Effective music mixing requires technical and creative finesse, but clear communication with the client is crucial. The mixing engineer must grasp the client's expectations, and preferences, and collaborate to achieve the desired sound. The tacit agreement for the desired sound of the mix is often established using guides like reference songs and demo mixes exchanged between the artist and the engineer and sometimes verbalised using semantic terms. This paper presents the findings of a two-phased exploratory study aimed at understanding how professional mixing engineers interact with clients and use their feedback to guide the mixing process. For phase one, semi-structured interviews were conducted with five mixing engineers with the aim of gathering insights about their communication strategies, creative processes, and decision-making criteria. Based on the inferences from these interviews, an online questionnaire was designed and administered to a larger group of 22 mixing engineers during the second phase. The results of this study shed light on the importance of collaboration, empathy, and intention in the mixing process, and can inform the development of smart multi-track mixing systems that better support these practices. By highlighting the significance of these findings, this paper contributes to the growing body of research on the collaborative nature of music production and provides actionable recommendations for the design and implementation of innovative mixing tools.

Reinforcement learning (RL) for physical design of silicon chips in a Google 2021 Nature paper stirred controversy due to poorly documented claims that raised eyebrows and drew critical media coverage. The paper withheld critical methodology steps and most inputs needed to reproduce results. Our meta-analysis shows how two separate evaluations filled in the gaps and demonstrated that Google RL lags behind (i) human designers, (ii) a well-known algorithm (Simulated Annealing), and (iii) generally-available commercial software, while being slower; and in a 2023 open research contest, RL methods weren't in top 5. Crosschecked data indicate that the integrity of the Nature paper is substantially undermined owing to errors in conduct, analysis and reporting. Before publishing, Google rebuffed internal allegations of fraud. We note policy implications and conclusions for chip design.

Symbolic music generation aims to create musical notes, which can help users compose music, such as generating target instrument tracks based on provided source tracks. In practical scenarios where there's a predefined ensemble of tracks and various composition needs, an efficient and effective generative model that can generate any target tracks based on the other tracks becomes crucial. However, previous efforts have fallen short in addressing this necessity due to limitations in their music representations and models. In this paper, we introduce a framework known as GETMusic, with ``GET'' standing for ``GEnerate music Tracks.'' This framework encompasses a novel music representation ``GETScore'' and a diffusion model ``GETDiff.'' GETScore represents musical notes as tokens and organizes tokens in a 2D structure, with tracks stacked vertically and progressing horizontally over time. At a training step, each track of a music piece is randomly selected as either the target or source. The training involves two processes: In the forward process, target tracks are corrupted by masking their tokens, while source tracks remain as the ground truth; in the denoising process, GETDiff is trained to predict the masked target tokens conditioning on the source tracks. Our proposed representation, coupled with the non-autoregressive generative model, empowers GETMusic to generate music with any arbitrary source-target track combinations. Our experiments demonstrate that the versatile GETMusic outperforms prior works proposed for certain specific composition tasks.

Undeterred by its many detractors, Meta is still trying to make the metaverse happen. This week, the company held its annual Connect developer conference at its headquarters in Menlo Park, California. Meta CEO Mark Zuckerberg took to the stage to announce a new mixed reality headset, the Meta Quest 3, as well as new smart glasses made by Ray-Ban that let the wearer livestream videos and interact with an AI-powered voice chatbot. Meta also showed off an array of celebrity-infused AI chatbots that can mimic big-name folks like Snoop Dogg and Kendall Jenner. You'd be forgiven for thinking all this feels a little bit like an episode of Black Mirror.

Stories about artificial intelligence have been with us for decades, even centuries. In some, the robots serve humanity as cheerful helpers or soulful lovers. In others, the machines eclipse their human makers and try to wipe us out. "The Creator," a sci-fi film that hits theaters Friday, turns that narrative around: The United States is intent on wiping out a society of androids in Asia, afraid the artificially intelligent beings threaten human survival. Do any of these stories reflect our real-life future?

I've been in the entertainment industry since I was nine. I joined the Screen Actors Guild (SAG) when I was 11 in 1977, the Writers Guild of America (WGA) when I was 22, and the Directors Guild of America (DGA) the following year. I got my start as a child actor on Broadway, studied film at NYU, then went on to act in movies like The Lost Boys and the Bill & Ted franchise while writing and directing my own narrative work. I've lived through several labor crises and strikes, but none like our current work shutdown, which began last spring when all three unions' contracts were simultaneously due for renegotiation and the Alliance of Motion Picture and Television Producers (AMPTP) refused their terms. The unifying stress point for labor is the devaluing of the worker, which reached a boiling point with the rapid advancement of highly sophisticated and ubiquitous machine learning tools. Actors have been replaced by AI replications of their likenesses, or their voices have been stolen outright.

Netflix held a virtual event called DROP 01 to celebrate its forthcoming slate of animated projects. The showcase was an absolute cornucopia of trailers and announcements for animation fans, particularly those who enjoy video game adaptations (all of us.) It started with a global livestream of Castlevania: Nocturne's first three episodes ahead of the official premiere later today. After that, the hits kept on coming. We knew there was a Devil May Cry anime being produced, adapting the popular game series, and now we have a trailer.

MyndVR Chief Operating Officer Dave Rawlins discussed with Fox News Digital his company's goal of creating connectivity through a virtual space without trying to'outsource' care to'a machine.' A company focused on developing virtual reality (VR) therapy treatments will turn to artificial intelligence (AI) to create immersive environments that could revolutionize therapy treatment, especially for the elderly and those with mobility issues. "We want to bring the world to residents who otherwise can't experience the world," MyndVR's Chief Operating Officer Dave Rawlins told Fox News Digital. "A lot of what we do is 360 content, provide experiences and applications, modules that give a resident the opportunity to potentially get out of the four walls of the facility, whether that be in travel or Broadway shows or experiencing things that they may have experienced in their earlier life," Rawlins explained. Launched in 2016, MyndVR developed lightweight VR headsets that allow users to engage in gamified environments that help them relax and distract them from a therapy session or boost their moods by leaving their homes to do things they are unable to physically do anymore.

Despite considerable advances in automated fake news detection, due to the timely nature of news, it remains a critical open question how to effectively predict the veracity of news articles based on limited fact-checks. Existing approaches typically follow a "Train-from-Scratch" paradigm, which is fundamentally bounded by the availability of large-scale annotated data. While expressive pre-trained language models (PLMs) have been adapted in a "Pre-Train-and-Fine-Tune" manner, the inconsistency between pre-training and downstream objectives also requires costly task-specific supervision. In this paper, we propose "Prompt-and-Align" (P&A), a novel prompt-based paradigm for few-shot fake news detection that jointly leverages the pre-trained knowledge in PLMs and the social context topology. Our approach mitigates label scarcity by wrapping the news article in a task-related textual prompt, which is then processed by the PLM to directly elicit task-specific knowledge. To supplement the PLM with social context without inducing additional training overheads, motivated by empirical observation on user veracity consistency (i.e., social users tend to consume news of the same veracity type), we further construct a news proximity graph among news articles to capture the veracity-consistent signals in shared readerships, and align the prompting predictions along the graph edges in a confidence-informed manner. Extensive experiments on three real-world benchmarks demonstrate that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.

Conspiracy theories have become a prominent and concerning aspect of online discourse, posing challenges to information integrity and societal trust. As such, we address conspiracy theory detection as proposed by the ACTI @ EVALITA 2023 shared task. The combination of pre-trained sentence Transformer models and data augmentation techniques enabled us to secure first place in the final leaderboard of both sub-tasks. Our methodology attained F1 scores of 85.71% in the binary classification and 91.23% for the fine-grained conspiracy topic classification, surpassing other competing systems.