Artificial Intelligence-powered influencers are the new social media trend. But there could be negative impacts from the perfect influencers, a humane technologist warns. INFLUENCER TRAP: New social media trend could prompt mental health crises, suicide as users tune into'fake life': tech founder. WORK WORRIES: A new poll reveals what Americans fear about AI taking their jobs. Republican Wisconsin state Rep. David Steffen has proposed restrictions for minors across all social media platforms.

Streaming may have killed off MP3s in the same way the iPod put paid to CDs and cassettes ended the dominance of vinyls. Each time the music industry has adapted and evolved. But what do record labels have up their sleeves to ward off the threat of artificial intelligence (AI)? Well, after the emergence of a string of'deepfake' songs where the likes of Johnny Cash, Frank Sinatra and Drake have had their voices convincingly mimicked, the world's largest record label is taking action. Universal Music is now in talks with Google to license artists' voices and melodies so they can be used for songs generated by AI.

Yet government officials, journalists, and tech companies were unable to take action before the image had real impact. It not only caused confusion but led to a dip in financial markets. Manipulated and misleading content is not a new phenomenon. But AI enables increasingly accessible, sophisticated, and hyperrealistic content creation that--while it can be used for good, in artistic expression or accessibility improvements--can also be abused to cast doubt on political events, or to defame, harass, and exploit. Whether to promote election integrity, protect evidence, reduce misinformation, or preserve historical records, audiences could benefit from knowing when content has been manipulated or generated with AI.

Google and Universal Music are negotiating a deal on how to license the voices and melodies of artists for artificial intelligence-generated songs. The artists would have a choice to opt in to the process. According to the Financial Times, the talks are at an early stage and there are no plans for an immediate product launch. The talks come after a popular and recent trend of fans using AI programs to make deepfake music that imitates artists' voices and cadence so it appears they are singing other people's songs or even new material. On TikTok, there are several viral videos of convincingly mimicked AI-made tracks of artists, usually without the consent of the artists' voices the creators are making.

More than 70 countries are due to hold regional or national elections by the end of 2024. It will be a period of huge political significance across the globe, with more than 2 billion people (mostly from the global south) directly affected by the outcome of these elections. The stakes for the integrity of democracy have never been higher. As concerns mount about the influential role of information pollution, disseminated through the vast platforms of US and Chinese corporations, in shaping these elections, a new shadow looms: how artificial intelligence – more specifically, generative AI such as OpenAI's ChatGPT – has increasingly moved into the mainstream of technology. The recent wave of hype around AI has seen a fair share of doom-mongering.

We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data. Our proposed method aims to improve performance in multi-task training, zero-shot and few-shot scenarios by providing a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations. We demonstrate that our proposed approach can effectively adapt to new structured forms, and can improve performance in comparison to current methods. For example, our method resulted in a 66% improvement in zero-shot BLEU scores when transferring models trained on table inputs to a knowledge graph dataset. Our proposed method is an important step towards a more general data-to-text generation framework.

In this paper, we propose a self-training approach for automatic speech recognition (ASR) for low-resource settings. While self-training approaches have been extensively developed and evaluated for high-resource languages such as English, their applications to low-resource languages like Punjabi have been limited, despite the language being spoken by millions globally. The scarcity of annotated data has hindered the development of accurate ASR systems, especially for low-resource languages (e.g., Punjabi and M\=aori languages). To address this issue, we propose an effective self-training approach that generates highly accurate pseudo-labels for unlabeled low-resource speech. Our experimental analysis demonstrates that our approach significantly improves word error rate, achieving a relative improvement of 14.94% compared to a baseline model across four real speech datasets. Further, our proposed approach reports the best results on the Common Voice Punjabi dataset.

Thesis concerns the study and development of hierarchical representations for spatio-temporal visual attention modeling and understanding in video sequences. More specifically, we propose two computational models for visual attention. First, we present a generative probabilistic model for context-aware visual attention modeling and understanding. Secondly, we develop a deep network architecture for visual attention modeling, which first estimates top-down spatio-temporal visual attention, and ultimately serves for modeling attention in the temporal domain. The first part of the thesis introduces our first proposal: a generative probabilistic framework for spatio-temporal visual attention modeling and understanding.

Privacy preservation has long been a concern in smart acoustic monitoring systems, where speech can be passively recorded along with a target signal in the system's operating environment. In this study, we propose the integration of two commonly used approaches in privacy preservation: source separation and adversarial representation learning. The proposed system learns the latent representation of audio recordings such that it prevents differentiating between speech and non-speech recordings. Initially, the source separation network filters out some of the privacy-sensitive data, and during the adversarial learning process, the system will learn privacy-preserving representation on the filtered signal. We demonstrate the effectiveness of our proposed method by comparing our method against systems without source separation, without adversarial learning, and without both. Overall, our results suggest that the proposed system can significantly improve speech privacy preservation compared to that of using source separation or adversarial learning solely while maintaining good performance in the acoustic monitoring task.

Fake news is fake material in a news media format but is not processed properly by news agencies. The fake material can provoke or defame significant entities or individuals or potentially even for the personal interests of the creators, causing problems for society. Distinguishing fake news and real news is challenging due to limited of domain knowledge and time constraints. According to the survey, the top three areas most exposed to hoaxes and misinformation by residents are in Banten, DKI Jakarta and West Java. The model of transformers is referring to an approach in the field of artificial intelligence (AI) in natural language processing utilizing the deep learning architectures. Transformers exercise a powerful attention mechanism to process text in parallel and produce rich and contextual word representations. A previous study indicates a superior performance of a transformer model known as BERT over and above non transformer approach. However, some studies suggest the performance can be improved with the use of improved BERT models known as ALBERT and RoBERTa. However, the modified BERT models are not well explored for detecting fake news in Bahasa Indonesia. In this research, we explore those transformer models and found that ALBERT outperformed other models with 87.6% accuracy, 86.9% precision, 86.9% F1-score, and 174.5 run-time (s/epoch) respectively. Source code available at: https://github.com/Shafna81/fakenewsdetection.git