Police seized ketamine hidden inside life-size Transformer robots in Thailand. A woman who was previously caught trying to ship meth hidden in a food processing machine was trying to send the robots to Taiwan. Thailand authorities made a startling discovery when they busted open lifesize Transformer robot statues and retrieved over 700 pounds of ketamine. "Currently, we are facing a drug trafficking problem with transnational crime networks hidden in all regions, using Thailand as a base to smuggle drugs to third countries continuously through international shipments via air or sea," Police Lt. Gen. Phanurat Lhakbun told reporters of the bust, which happened on April 25. Australian authorities found around 220 pounds of methamphetamine that an unidentified woman tried to smuggle inside a food processing machine on March 12, and they kept an eye on her activities in the following weeks, Viral Press reported.

Shortly after the October 7 attacks on Israel, Google CEO Sundar Pichai issued a statement on social media, extending sympathy to Israelis without mentioning the Palestinians. Other tech executives – including from Meta, Amazon, Microsoft and IBM – offered their gushing support for Israel as well. Since then, they have remained largely silent as the Israeli army has massacred close to 35,000 Palestinians, including more than 14,500 children, destroyed hundreds of schools and all universities and devastated Palestinian homes, healthcare infrastructure, mosques and heritage sites. To execute this shocking level of destruction, the Israeli military has been assisted by artificial intelligence (AI) programs designed to produce targets with little human oversight. It is not clear to what extent foreign tech giants are directly involved in these projects, but we can say with certainty that they supply much of the core infrastructure required to build them, including advanced computer chips, software and cloud computing. Amid this AI-assisted genocide, Big Tech in the United States is quietly continuing business as usual with Israel.

Last week a video showing 60-year-old Peter Abbott screaming abuse at TV producer Samantha Isaacs gained a viral audience, after Abbott was found guilty at Poole magistrates court of "using threatening words or behaviour to cause alarm, distress or fear of violence". In the phone-filmed video, Abbott is seen snarling and shouting as he presses his face up against Isaacs' car window. He looks as if he's channelling the Harry Enfield character Angry Frank, so cartoonishly aggressive are his contorted facial expressions and confrontational behaviour. Not only did he hammer on Isaacs' car but he also called her a "slag" and a "whore". When another male driver pointed out the terrible optics of bullying a woman, he replied: "She's a fucking bloody annoying woman."

This paper presents the Character Decision Points Detection (CHADPOD) task, a task of identification of points within narratives where characters make decisions that may significantly influence the story's direction. We propose a novel dataset based on Choose Your Own Adventure (a registered trademark of Chooseco LLC) games graphs to be used as a benchmark for such a task. We provide a comparative analysis of different models' performance on this task, including a couple of LLMs and several MLMs as baselines, achieving up to 89% accuracy. This underscores the complexity of narrative analysis, showing the challenges associated with understanding character-driven story dynamics. Additionally, we show how such a model can be applied to the existing text to produce linear segments divided by potential branching points, demonstrating the practical application of our findings in narrative analysis.

This paper explores the seamless integration of Generative AI (GenAI) and Evolutionary Algorithms (EAs) within the domain of large-scale multi-objective optimization. Focusing on the transformative role of Large Language Models (LLMs), our study investigates the potential of LLM-Assisted Inference to automate and enhance decision-making processes. Specifically, we highlight its effectiveness in illuminating key decision variables in evolutionarily optimized solutions while articulating contextual trade-offs. Tailored to address the challenges inherent in inferring complex multi-objective optimization solutions at scale, our approach emphasizes the adaptive nature of LLMs, allowing them to provide nuanced explanations and align their language with diverse stakeholder expertise levels and domain preferences. Empirical studies underscore the practical applicability and impact of LLM-Assisted Inference in real-world decision-making scenarios.

Synthetic data generation is increasingly recognized as a crucial solution to address data related challenges such as scarcity, bias, and privacy concerns. As synthetic data proliferates, the need for a robust evaluation framework to select a synthetic data generator becomes more pressing given the variety of options available. In this research study, we investigate two primary questions: 1) How can we select the most suitable synthetic data generator from a set of options for a specific purpose? 2) How can we make the selection process more transparent, accountable, and auditable? To address these questions, we introduce a novel approach in which the proposed ranking algorithm is implemented as a smart contract within a permissioned blockchain framework called Sawtooth. Through comprehensive experiments and comparisons with state-of-the-art baseline ranking solutions, our framework demonstrates its effectiveness in providing nuanced rankings that consider both desirable and undesirable properties. Furthermore, our framework serves as a valuable tool for selecting the optimal synthetic data generators for specific needs while ensuring compliance with data protection principles.

With the growing adoption of machine learning (ML) systems in areas like law enforcement, criminal justice, finance, hiring, and admissions, it is increasingly critical to guarantee the fairness of decisions assisted by ML. In this paper, we study the tradeoff between fairness and accuracy under the statistical notion of equalized odds. We present a new upper bound on the accuracy (that holds for any classifier), as a function of the fairness budget. In addition, our bounds also exhibit dependence on the underlying statistics of the data, labels and the sensitive group attributes. We validate our theoretical upper bounds through empirical analysis on three real-world datasets: COMPAS, Adult, and Law School. Specifically, we compare our upper bound to the tradeoffs that are achieved by various existing fair classifiers in the literature. Our results show that achieving high accuracy subject to a low-bias could be fundamentally limited based on the statistical disparity across the groups.

Large Language Models (LLMs) have become integral to a wide spectrum of applications, ranging from traditional computing tasks to advanced artificial intelligence (AI) applications. This widespread adoption has spurred extensive research into LLMs across various disciplines, including the social sciences. Notably, studies have revealed that LLMs possess emotional intelligence, which can be further developed through positive emotional stimuli. This discovery raises an intriguing question: can negative emotions similarly influence LLMs, potentially enhancing their performance? In response to this question, we introduce NegativePrompt, a novel approach underpinned by psychological principles, involving ten specifically designed negative emotional stimuli. We embark on rigorous experimental evaluations of five LLMs including Flan-T5-Large, Vicuna, Llama 2, ChatGPT, and GPT-4, across a set of 45 tasks. The results are revealing: NegativePrompt markedly enhances the performance of LLMs, evidenced by relative improvements of 12.89% in Instruction Induction tasks and 46.25% in BIG-Bench tasks. Moreover, we conduct attention visualization experiments to decipher the underlying mechanisms of NegativePrompt's influence. Our research contributes significantly to the understanding of LLMs and emotion interaction, demonstrating the practical efficacy of NegativePrompt as an emotion-driven method and offering novel insights for the enhancement of LLMs in real-world applications. The code is available at https://github.com/wangxu0820/NegativePrompt.

"Very creepy," was April's first thought when she saw her face on a generative AI website. April is one half of the Maddison twins. She and her sister Amelia make content for OnlyFans, Instagram and other platforms, but they also existed as a custom generative AI model – made without their consent. "It was really weird to see our faces, but not really our faces," she says. Deepfakes – the creation of realistic but false imagery, video and audio using artificial intelligence – is on the political agenda after the federal government announced last week it would introduce legislation to ban the creation and sharing of deepfake pornography as part of measures to combat violence against women.

Madi Hime is taking a deep drag on a blue vape in the video, her eyes shut, her face flushed with pleasure. The 16-year-old exhales with her head thrown back, collapsing into laughter that causes smoke to billow out of her mouth. The clip is grainy and shaky – as if shot in low light by someone who had zoomed in on Madi's face – but it was damning. Madi was a cheerleader with the Victory Vipers, a highly competitive "all-star" squad based in Doylestown, Pennsylvania. The Vipers had a strict code of conduct; being caught partying and vaping could have got her thrown out of the team. And in July 2020, an anonymous person sent the incriminating video directly to Madi's coaches. Eight months later, that footage was the subject of a police news conference. "The police reviewed the video and other photographic images and found them to be what we now know to be called deepfakes," district attorney Matt Weintraub told the assembled journalists at the Bucks County courthouse on 15 March 2021. Someone was deploying cutting-edge technology to tarnish a teenage cheerleader's reputation. The vaping video was just one of many disturbing communications brought to the attention of Hilltown Township police department, Weintraub said. Madi had been receiving messages telling her she should kill herself. Her mother, Jennifer Hime, had told officers someone had been taking images from Madi's social media and manipulating them "to make her appear to be drinking".