When it first opened in theatres, in the fall of 1984, "Stop Making Sense," directed by Jonathan Demme and starring the rock group Talking Heads, was quickly recognized as one of the finest concert films ever made. Reviewer after reviewer settled on the word "exhilarating" to describe the experience of watching an expanded nine-member iteration of the four-piece group perform sixteen of their best-known songs in an uninterrupted sequence of dynamically staged and photographed musical vignettes. In the pages of this magazine, Pauline Kael praised the film as "close to perfection," and described the Heads front man, David Byrne, as "a stupefying performer." "He's so white he's almost mock-white," Kael wrote, "and so are his jerky, long-necked, mechanical-man movements. He seems fleshless, bloodless; he might almost be a Black man's parody of how a clean-cut white man moves. But Byrne himself is the parodist, and he commands the stage by his hollow-eyed, frosty verve."

Rapid advances in artificial intelligence are boosting online disinformation and enabling governments to increase censorship and surveillance in a growing threat to human rights, a U.S. nonprofit said in a report published Wednesday. Global internet freedom declined for the 13th consecutive year, with China, Myanmar and Iran having the worst conditions of the 70 countries surveyed by the Freedom on the Net report, which highlighted the risks posed by easy access to generative AI technology. AI allows governments to "enhance and refine online censorship" and amplify digital repression, making surveillance, and the creation and spread of disinformation faster, cheaper, and more effective, said the annual report by Freedom House.

The annual report, Freedom on the Net, scores and ranks countries according to their relative degree of internet freedom, as measured by a host of factors like internet shutdowns, laws limiting online expression, and retaliation for online speech. The 2023 edition, released on October 4, found that global internet freedom declined for the 13th consecutive year, driven in part by the proliferation of artificial intelligence. "Internet freedom is at an all-time low, and advances in AI are actually making this crisis even worse," says Allie Funk, a researcher on the report. Funk says one of their most important findings this year has to do with changes in the way governments use AI, though we are just beginning to learn how the technology is boosting digital oppression. Funk found there were two primary factors behind these changes: the affordability and accessibility of generative AI is lowering the barrier of entry for disinformation campaigns, and automated systems are enabling governments to conduct more precise and more subtle forms of online censorship.

Although pervasive spread of misinformation on social media platforms has become a pressing challenge, existing platform interventions have shown limited success in curbing its dissemination. In this study, we propose a stance-aware graph neural network (stance-aware GNN) that leverages users' stances to proactively predict misinformation spread. As different user stances can form unique echo chambers, we customize four information passing paths in stance-aware GNN, while the trainable attention weights provide explainability by highlighting each structure's importance. Evaluated on a real-world dataset, stance-aware GNN outperforms benchmarks by 32.65% and exceeds advanced GNNs without user stance by over 4.69%. Furthermore, the attention weights indicate that users' opposition stances have a higher impact on their neighbors' behaviors than supportive ones, which function as social correction to halt misinformation propagation. Overall, our study provides an effective predictive model for platforms to combat misinformation, and highlights the impact of user stances in the misinformation propagation.

The adoption of generative AI technologies is swiftly expanding. Services employing both linguistic and mul-timodal models are evolving, offering users increasingly precise responses. Consequently, human reliance on these technologies is expected to grow rapidly. With the premise that people will be impacted by the output of AI, we explored approaches to help AI output produce better results. Initially, we evaluated how contemporary AI services competitively meet user needs, then examined society's depiction as mirrored by Large Language Models (LLMs). We did a query experiment, querying about social conventions in various countries and eliciting a one-word response. We compared the LLMs' value judgments with public data and suggested an model of decision-making in value-conflicting scenarios which could be adopted for future machine value judgments. This paper advocates for a practical approach to using AI as a tool for investigating other remote worlds. This re-search has significance in implicitly rejecting the notion of AI making value judgments and instead arguing a more critical perspective on the environment that defers judgmental capabilities to individuals. We anticipate this study will empower anyone, regardless of their capacity, to receive safe and accurate value judgment-based out-puts effectively.

Large language models (LLMs) are trained on massive internet corpora that often contain copyrighted content. This poses legal and ethical challenges for the developers and users of these models, as well as the original authors and publishers. In this paper, we propose a novel technique for unlearning a subset of the training data from a LLM, without having to retrain it from scratch. We evaluate our technique on the task of unlearning the Harry Potter books from the Llama2-7b model (a generative language model recently open-sourced by Meta). While the model took over 184K GPU-hours to pretrain, we show that in about 1 GPU hour of finetuning, we effectively erase the model's ability to generate or recall Harry Potter-related content, while its performance on common benchmarks (such as Winogrande, Hellaswag, arc, boolq and piqa) remains almost unaffected. We make our fine-tuned model publicly available on HuggingFace for community evaluation. To the best of our knowledge, this is the first paper to present an effective technique for unlearning in generative language models. Our technique consists of three main components: First, we use a reinforced model that is further trained on the target data to identify the tokens that are most related to the unlearning target, by comparing its logits with those of a baseline model. Second, we replace idiosyncratic expressions in the target data with generic counterparts, and leverage the model's own predictions to generate alternative labels for every token. These labels aim to approximate the next-token predictions of a model that has not been trained on the target data. Third, we finetune the model on these alternative labels, which effectively erases the original text from the model's memory whenever it is prompted with its context.

Recently, there has been a significant advancement in text-to-image diffusion models, leading to groundbreaking performance in 2D image generation. These advancements have been extended to 3D models, enabling the generation of novel 3D objects from textual descriptions. This has evolved into NeRF editing methods, which allow the manipulation of existing 3D objects through textual conditioning. However, existing NeRF editing techniques have faced limitations in their performance due to slow training speeds and the use of loss functions that do not adequately consider editing. To address this, here we present a novel 3D NeRF editing approach dubbed ED-NeRF by successfully embedding real-world scenes into the latent space of the latent diffusion model (LDM) through a unique refinement layer. This approach enables us to obtain a NeRF backbone that is not only faster but also more amenable to editing compared to traditional image space NeRF editing. Furthermore, we propose an improved loss function tailored for editing by migrating the delta denoising score (DDS) distillation loss, originally used in 2D image editing to the three-dimensional domain. This novel loss function surpasses the well-known score distillation sampling (SDS) loss in terms of suitability for editing purposes. Our experimental results demonstrate that ED-NeRF achieves faster editing speed while producing improved output quality compared to state-of-the-art 3D editing models.

You can organize your digital image library on your PC with a number of programs and tools, including Microsoft Photos, Google Photos, ACDSee and digiKam.

Hosted by Eleanor Drage and Kerry Mackereth, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode, we talk to Dr Hayleigh Bosher, Associate Dean and Reader in intellectual property law at Brunel University and host of the podcast Whose Song is it Anyway?, a podcast on the intersections of intellectual property (IP) and the music industry. She tells us why AI can never create an original song, what it takes to sue a generative AI company for creating music in the style of someone, and why generative AI risks missing the point about what creativity is. Hayleigh is a Reader in Intellectual Property Law and Associate Dean (Professional Development and Graduate Outcomes) at Brunel University London, as well as, Visiting Research Fellow at the Centre for Intellectual Property, Policy and Management, a legal consultant in the creative industries, an advisor for the independent UK charity for professional musicians, Help Musicians, writer and Book Review Editor for the specialist IP blog IPKat. Her work in this area has been cited extensively in academic, practitioner and policy outputs and she is regularly interviewed by numerous national and international media outlets, including the BBC, ITV, Sky News, Channel 5 News and The Guardian, The Times and The Wall Street Journal.

Just two days before Slovakia's elections, an audio recording was posted to Facebook. On it were two voices: allegedly, Michal Šimečka, who leads the liberal Progressive Slovakia party, and Monika Tódová from the daily newspaper Denník N. They appeared to be discussing how to rig the election, partly by buying votes from the country's marginalized Roma minority. The fact-checking department of news agency AFP said the audio showed signs of being manipulated using AI. But the recording was posted during a 48-hour moratorium ahead of the polls opening, during which media outlets and politicians are supposed to stay silent.