U.S. citizens who visit Mexico are being warned that they may be at risk of being kidnapped by people who lure them in through dating apps, according to federal officials. The U.S. Consulate General Guadalajara warned that the victims of such schemes were kidnapped in Puerto Vallarta and Nuevo Nayarit areas in recent months, according to a news release. The consulate did not say how often this type of crime has occurred or whether any suspects have been arrested. Victims and their family members were extorted for large amounts of money in order to be released, officials said. Some of the victims met their captors in residences or hotel rooms.

Turning text into videos is one of the latest AI skills creating a buzz. And Microsoft is offering a free way to transform your ideas into quick video clips. Introduced on Monday, the Bing Video Creator is now accessible through the Bing mobile app and is coming soon to Bing on the desktop and Copilot Search. "Bing Video Creator represents our efforts to democratize the power of AI video generation," Microsoft said in its blog post. "We believe creativity should be effortless and accessible to help you satisfy your answer-seeking process."

This week, I'm wondering what my first jobs in journalism would have been like had generative AI been around. In other news: Elon Musk leaves a trail of chaos, and influencers are selling the text they fed to AI to make art. Generative artificial intelligence may eliminate the job you got with your diploma still in hand, say executives who offered grim assessments of the entry-level job market last week in multiple forums. Dario Amodei, CEO of Anthropic, which makes the multifunctional AI model Claude, told Axios last week that he believes that AI could cut half of all entry-level white-collar jobs and send overall unemployment rocketing to 20% within the next five years. One explanation why an AI company CEO might make such a dire prediction is to hype the capabilities of his product.

What message does Ukraine's Operation Spider's Web send to Russia and US? Ukraine carries out large-scale drone strikes on multiple Russian airbases.Read more Eighteen months in the making, Ukraine's Operation Spider's Web saw hundreds of AI-trained drones target military aircraft deep inside Russia's borders. Ukrainian President Volodymyr Zelenskyy says Sunday's attacks will go down in history. He followed them up with a proposal for an unconditional ceasefire as the two sides met in Istanbul. The European Union is preparing its 18th package of sanctions on Russia, while US President Donald Trump has threatened to use "devastating" measures against Russia if he feels the time is right. So, is the time right now?

Lanyard-clad attendees with branded tote bags and pink-shirted volunteers flowed through London's Brick Lane on Monday, marking the launch of the inaugural SXSW London festival. Taking place over multiple stages and venues in Shoreditch and Hoxton, SXSW London has officially kicked off its first full day of panels, keynotes, demonstrations, movie premieres, and music gigs. And luckily, Londoners are no strangers to a queue, with SXSW's penchant for long lines outside Austin venues replicated in the UK capital. Playing to the strengths of fellow conferences, the biggest topics of SXSW London are the impact of AI on essentially anything you could think of, the creator economy and online communities, and self-driving tech -- I spied a Wayve autonomous vehicle carefully navigating the pedestrian-filled Brick Lane (with a human driver behind the wheel, just in case). London mayor Sadiq Khan officially launched the festival with a speech Monday morning, championing London as "a global centre for AI investment and innovation," emphasising a focus on ethical and accessible AI development, and playing to the audience with a ChatGPT anecdote.

The data deluge comes with high demands for data labeling. Crowdsourcing (or, more generally, ensemble learning) techniques aim to produce accurate labels via integrating noisy, non-expert labeling from annotators. The classic Dawid-Skene estimator and its accompanying expectation maximization (EM) algorithm have been widely used, but the theoretical properties are not fully understood. Tensor methods were proposed to guarantee identification of the Dawid-Skene model, but the sample complexity is a hurdle for applying such approaches--since the tensor methods hinge on the availability of third-order statistics that are hard to reliably estimate given limited data. In this paper, we propose a framework using pairwise co-occurrences of the annotator responses, which naturally admits lower sample complexity. We show that the approach can identify the Dawid-Skene model under realistic conditions. We propose an algebraic algorithm reminiscent of convex geometry-based structured matrix factorization to solve the model identification problem efficiently, and an identifiability-enhanced algorithm for handling more challenging and critical scenarios. Experiments show that the proposed algorithms outperform the state-of-art algorithms under a variety of scenarios.

This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. It is constructed such that unimodal models struggle and only multimodal models can succeed: difficult examples ("benign confounders") are added to the dataset to make it hard to rely on unimodal signals. The task requires subtle reasoning, yet is straightforward to evaluate as a binary classification problem. We provide baseline performance numbers for unimodal models, as well as for multimodal models with various degrees of sophistication. We find that state-of-the-art methods perform poorly compared to humans, illustrating the difficulty of the task and highlighting the challenge that this important problem poses to the community.

Graph Neural Networks (GNNs) often perform better for high-degree nodes than low-degree nodes on node classification tasks. This degree bias can reinforce social marginalization by, e.g., privileging celebrities and other high-degree actors in social networks during social and content recommendation. While researchers have proposed numerous hypotheses for why GNN degree bias occurs, we find via a survey of 38 degree bias papers that these hypotheses are often not rigorously validated, and can even be contradictory. Thus, we provide an analysis of the origins of degree bias in message-passing GNNs with different graph filters. We prove that high-degree test nodes tend to have a lower probability of misclassification regardless of how GNNs are trained. Moreover, we show that degree bias arises from a variety of factors that are associated with a node's degree (e.g., homophily of neighbors, diversity of neighbors). Furthermore, we show that during training, some GNNs may adjust their loss on low-degree nodes more slowly than on high-degree nodes; however, with sufficiently many epochs of training, message-passing GNNs can achieve their maximum possible training accuracy, which is not significantly limited by their expressive power. Throughout our analysis, we connect our findings to previouslyproposed hypotheses for the origins of degree bias, supporting and unifying some while drawing doubt to others. We validate our theoretical findings on 8 common real-world networks, and based on our theoretical and empirical insights, describe a roadmap to alleviate degree bias.

But Tinder has sparked controversy this week, following the launch of its latest feature. The dating app has quietly started testing a height filter. Spotted within the Premium Discovery section of Tinder's Settings, the tool allows users to specify the minimum and maximum heights for their matches. Posting a screenshot to Reddit, user @Extra_Barracudaaaa wrote: 'Oh God. They add a height filter.'

The approval document will be shared upon request.