AI expert Marva Bailer explains how, even though there are currently laws in place, the average person has more access than ever to create deepfakes of celebrities. Tom Hanks warned fans of false ads using his name, likeness and voice to promote "wonder drugs," claiming the companies were using artifical intelligence to scam consumers. Hanks shared the "public service announcement" on Thursday on Instagram. "There are multiple ads over the internet falsely using my name, likeness and voice promoting miracle cures and wonder drugs," Hanks wrote in the statement. "These ads have been created without my consent, fraudulently and through AI." Tom Hanks has appeared in several projects that use artificial intelligence, including "The Polar Express" and "Here."

The digitisation of historical print media archives is crucial for increasing accessibility to contemporary records. However, the process of Optical Character Recognition (OCR) used to convert physical records to digital text is prone to errors, particularly in the case of newspapers and periodicals due to their complex layouts. This paper introduces Context Leveraging OCR Correction (CLOCR-C), which utilises the infilling and context-adaptive abilities of transformer-based language models (LMs) to improve OCR quality. The study aims to determine if LMs can perform post-OCR correction, improve downstream NLP tasks, and the value of providing the socio-cultural context as part of the correction process. Experiments were conducted using seven LMs on three datasets: the 19th Century Serials Edition (NCSE) and two datasets from the Overproof collection. The results demonstrate that some LMs can significantly reduce error rates, with the top-performing model achieving over a 60% reduction in character error rate on the NCSE dataset. The OCR improvements extend to downstream tasks, such as Named Entity Recognition, with increased Cosine Named Entity Similarity. Furthermore, the study shows that providing socio-cultural context in the prompts improves performance, while misleading prompts lower performance. In addition to the findings, this study releases a dataset of 91 transcribed articles from the NCSE, containing a total of 40 thousand words, to support further research in this area. The findings suggest that CLOCR-C is a promising approach for enhancing the quality of existing digital archives by leveraging the socio-cultural information embedded in the LMs and the text requiring correction.

Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language models to capture the dialogue context. Most CRS approaches, trained on benchmark datasets, assume that the standard items and responses in these benchmarks are optimal. However, they overlook that users may express negative emotions with the standard items and may not feel emotionally engaged by the standard responses. This issue leads to a tendency to replicate the logic of recommenders in the dataset instead of aligning with user needs. To remedy this misalignment, we introduce empathy within a CRS. With empathy we refer to a system's ability to capture and express emotions. We propose an empathetic conversational recommender (ECR) framework. ECR contains two main modules: emotion-aware item recommendation and emotion-aligned response generation. Specifically, we employ user emotions to refine user preference modeling for accurate recommendations. To generate human-like emotional responses, ECR applies retrieval-augmented prompts to fine-tune a pre-trained language model aligning with emotions and mitigating hallucination. To address the challenge of insufficient supervision labels, we enlarge our empathetic data using emotion labels annotated by large language models and emotional reviews collected from external resources. We propose novel evaluation metrics to capture user satisfaction in real-world CRS scenarios. Our experiments on the ReDial dataset validate the efficacy of our framework in enhancing recommendation accuracy and improving user satisfaction.

Automatic melody-to-lyric generation aims to produce lyrics that align with a given melody. Although previous work can generate lyrics based on high-level control signals, such as keywords or genre, they often struggle with three challenges: (1) lack of controllability, as prior works are only able to produce lyrics from scratch, with little or no control over the content; (2) inability to generate fully structured songs with the desired format; and (3) failure to align prominent words in the lyrics with prominent notes in the melody, resulting in poor lyrics-melody alignment. In this work, we introduce REFFLY (REvision Framework For Lyrics), the first revision framework designed to edit arbitrary forms of plain text draft into high-quality, full-fledged song lyrics. Our approach ensures that the generated lyrics retain the original meaning of the draft, align with the melody, and adhere to the desired song structures. We demonstrate that REFFLY performs well in diverse task settings, such as lyrics revision and song translation. Experimental results show that our model outperforms strong baselines, such as Lyra (Tian et al. 2023) and GPT-4, by 25% in both musicality and text quality.

The stability of visual odometry (VO) systems is undermined by degraded image quality, especially in environments with significant illumination changes. This study employs a deep reinforcement learning (DRL) framework to train agents for exposure control, aiming to enhance imaging performance in challenging conditions. A lightweight image simulator is developed to facilitate the training process, enabling the diversification of image exposure and sequence trajectory. This setup enables completely offline training, eliminating the need for direct interaction with camera hardware and the real environments. Different levels of reward functions are crafted to enhance the VO systems, equipping the DRL agents with varying intelligence. Extensive experiments have shown that our exposure control agents achieve superior efficiency-with an average inference duration of 1.58 ms per frame on a CPU-and respond more quickly than traditional feedback control schemes. By choosing an appropriate reward function, agents acquire an intelligent understanding of motion trends and anticipate future illumination changes. This predictive capability allows VO systems to deliver more stable and precise odometry results. The codes and datasets are available at https://github.com/ShuyangUni/drl_exposure_ctrl.

Sound event localization and detection (SELD) systems using audio recordings from a microphone array rely on spatial cues for determining the location of sound events. As a consequence, the localization performance of such systems is to a large extent determined by the quality of the audio features that are used as inputs to the system. We propose a new feature, based on neural generalized cross-correlations with phase-transform (NGCC-PHAT), that learns audio representations suitable for localization. Using permutation invariant training for the time-difference of arrival (TDOA) estimation problem enables NGCC-PHAT to learn TDOA features for multiple overlapping sound events. These features can be used as a drop-in replacement for GCC-PHAT inputs to a SELD-network. We test our method on the STARSS23 dataset and demonstrate improved localization performance compared to using standard GCC-PHAT or SALSA-Lite input features.

Aligned LLMs are highly secure, capable of recognizing and refusing to answer malicious questions. However, the role of internal parameters in maintaining this security is not well understood, further these models are vulnerable to security degradation when fine-tuned with non-malicious backdoor data or normal data. To address these challenges, our work uncovers the mechanism behind security in aligned LLMs at the parameter level, identifying a small set of contiguous layers in the middle of the model that are crucial for distinguishing malicious queries from normal ones, referred to as "safety layers". We first confirm the existence of these safety layers by analyzing variations in input vectors within the model's internal layers. Additionally, we leverage the over-rejection phenomenon and parameters scaling analysis to precisely locate the safety layers. Building on this understanding, we propose a novel fine-tuning approach, Safely Partial-Parameter Fine-Tuning (SPPFT), that fixes the gradient of the safety layers during fine-tuning to address the security degradation. Our experiments demonstrate that this approach significantly preserves model security while maintaining performance and reducing computational resources compared to full fine-tuning. Recent advancements in Large Language Models (LLMs) have showcased remarkable abilities in natural language generation. However, this progress is accompanied by the risk of producing of harmful or biased outputs, especially when confronted with malicious input prompts. To address this issue, the prevalent approach involves additional reinforcement learning from human feedback (RLHF) (Bai et al., 2022; Dai et al., 2023; Ouyang et al., 2022b) and instruction fine-tuning Wang et al. (2022) on pre-trained LLMs. This process aligns the LLMs with human values and ensures their behavior remains within safe boundaries. These securely aligned models significantly reduce the risk of harmful content leakage when the models are used directly. Real-world applications often require fine-tuning aligned models to adapt to specific domains. This presents a significant challenge: fine-tuning these models with non-malicious normal datasets alongside backdoor datasets, which may favor positive responses, can compromise the security alignment of the models (Qi et al., 2023; Kumar et al., 2024). Restoring security alignment in compromised fine-tuned large language models (LLMs) is frequently inefficient and costly (Dai et al., 2023).

Optical music recognition (OMR) aims to convert music notation into digital formats. One approach to tackle OMR is through a multi-stage pipeline, where the system first detects visual music notation elements in the image (object detection) and then assembles them into a music notation (notation assembly). Most previous work on notation assembly unrealistically assumes perfect object detection. In this study, we focus on the MUSCIMA++ v2.0 dataset, which represents musical notation as a graph with pairwise relationships among detected music objects, and we consider both stages together. First, we introduce a music object detector based on YOLOv8, which improves detection performance. Second, we introduce a supervised training pipeline that completes the notation assembly stage based on detection output. We find that this model is able to outperform existing models trained on perfect detection output, showing the benefit of considering the detection and assembly stages in a more holistic way. These findings, together with our novel evaluation metric, are important steps toward a more complete OMR solution.

Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems, where traditional XAI approaches typically offer one level of abstraction for explanations, often in the form of heatmaps highlighting single or multiple input features. However, we ask whether abstract reasoning or problem-solving strategies of a model may also be relevant, as these align more closely with how humans approach solutions to problems. We propose a framework, called Symbolic XAI, that attributes relevance to symbolic queries expressing logical relationships between input features, thereby capturing the abstract reasoning behind a model's predictions. The methodology is built upon a simple yet general multi-order decomposition of model predictions. This decomposition can be specified using higher-order propagation-based relevance methods, such as GNN-LRP, or perturbation-based explanation methods commonly used in XAI. The effectiveness of our framework is demonstrated in the domains of natural language processing (NLP), vision, and quantum chemistry (QC), where abstract symbolic domain knowledge is abundant and of significant interest to users. The Symbolic XAI framework provides an understanding of the model's decision-making process that is both flexible for customization by the user and human-readable through logical formulas.

Netflix and the BBC have released an all-too-brief look at Wallace and Gromit: Vengeance Most Fowl. A clip from the stop-motion animated movie features Wallace proudly revealing his latest invention, a "smart gnome" called Norbot. The robot aggressively shakes Gromit's paw while introducing itself to the pooch, hinting at trouble ahead. The concept of a smart gnome as a riff on the smart home is funny by itself and it perfectly matches the type of humor the Wallace and Gromit series is known for. Wallace encouraging Gromit to put the voice-activated Norbot through its paces is a great touch too, considering that the beagle is famously silent.