Explanations in conventional recommender systems have demonstrated benefits in helping the user understand the rationality of the recommendations and improving the system's efficiency, transparency, and trustworthiness. In the conversational environment, multiple contextualized explanations need to be generated, which poses further challenges for explanations. To better measure explainability in conversational recommender systems (CRS), we propose ten evaluation perspectives based on concepts from conventional recommender systems together with the characteristics of CRS. We assess five existing CRS benchmark datasets using these metrics and observe the necessity of improving the explanation quality of CRS. To achieve this, we conduct manual and automatic approaches to extend these dialogues and construct a new CRS dataset, namely Explainable Recommendation Dialogues (E-ReDial). It includes 756 dialogues with over 2,000 high-quality rewritten explanations. We compare two baseline approaches to perform explanation generation based on E-ReDial. Experimental results suggest that models trained on E-ReDial can significantly improve explainability while introducing knowledge into the models can further improve the performance. GPT-3 in the in-context learning setting can generate more realistic and diverse movie descriptions. In contrast, T5 training on E-ReDial can better generate clear reasons for recommendations based on user preferences. E-ReDial is available at https://github.com/Superbooming/E-ReDial.

Many recommender systems are based on optimizing a linear weighting of different user behaviors, such as clicks, likes, shares, etc. Though the choice of weights can have a significant impact, there is little formal study or guidance on how to choose them. We analyze the optimal choice of weights from the perspectives of both users and content producers who strategically respond to the weights. We consider three aspects of user behavior: value-faithfulness (how well a behavior indicates whether the user values the content), strategy-robustness (how hard it is for producers to manipulate the behavior), and noisiness (how much estimation error there is in predicting the behavior). Our theoretical results show that for users, upweighting more value-faithful and less noisy behaviors leads to higher utility, while for producers, upweighting more value-faithful and strategy-robust behaviors leads to higher welfare (and the impact of noise is non-monotonic). Finally, we discuss how our results can help system designers select weights in practice.

Fox News' Eben Brown reports on how more companies are using AI technology to set retail prices based on data-driven supply and demand. Most voters think artificial intelligence technology will change the way we live in the U.S. in the next few years. Whether that is a good thing or bad remains to be seen. In the latest Fox News national survey, voters were asked their main reactions -- without the aid options -- when they think about artificial intelligence. Most often, the response was negative, with the top mentions being afraid and dangerous (16%).

In the first thirty seconds of the director and artist Paul Trillo's short film "Thank You for Not Answering," a woman gazes out the window of a subway car that appears to have sunk underwater. A man appears in the window swimming toward the car, his body materializing from the darkness and swirling water. It's a frightening, claustrophobic, violent scene--one that could have taken hundreds of thousands of dollars of props and special effects to shoot, but Trillo generated it in a matter of minutes using an experimental tool kit made by an artificial-intelligence company called Runway. The figures in the film appear real, played by humans who may actually be underwater. But another glance reveals the uncanniness in their blank eyes, distended limbs, mushy features.

In late March, a well-funded artificial intelligence startup hosted what it said was the first ever AI film festival at the Alamo Drafthouse theater in San Francisco. The startup, called Runway, is best known for cocreating Stable Diffusion, the standout text-to-image AI tool that captured imaginations in 2022. Then, in February of this year, Runway released a tool that could change the entire style of an existing video with just a simple prompt. Runway told budding filmmakers to have at it and later selected 10 short films to showcase at the fest. The short films were mostly demonstrations of technology; well-constructed narratives took a backseat.

People in Texas sounded off on AI job displacement, with half of people who spoke to Fox News convinced that the tech will rob them of work. With new developments in generative artificial intelligence bringing the technology to the forefront of public conversation, concerns about how it will affect jobs in the entertainment industry have risen, even contributing in a writer strike in Hollywood. But, founders of Web3 animation studio Toonstar have been using artificial intelligence in their studio for years, and told Fox News Digital it serves as an aid in the creative process. AI can "unlock creativity" and give animators a "head start" in terms of creativity, Luisa Huang, COO and co-founder of Toonstar told Fox News Digital. "But I have yet to see AI be able to put output anything … that is ready for production," she added.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' This is what you call speedy service. UVeye is a new system that uses artificial intelligence to perform multi-point vehicle inspections in seconds, saving hours of work compared to traditional methods. "It's kind of like an MRI for your car," UVeye Chief Marketing Officer Yaron Saghiv told Fox News Digital. The technology uses computer vision and deep learning originally developed in Israel as a security system that could scan below vehicles for explosive devices and other smuggled items.

The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning. The evolution of DRM architecture has been driven by changes in data forms. However, the development of AI-generated content (AIGC) technology, such as ChatGPT and Stable Diffusion, has given black and shadow industries powerful tools to personalize data and generate realistic images and conversations for fraudulent activities. This poses a challenge for DRM systems to control risks from the source of data generation and to respond quickly to the fast-changing risk environment. This paper aims to provide a technical analysis of the challenges and opportunities of AIGC from upstream, midstream, and downstream paths of black/shadow industries and suggest future directions for improving existing risk control systems. The paper will explore the new black and shadow techniques triggered by generative AI technology and provide insights for building the next-generation DRM system.

Worry over polarization has grown alongside the digital information consumption revolution. Where most scientific work considered user-generated and user-disseminated (i.e.,~Web 2.0) content as the culprit, the potential of purely increased access to information (or Web 1.0) has been largely overlooked. Here, we suggest that the shift to Web 1.0 alone could include a powerful mechanism of belief extremization. We study an empirically calibrated persuasive argument model with confirmation bias. We compare an offline setting -- in which a limited number of arguments is broadcast by traditional media -- with an online setting -- in which the agent can choose to watch contents within a very wide set of possibilities. In both cases, we assume that positive and negative arguments are balanced. The simulations show that the online setting leads to significantly more extreme opinions and amplifies initial prejudice.

The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations. This paper bridges the singability quality gap by formalizing lyric translation into a constrained translation problem, converting theoretical guidance and practical techniques from translatology literature to prompt-driven NMT approaches, exploring better adaptation methods, and instantiating them to an English-Chinese lyric translation system. Our model achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme accuracy, and word boundary recall. In our subjective evaluation, our model shows a 75% relative enhancement on overall quality, compared against naive fine-tuning (Code available at https://github.com/Sonata165/ControllableLyricTranslation).