Using existing scripts to train AIs and deploying the technology to draft new scripts are major concerns in the ongoing Hollywood writers' strike Could AI soon write your favourite Hollywood film or streaming show? That concern is one of the issues driving a US film and television writers' strike that has halted many productions nationwide. The Writers Guild of America (WGA), a labour union representing writers who primarily work in film and television, began the work strike this month after reaching an impasse in negotiations with the Alliance of Motion Picture and Television Producers that represents the US entertainment industry. Part of the disagreement revolves around a WGA proposal to ban the industry from using AIs such as ChatGPT to generate story ideas or scripts for films and shows – the union wants to ensure that such technologies do not undermine writers' compensation and writing credits. "The fear is that AI could be used to produce first drafts of shows, and then a small number of writers would work off of those scripts," says Virginia Doellgast at Cornell University in New York.

One killed, 10 injured and dozens of homes damage after tornado strikes Laguna Heights, Texas. Drone footage has emerged capturing the aftermath of a deadly tornado that ripped through a Texas Gulf Coast town near the U.S.-Mexico border. The EF-1 twister that struck Laguna Heights early Saturday, located on the mainland across from South Padre Island, left one dead and 10 injured, officials said. Video taken by the Brownsville Fire Department shows the damage that was inflicted upon as many as 60 homes, with some missing roofs and others reduced to piles of rubble. Roberto Flores, 42, died after being "basically crushed as a result of the damage to his mobile home," according to Eddie Treviño Jr., a judge in Cameron County.

These days, so-called generative AI can (allegedly) make art, write books, and compose poetry. Systems like Stable Diffusion, Midjourney, and ChatGPT are seemingly quite good at it. But for some artists, this creates problems. Namely, determining what legal rights they have when their work is scraped by these tools. Faced by the rise in these systems, authors and artists are pushing back.

A shower of pink petals rains down in slow motion against an ethereal backdrop of minimalist white arches, bathed in the soft focus of a cosmetics advert. The camera pulls back to reveal the petals have clustered together to form a delicate puffy armchair, standing in the centre of a temple-like space, surrounded by a dreamy landscape of fluffy pink trees. It looks like a luxury zen retreat, as conceived by Glossier. The aesthetic is eerily familiar: these are the pastel tones, tactile textures and ubiquitous arches of Instagram architecture, an amalgamation of design tropes specifically honed for likes. An ode to millennial pink, this computer-rendered scene has been finely tuned to seduce the social media algorithm, calibrated to slide into your feed like a sugary tranquilliser, promising to envelop you in its candy-floss embrace.

PsychoGenics CEO Emer Leahy of Paramus, New Jersey, explains how the first potential AI-discovered treatment for schizophrenia was developed through machine learning. Fox News Digital spoke with her. AI experts and tech-inclined political scientists are sounding the alarm on the unregulated use of AI tools going into an election season. Generative AI can not only rapidly produce targeted campaign emails, texts or videos, it also could be used to mislead voters, impersonate candidates and undermine elections on a scale and at a speed not yet seen. A booth is ready for a voter, Feb. 24, 2020, at City Hall in Cambridge, Mass., on the first morning of early voting in the state.

Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation. What limits the successful transfer between them is the sizable gap between machine-generated fake news and human-authored ones, including the notable differences in terms of style and underlying intent. With this in mind, we propose a novel framework for generating training examples that are informed by the known styles and strategies of human-authored propaganda. Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles, while also incorporating propaganda techniques, such as appeal to authority and loaded language. In particular, we create a new training dataset, PropaNews, with 2,256 examples, which we release for future use. Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.

Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information often exists in large corpora. To find it, we introduce a method that uses query augmentation to search for a diverse set of passages that could answer the original question but are less likely to have been poisoned. We integrate these new passages into the model through the design of a novel confidence method, comparing the predicted answer to its appearance in the retrieved contexts (what we call \textit{Confidence from Answer Redundancy}, i.e. CAR). Together these methods allow for a simple but effective way to defend against poisoning attacks that provides gains of nearly 20\% exact match across varying levels of data poisoning/knowledge conflicts.

Denoising Diffusion Probabilistic models have emerged as simple yet very powerful generative models. Unlike other generative models, diffusion models do not suffer from mode collapse or require a discriminator to generate high-quality samples. In this paper, a diffusion model that uses a binomial prior distribution to generate piano rolls is proposed. The paper also proposes an efficient method to train the model and generate samples. The generated music has coherence at time scales up to the length of the training piano roll segments. The paper demonstrates how this model is conditioned on the input and can be used to harmonize a given melody, complete an incomplete piano roll, or generate a variation of a given piece. The code is publicly shared to encourage the use and development of the method by the community.

Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks. Along this line of research, this work aims to investigate the capacity of LLMs that act as the ranking model for recommender systems. To conduct our empirical study, we first formalize the recommendation problem as a conditional ranking task, considering sequential interaction histories as conditions and the items retrieved by the candidate generation model as candidates. We adopt a specific prompting approach to solving the ranking task by LLMs: we carefully design the prompting template by including the sequential interaction history, the candidate items, and the ranking instruction. We conduct extensive experiments on two widely-used datasets for recommender systems and derive several key findings for the use of LLMs in recommender systems. We show that LLMs have promising zero-shot ranking abilities, even competitive to or better than conventional recommendation models on candidates retrieved by multiple candidate generators. We also demonstrate that LLMs struggle to perceive the order of historical interactions and can be affected by biases like position bias, while these issues can be alleviated via specially designed prompting and bootstrapping strategies.

In this paper, we present ChatPLUG, a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified internet-augmented format. Different from other open-domain dialogue models that focus on large-scale pre-training and scaling up model size or dialogue corpus, we aim to build a powerful and practical dialogue system for digital human with diverse skills and good multi-task generalization by internet-augmented instruction tuning. To this end, we first conduct large-scale pre-training on both common document corpus and dialogue data with curriculum learning, so as to inject various world knowledge and dialogue abilities into ChatPLUG. Then, we collect a wide range of dialogue tasks spanning diverse features of knowledge, personality, multi-turn memory, and empathy, on which we further instruction tune \modelname via unified natural language instruction templates. External knowledge from an internet search is also used during instruction finetuning for alleviating the problem of knowledge hallucinations. We show that \modelname outperforms state-of-the-art Chinese dialogue systems on both automatic and human evaluation, and demonstrates strong multi-task generalization on a variety of text understanding and generation tasks. In addition, we deploy \modelname to real-world applications such as Smart Speaker and Instant Message applications with fast inference. Our models and code will be made publicly available on ModelScope: https://modelscope.cn/models/damo/ChatPLUG-3.7B and Github: https://github.com/X-PLUG/ChatPLUG .