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A woman has a text chat with her long-dead lover. A family gets to hear a deceased elder speak again. A mother gets another chance to say goodbye to her child, who died suddenly, via a digital facsimile. This isn't a preview of the next season of Black Mirror -- these are all true stories from the Sundance documentary Eternal You, a fascinating and frightening dive into tech companies using AI to digitally resurrect the dead. It's yet another way modern AI, which includes large language models like ChatGPT and similar bespoke solutions, has the potential to transform society.

If anyone can rally up a base, it's Taylor Swift. When sexually explicit, likely AI-generated images of Swift circulated on social media this week, it galvanized her fans. Swifties found phrases and hashtags related to the images and flooded them with videos and photos of Swift performing. "Protect Taylor Swift" went viral, trending as Swifties spoke out against not just the Swift deepfakes, but all nonconsensual, explicit images made of women. Swift, arguably the most famous woman in the world right now, has become the high-profile victim of an all-too-frequent form of harassment.

Almost every day, a publication announces layoffs or shuts down. Sports Illustrated just let go almost all of its staff after weathering an embarrassing scandal about AI-generated articles. It's unclear what the desiccated magazine's future holds, but the sad fate of another formerly great outlet offers a preview of what may await fallen media properties. In 2018, the indie women's website The Hairpin stopped publishing, along with its sister site The Awl. This year, The Hairpin has been Frankensteined back into existence and stuffed with slapdash AI-generated articles designed to attract search engine traffic.

The rapid online spread of deepfake pornographic images of Taylor Swift has renewed calls, including from US politicians, to criminalise the practice, in which artificial intelligence is used to synthesise fake but convincing explicit imagery. The images of the US popstar have been distributed across social media and seen by millions this week. Previously distributed on the app Telegram, one of the images of Swift hosted on X was seen 47m times before it was removed. X said in a statement: "Our teams are actively removing all identified images and taking appropriate actions against the accounts responsible for posting them." Yvette D Clarke, a Democratic congresswoman for New York, wrote on X: "What's happened to Taylor Swift is nothing new. For yrs, women have been targets of deepfakes [without] their consent. And [with] advancements in AI, creating deepfakes is easier & cheaper. This is an issue both sides of the aisle & even Swifties should be able to come together to solve."

Dr. Michael Snyder, chair of the genetics department at Stanford School of Medicine, joins "America's Newsroom." While artificial intelligence continues to proliferate in our society in many different ways, wearable AI has yet to take off, and a Harvard dropout named Avi Schiffmann is trying to change that. He's hoping his next project, Tab, reaches the masses. Tab is an AI necklace potentially intended to intercede with God. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER While the success of Harvard dropouts is legendary, from Bill Gates to Mark Zuckerberg, Avi's focus is not just on innovation but also on how technology can create solutions through collaboration.

To resolve the semantic ambiguity in texts, we propose a model, which innovatively combines a knowledge graph with an improved attention mechanism. An existing knowledge base is utilized to enrich the text with relevant contextual concepts. The model operates at both character and word levels to deepen its understanding by integrating the concepts. We first adopt information gain to select import words. Then an encoder-decoder framework is used to encode the text along with the related concepts. The local attention mechanism adjusts the weight of each concept, reducing the influence of irrelevant or noisy concepts during classification. We improve the calculation formula for attention scores in the local self-attention mechanism, ensuring that words with different frequencies of occurrence in the text receive higher attention scores. Finally, the model employs a Bi-directional Gated Recurrent Unit (Bi-GRU), which is effective in feature extraction from texts for improved classification accuracy. Its performance is demonstrated on datasets such as AGNews, Ohsumed, and TagMyNews, achieving accuracy of 75.1%, 58.7%, and 68.5% respectively, showing its effectiveness in classifying tasks.

Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users. Unfortunately, the performance of most KBQA models tends to decline significantly in real-world scenarios where high-quality annotated data is insufficient. To mitigate the burden associated with manual annotation, we introduce FlexKBQA by utilizing Large Language Models (LLMs) as program translators for addressing the challenges inherent in the few-shot KBQA task. Specifically, FlexKBQA leverages automated algorithms to sample diverse programs, such as SPARQL queries, from the knowledge base, which are subsequently converted into natural language questions via LLMs. This synthetic dataset facilitates training a specialized lightweight model for the KB. Additionally, to reduce the barriers of distribution shift between synthetic data and real user questions, FlexKBQA introduces an executionguided self-training method to iterative leverage unlabeled user questions. Furthermore, we explore harnessing the inherent reasoning capability of LLMs to enhance the entire framework. Consequently, FlexKBQA delivers substantial flexibility, encompassing data annotation, deployment, and being domain agnostic. Through extensive experiments on GrailQA, WebQSP, and KQA Pro, we observe that under the few-shot even the more challenging zero-shot scenarios, FlexKBQA achieves impressive results with a few annotations, surpassing all previous baselines and even approaching the performance of supervised models, achieving a remarkable 93% performance relative to the fully-supervised models. We posit that FlexKBQA represents a significant advancement towards exploring better integration of large and lightweight models. The code is open-sourced.

The rapid and wide-scale adoption of AI to generate human speech poses a range of significant ethical and safety risks to society that need to be addressed. For example, a growing number of speech generation incidents are associated with swatting attacks in the United States, where anonymous perpetrators create synthetic voices that call police officers to close down schools and hospitals, or to violently gain access to innocent citizens' homes. Incidents like this demonstrate that multimodal generative AI risks and harms do not exist in isolation, but arise from the interactions of multiple stakeholders and technical AI systems. In this paper we analyse speech generation incidents to study how patterns of specific harms arise. We find that specific harms can be categorised according to the exposure of affected individuals, that is to say whether they are a subject of, interact with, suffer due to, or are excluded from speech generation systems. Similarly, specific harms are also a consequence of the motives of the creators and deployers of the systems. Based on these insights we propose a conceptual framework for modelling pathways to ethical and safety harms of AI, which we use to develop a taxonomy of harms of speech generators. Our relational approach captures the complexity of risks and harms in sociotechnical AI systems, and yields an extensible taxonomy that can support appropriate policy interventions and decision making for responsible multimodal model development and release of speech generators.

Within-basket recommendation (WBR) refers to the task of recommending items to the end of completing a non-empty shopping basket during a shopping session. While the latest innovations in this space demonstrate remarkable performance improvement on benchmark datasets, they often overlook the complexity of user behaviors in practice, such as 1) co-existence of multiple shopping intentions, 2) multi-granularity of such intentions, and 3) interleaving behavior (switching intentions) in a shopping session. This paper presents Neural Pattern Associator (NPA), a deep item-association-mining model that explicitly models the aforementioned factors. Specifically, inspired by vector quantization, the NPA model learns to encode common user intentions (or item-combination patterns) as quantized representations (a.k.a. codebook), which permits identification of users's shopping intentions via attention-driven lookup during the reasoning phase. This yields coherent and self-interpretable recommendations. We evaluated the proposed NPA model across multiple extensive datasets, encompassing the domains of grocery e-commerce (shopping basket completion) and music (playlist extension), where our quantitative evaluations show that the NPA model significantly outperforms a wide range of existing WBR solutions, reflecting the benefit of explicitly modeling complex user intentions.