a welfare check call on his 73-year-old father Rodney Hays, according to a probable cause affidavit cited by local Fox affiliate WXIN. The person who called the police informed them that Hays told them that he had shot and mutilated his father because he had been turned into a robot. Shawn Hays, 53, was slapped with 10 charges after he allegedly fatally shot his father and dismembered his corpse after believing him to be a robot.

Type "Teddy bears working on new AI research on the moon in the 1980s" into any of the recently released text-to-image artificial intelligence image generators, and after just a few seconds the sophisticated software will produce an eerily pertinent image. Seemingly bound by only your imagination, this latest trend in synthetic media has delighted many, inspired others and struck fear in some. Google, research firm OpenAI and AI vendor Stability AI have each developed a text-to-image image generator powerful enough that some observers are questioning whether in the future people will be able to trust the photographic record. As a computer scientist who specializes in image forensics, I have been thinking a lot about this technology: what it is capable of, how each of the tools have been rolled out to the public, and what lessons can be learned as this technology continues its ballistic trajectory. Although their digital precursor dates back to 1997, the first synthetic images splashed onto the scene just five years ago.

In a car outside a convenience store in Flint, Michigan, in late 2016, Kemo Parks handed his cousin Dequavion Harris a gun. Things happened quickly after that: Witnesses saw Harris "with his arm up and extended" toward a red truck. The wounded driver sped off but crashed into a tree. EMTs rushed him to the hospital. He was dead on arrival.

With the advancement of technology, Artificial Intelligence (AI) is increasingly being used to aid in crime solving. AI has the potential to be used in all aspects of crime solving, from aiding in investigations to helping in the prosecution of suspects. AI can be used to analyze huge amounts of data quickly and accurately, allowing investigators to make faster, more informed decisions. AI can also help in facial recognition and biometric identification, allowing for more accurate identification of suspects and witnesses. By utilizing AI in crime solving, law enforcement can more effectively and efficiently combat crime.

Before we all get too deep into using ChatGPT or other AI tools to create things for us, we need to address some of the questions raised around content custody, ownership, and attribution. Some have breathlessly proclaimed ChatGPT to be the most important development since the invention of the printing press or the splitting of the atom. But there are issues with the accuracy, truthfulness, and inherent bias of the materials that AI platforms such as ChatGPT generate. In another matter, since there is speculation that ChatGPT or other AI platforms could take over at least some of the work of writers, analysts, and other content creators, we need to also understand its legal ramifications. And the rules around using ChatGPT to generate term papers seem pretty clear (don't even think about it). But when it comes to applying AI-generated prose in content intended for wider distribution -- say marketing materials, white papers, or even articles -- the legalities get a little murky.

How does Twitter use artificial intelligence and machine learning? Twitter uses large-scale machine learning and AI for sentiment analysis, bot analysis and detection of fake accounts, image classification and more. From Amazon to Instagram, Sephora, Microsoft, and Twitter, AI will shape the future of speech in America and beyond. The big question is not if they use it, but how it is being used, and what impact will this have on consumer privacy in the future. For the past fifteen years, I have been a national commentator on the politics of big tech and social media platforms. Social Media content decisions have become highly political, and artificial intelligence has proliferated this process at scale. But somewhere along the way, the public was left in the dark on just how large of a role machine learning plays in large-scale content operations in Silicon Valley. While the national conversation on free speech focuses on high-profile executives of tech companies and how content ...

President & CEO of BBB National Programs, a non-profit organization dedicated to fostering a more accountable, trustworthy marketplace. In recent years, government scrutiny over the use of artificial intelligence (AI) tools in the recruiting and hiring process has risen. Since I wrote about this topic last year, there has been significant activity within several federal government agencies regarding the use of AI and machine learning in the employment context. A better understanding of these actions can help business leaders reduce their risk of legal liability and better understand how to use AI and machine learning responsibly in their organizations. The Equal Employment Opportunity Commission (EEOC) has been particularly active through its EEOC initiative on AI and algorithmic fairness and its joint HIRE initiative with the U.S. Department of Labor.

Artificial intelligence (AI) in its various forms finds more and more its way into complex distributed systems. For instance, it is used locally, as part of a sensor system, on the edge for low-latency high-performance inference, or in the cloud, e.g. for data mining. Modern complex systems, such as connected vehicles, are often part of an Internet of Things (IoT). To manage complexity, architectures are described with architecture frameworks, which are composed of a number of architectural views connected through correspondence rules. Despite some attempts, the definition of a mathematical foundation for architecture frameworks that are suitable for the development of distributed AI systems still requires investigation and study. In this paper, we propose to extend the state of the art on architecture framework by providing a mathematical model for system architectures, which is scalable and supports co-evolution of different aspects for example of an AI system. Based on Design Science Research, this study starts by identifying the challenges with architectural frameworks. Then, we derive from the identified challenges four rules and we formulate them by exploiting concepts from category theory. We show how compositional thinking can provide rules for the creation and management of architectural frameworks for complex systems, for example distributed systems with AI. The aim of the paper is not to provide viewpoints or architecture models specific to AI systems, but instead to provide guidelines based on a mathematical formulation on how a consistent framework can be built up with existing, or newly created, viewpoints. To put in practice and test the approach, the identified and formulated rules are applied to derive an architectural framework for the EU Horizon 2020 project ``Very efficient deep learning in the IoT" (VEDLIoT) in the form of a case study.

Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to detect and manage malicious information; however, malicious actors also improve their skills, developing strategies to surpass these barriers and continuing to spread misleading information. Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. In response to this recent ongoing issue, this paper presents an innovative approach to address this linguistic trend in social networks through the simulation of different content evasion techniques and a multilingual Transformer model for content evasion detection. In this way, we share with the rest of the scientific community a multilingual public tool, named "pyleetspeak" to generate/simulate in a customizable way the phenomenon of content evasion through automatic word camouflage and a multilingual Named-Entity Recognition (NER) Transformer-based model tuned for its recognition and detection. The multilingual NER model is evaluated in different textual scenarios, detecting different types and mixtures of camouflage techniques, achieving an overall weighted F1 score of 0.8795. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content on social networks, making the fight against information disorders more effective.

Legal Judgment Prediction is one of the most acclaimed fields for the combined area of NLP, AI, and Law. By legal prediction we mean an intelligent systems capable to predict specific judicial characteristics, such as judicial outcome, a judicial class, predict an specific case. In this research, we have used AI classifiers to predict judicial outcomes in the Brazilian legal system. For this purpose, we developed a text crawler to extract data from the official Brazilian electronic legal systems. These texts formed a dataset of second-degree murder and active corruption cases. We applied different classifiers, such as Support Vector Machines and Neural Networks, to predict judicial outcomes by analyzing textual features from the dataset. Our research showed that Regression Trees, Gated Recurring Units and Hierarchical Attention Networks presented higher metrics for different subsets. As a final goal, we explored the weights of one of the algorithms, the Hierarchical Attention Networks, to find a sample of the most important words used to absolve or convict defendants.