April 10 was a very bad day in the life of celebrity gamer and YouTuber Atrioc (Brandon Ewing). Ewing was broadcasting one of his usual Twitch livestreams when his browser window was accidentally exposed to his audience. During those few moments, viewers were suddenly face-to-face with what appeared to be deepfake porn videos featuring female YouTubers and gamers QTCinderella and Pokimane--colleagues and, to my understanding, Ewing's friends. Moments later, a quick-witted viewer uploaded a screenshot of the scene to Reddit, and thus the scandal was a fact. Deepfakes refer broadly to media doctored by AI, commonly to superimpose a person's face onto that of, say, an actor in a movie or video clip.

The U.N. Secretary-General's envoy on technology, Amandeep Gill, discussed with Fox News Digital the need to try and align global policy on the emerging artificial intelligence technology, which has "tremendous" potential. The United Nations sees an urgent need for an artificial intelligence (AI) watchdog group but understands that member states must first align on general policies and interests before any such agency could form. "The urgency is very clear in the U.N.'s perspective," Under-Secretary-General and U.N. Tech Envoy Amandeep Gill told Fox News Digital in an interview. Gill has led the way on the U.N.'s efforts to establish an advisory committee on AI policy, which the organization expects to have up and running by the end of the year. The committee is something the U.N. can establish since it has no governing power, and any watchdog agency must come from the member states and the U.N. can only act with what power the members provide it.

College volleyball player Macy Petty reacts to the U.S. House passing a bill that would ban biological males from competing in women's sports on'Fox News @ Night.' An NCAA volleyball player claims ChatGPT scolded her when she asked the artificial intelligence platform to shorten a tweet about the debate over transgender athletes participating in women's sports. "I was trying to explain [in the tweet] that I'm an NCAA athlete, and that it's important to champion the voice of female athletes and to stand up against this ideological war that's going on that's putting women in danger and taking away the opportunities for scholarships," Macy Petty told Fox News Digital in a phone interview Thursday, explaining it was "a lot of information to cram in one tweet." Petty said she is novice when it comes to using ChatGPT - OpenAI's wildly popular chatbot that can mimic human conversation based on prompts - and had seen an Instagram reel touting the importance of using the platform as the future of technology. After watching the reel, Petty said she was presented with a great opportunity to use the system: Allegedly asking ChatGPT to shorten a tweet on women's sports that had gone over the social media platform's character limit.

Paul McCartney has clarified how artificial intelligence has been used to create a new Beatles song, saying that "nothing has been artificially or synthetically created". Last week, McCartney announced that he had employed AI technology on an unreleased Beatles demo from the 70s, telling BBC Radio 4's Today programme that AI had been used to "extricate" John Lennon's voice from a cassette recording of the demo. "We were able to take John's voice and get it pure through this AI," he said. "Then we can mix the record, as you would normally do. It gives you some sort of leeway."

Fox News host Greg Gutfeld reacts to a Danish study finding artificial intelligence can help predict a person’s political ideology based on their facial characteristics on "Gutfeld!"

We describe a significant emerging trend away from the techno-solutionist approach to problems that seeks to create and understand a new paradigm: a productive interplay between algorithms and people.

During the 2016 U.S. presidential race, a Russian "troll-farm" calling itself the Internet Research Agency sought to harm Hillary Clinton's election chances and help Donald Trump reach the White House by using Twitter to spread false news stories and other disinformation, according to a 2020 report from the Senate Intelligence Committee. Most of that content apparently was produced by human beings, a supposition supported by the fact that activity dropped off on Russian holidays. Soon, though, if not already, such propaganda will be produced automatically by artificial intelligence (AI) systems such as ChatGPT, a chatbot capable of creating human-sounding text. "Imagine a scenario where you have ChatGPT generating these tweets. The number of fake accounts you could manage for the same price would be much larger," says V.S. Subrahmanian, a professor of computer science at Northwestern University, whose research focuses on the intersection of AI and security problems.

Deductive coding is a widely used qualitative research method for determining the prevalence of themes across documents. While useful, deductive coding is often burdensome and time consuming since it requires researchers to read, interpret, and reliably categorize a large body of unstructured text documents. Large language models (LLMs), like ChatGPT, are a class of quickly evolving AI tools that can perform a range of natural language processing and reasoning tasks. In this study, we explore the use of LLMs to reduce the time it takes for deductive coding while retaining the flexibility of a traditional content analysis. We outline the proposed approach, called LLM-assisted content analysis (LACA), along with an in-depth case study using GPT-3.5 for LACA on a publicly available deductive coding data set. Additionally, we conduct an empirical benchmark using LACA on 4 publicly available data sets to assess the broader question of how well GPT-3.5 performs across a range of deductive coding tasks. Overall, we find that GPT-3.5 can often perform deductive coding at levels of agreement comparable to human coders. Additionally, we demonstrate that LACA can help refine prompts for deductive coding, identify codes for which an LLM is randomly guessing, and help assess when to use LLMs vs. human coders for deductive coding. We conclude with several implications for future practice of deductive coding and related research methods.

Both knowledge graphs and user-item interaction graphs are frequently used in recommender systems due to their ability to provide rich information for modeling users and items. However, existing studies often focused on one of these sources (either the knowledge graph or the user-item interaction graph), resulting in underutilization of the benefits that can be obtained by integrating both sources of information. In this paper, we propose DEKGCI, a novel double-sided recommendation model. In DEKGCI, we use the high-order collaborative signals from the user-item interaction graph to enrich the user representations on the user side. Additionally, we utilize the high-order structural and semantic information from the knowledge graph to enrich the item representations on the item side. DEKGCI simultaneously learns the user and item representations to effectively capture the joint interactions between users and items. Three real-world datasets are adopted in the experiments to evaluate DEKGCI's performance, and experimental results demonstrate its high effectiveness compared to seven state-of-the-art baselines in terms of AUC and ACC.

Despite recent efforts by the Artificial Intelligence (AI) community to move towards standardised procedures for documenting models, methods, systems or datasets, there is currently no methodology focused on use cases aligned with the risk-based approach of the European AI Act (AI Act). In this paper, we propose a new framework for the documentation of use cases, that we call "use case cards", based on the use case modelling included in the Unified Markup Language (UML) standard. Unlike other documentation methodologies, we focus on the intended purpose and operational use of an AI system. It consists of two main parts. Firstly, a UML-based template, tailored to allow implicitly assessing the risk level of the AI system and defining relevant requirements. Secondly, a supporting UML diagram designed to provide information about the system-user interactions and relationships. The proposed framework is the result of a co-design process involving a relevant team of EU policy experts and scientists. We have validated our proposal with 11 experts with different backgrounds and a reasonable knowledge of the AI Act as a prerequisite. We provide the 5 "use case cards" used in the co-design and validation process. "Use case cards" allows framing and contextualising use cases in an effective way, and we hope this methodology can be a useful tool for policy makers and providers for documenting use cases, assessing the risk level, adapting the different requirements and building a catalogue of existing usages of AI.