Country star Tracy Lawrence talks about his experience using AI and why he thinks there needs to be more regulations while appearing at the ACM Awards. At the heart of most talented singers is their innate ability to write meaningful music. In the country music world, there is a premium for artists who can not only sing but write their own hits, so for some country stars, the concept of artificial intelligence being used in the music industry is nonsense. "I would struggle to think something that couldn't feel could really write a song, to make somebody else feel," musician Riley Green told Fox News Digital of the threat of AI in music at the ACM Awards. "I mean, the world's always going to change. Nothing's worth freaking out over, I think is the main thing. Riley Green is wary of music written by AI, questioning how much people would be able to take away from it. AI HAS KEANU REEVES, HARRISON FORD, ELON MUSK'S EX-GIRLFRIEND GRIMES AT ODDS OVER ITS USE Nate Smith isn't exactly concerned with AI infiltrating the music scene. Other artists have fears over AI's involvement within the industry. I played around with … a little bit of the AI stuff," Tracy Lawrence told Fox News Digital.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' Robots could soon be cloaked in human-like synthetic skin, similar to the cyborg assassin of the "Terminator" movie franchise, after Stanford University researchers developed an ultra-realistic, self-healing material. Researchers have been studying and developing convincing skin materials for robots for years, with Stanford professor Zhenan Bao touting the first multi-layer self-healing synthetic electronic skin back in 2012. More than a decade later, Bao and fellow researchers have taken their studies even further into the future: layers of synthetic skin that can now self-recognize and align with each other when injured, simultaneously allowing the skin to continue functioning while healing. "We've achieved what we believe to be the first demonstration of a multi-layer, thin film sensor that automatically realigns during healing," Christopher B. Cooper, Stanford Ph.D. student and co-author of the study, told SWNS.

As more companies rush to implement AI solutions and software, a growing number of experts are warning that it could result in an explosion of'fake news' and misinformation. Artificial intelligence (AI) will become a "fundamental game changer" throughout the world, enabling scalable disinformation campaigns and online scams, but global cyber-cooperation and traditional security hygiene should provide significant protection for companies and individuals, according to experts. Center for a New American Security CEO Richard Fontaine told Fox News Digital that until now, humans have primarily created disinformation. While it may have been propagated through digital means, it was not made through digital means. But these new AI applications could now allow a government to propagate and originate disinformation at scale.

The emergence of large language models (LLMs) has resulted in the production of LLM-generated texts that is highly sophisticated and almost indistinguishable from texts written by humans. However, this has also sparked concerns about the potential misuse of such texts, such as spreading misinformation and causing disruptions in the education system. Although many detection approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. This survey aims to provide an overview of existing LLM-generated text detection techniques and enhance the control and regulation of language generation models. Furthermore, we emphasize crucial considerations for future research, including the development of comprehensive evaluation metrics and the threat posed by open-source LLMs, to drive progress in the area of LLM-generated text detection.

Differentially private (DP) training methods like DP-SGD can protect sensitive training data by ensuring that ML models will not reveal private information. An alternative approach, which this paper studies, is to use a sensitive dataset to generate a new synthetic dataset which is differentially private with respect to the original data. Doing so has several advantages: synthetic data can be reused for other tasks (including for hyper parameter tuning), retained indefinitely, or shared with third parties without sacrificing privacy. However, obtaining DP data is much harder than introducing DP during training. To make it feasible for text, recent work has utilized public data by starting with a pre-trained generative language model and privately finetuning it on sensitive data. This model can be used to sample a DP synthetic dataset. While this strategy seems straightforward, executing it has proven problematic. Previous approaches either show significant performance loss, or have, as we show, critical design flaws. In this paper we demonstrate that a proper training objective along with tuning fewer parameters results in excellent DP synthetic data quality. Our approach is competitive with direct DP-training of downstream classifiers in terms of performance on downstream tasks. We also demonstrate that our DP synthetic data is not only useful for downstream classifier training, but also to tune those same models.

Existing aspect extraction methods mostly rely on explicit or ground truth aspect information, or using data mining or machine learning approaches to extract aspects from implicit user feedback such as user reviews. It however remains under-explored how the extracted aspects can help generate more meaningful recommendations to the users. Meanwhile, existing research on aspect-based recommendations often relies on separate aspect extraction models or assumes the aspects are given, without accounting for the fact the optimal set of aspects could be dependent on the recommendation task at hand. In this work, we propose to combine aspect extraction together with aspect-based recommendations in an end-to-end manner, achieving the two goals together in a single framework. For the aspect extraction component, we leverage the recent advances in large language models and design a new prompt learning mechanism to generate aspects for the end recommendation task. For the aspect-based recommendation component, the extracted aspects are concatenated with the usual user and item features used by the recommendation model. The recommendation task mediates the learning of the user embeddings and item embeddings, which are used as soft prompts to generate aspects. Therefore, the extracted aspects are personalized and contextualized by the recommendation task. We showcase the effectiveness of our proposed method through extensive experiments on three industrial datasets, where our proposed framework significantly outperforms state-of-the-art baselines in both the personalized aspect extraction and aspect-based recommendation tasks. In particular, we demonstrate that it is necessary and beneficial to combine the learning of aspect extraction and aspect-based recommendation together. We also conduct extensive ablation studies to understand the contribution of each design component in our framework.

Audio bandwidth extension involves the realistic reconstruction of high-frequency spectra from bandlimited observations. In cases where the lowpass degradation is unknown, such as in restoring historical audio recordings, this becomes a blind problem. This paper introduces a novel method called BABE (Blind Audio Bandwidth Extension) that addresses the blind problem in a zero-shot setting, leveraging the generative priors of a pre-trained unconditional diffusion model. During the inference process, BABE utilizes a generalized version of diffusion posterior sampling, where the degradation operator is unknown but parametrized and inferred iteratively. The performance of the proposed method is evaluated using objective and subjective metrics, and the results show that BABE surpasses state-of-the-art blind bandwidth extension baselines and achieves competitive performance compared to non-blind filter-informed methods when tested with synthetic data. Moreover, BABE exhibits robust generalization capabilities when enhancing real historical recordings, effectively reconstructing the missing high-frequency content while maintaining coherence with the original recording. Subjective preference tests confirm that BABE significantly improves the audio quality of historical music recordings. Examples of historical recordings restored with the proposed method are available on the companion webpage: (http://research.spa.aalto.fi/publications/papers/ieee-taslp-babe/)

In this paper, we present a new dataset and benchmark tailored to the task of semantic similarity in song lyrics. Our dataset, originally consisting of 2775 pairs of Spanish songs, was annotated in a collective annotation experiment by 63 native annotators. After collecting and refining the data to ensure a high degree of consensus and data integrity, we obtained 676 high-quality annotated pairs that were used to evaluate the performance of various state-of-the-art monolingual and multilingual language models. Consequently, we established baseline results that we hope will be useful to the community in all future academic and industrial applications conducted in this context.

Useful conversational agents must accurately capture named entities to minimize error for downstream tasks, for example, asking a voice assistant to play a track from a certain artist, initiating navigation to a specific location, or documenting a laboratory result for a patient. However, where named entities such as ``Ukachukwu`` (Igbo), ``Lakicia`` (Swahili), or ``Ingabire`` (Rwandan) are spoken, automatic speech recognition (ASR) models' performance degrades significantly, propagating errors to downstream systems. We model this problem as a distribution shift and demonstrate that such model bias can be mitigated through multilingual pre-training, intelligent data augmentation strategies to increase the representation of African-named entities, and fine-tuning multilingual ASR models on multiple African accents. The resulting fine-tuned models show an 81.5\% relative WER improvement compared with the baseline on samples with African-named entities.

To make The Frost, Waymark took a script written by Josh Rubin, an executive producer at the company who directed the film, and fed it to OpenAI's image-making model DALL-E 2. After some trial and error to get the model to produce images in a style they were happy with, the filmmakers used DALL-E 2 to generate every single shot. Then they used D-ID, an AI tool that can add movement to still images, to animate these shots, making tents flap in the wind and lips move. "We built a world out of what DALL-E was giving back to us," says Rubin. "It's a strange aesthetic, but we welcomed it with open arms. It became the look of the film." "This is certainly the first generative AI film I've seen where the style feels consistent," says Souki Mehdaoui, an independent filmmaker and cofounder of Bell & Whistle, a consultancy specializing in creative technologies.