Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent semantics between images and texts, and rarely spot the inconsistency among the post contents and background knowledge. In addition, they commonly assume the completeness of multiple modalities and thus are incapable of handling handle missing modalities in real-life scenarios. Motivated by the intuition that rumors in social media are more likely to have inconsistent semantics, a novel Knowledge-guided Dual-consistency Network is proposed to detect rumors with multimedia contents. It uses two consistency detection subnetworks to capture the inconsistency at the cross-modal level and the content-knowledge level simultaneously. It also enables robust multi-modal representation learning under different missing visual modality conditions, using a special token to discriminate between posts with visual modality and posts without visual modality. Extensive experiments on three public real-world multimedia datasets demonstrate that our framework can outperform the state-of-the-art baselines under both complete and incomplete modality conditions. Our codes are available at https://github.com/MengzSun/KDCN.

In the three years since Black Mirror's previous (and somewhat disappointing) season, we've lived through a global pandemic, watched a US president trigger a mob attack on the Capitol, and AI has gone mainstream. We're barreling towards the future faster than ever, but loneliness remains a key issue in modern life. What better time for Charlie Brooker to bring back his feel-bad series for another season? In 2019, I argued that Brooker was running out of things to say with the show, despite his deft ability to predict our tech-infused dystopia with Black Mirror's first few seasons. Something was lost with his transition to Netflix, which led to bigger budgets and more notable stars, but less of the sharp insight that made the show so memorable.

Center for A.I. Safety Director Dan Hendrycks explains concerns about how the rapid growth of artificial intelligence could impact society. People need to change how they're thinking about regulating artificial intelligence, according to a prominent expert in the field, who pushed back on an idea gaining traction among lawmakers to create a new government agency to regulate AI. "Regulation is a really hard question," Andres Sawicki, a professor of law and director of the business of innovation, law, and technology (BILT) concentration at the University of Miami, told Fox News Digital. "The topic of AI is too big to be handled in one big coherent manner." Rather than tackling AI in a sweeping, comprehensive way, Sawicki recommend a more pragmatic, piecemeal approach. "Look specifically and concretely at effects the technology is having, the impact of AI on this or that issue. There shouldn't be a Department of AI to handle this in one big swoop."

Other industry leaders are taking a different tact, blitzing Congress with their vision for how Washington should regulate their companies. Altman in May had private meetings and a dinner with lawmakers, where he demonstrated -- to their amusement -- how ChatGPT could write a speech for them to deliver on the chamber floor. Smith has given legislators a lesson on the technical stack that underpins generative AI models like ChatGPT, including computing infrastructure and applications. And Smith recently unveiled his blueprint for AI regulation at a speech in Washington attended by half a dozen lawmakers.

Scientists at the University of Bristol have shown that reinforcement learning, a type of machine learning in which a computer program learns to make decisions by trying different actions, significantly outperforms commercial blood glucose controllers in terms of safety and effectiveness. By using offline reinforcement learning, where the algorithm learns from patient records, the researchers improve on prior work, showing that good blood glucose control can be achieved by learning from the decisions of the patient rather than by trial and error. Type 1 diabetes is one of the most prevalent auto-immune conditions in the UK and is characterised by an insufficiency of the hormone insulin, which is responsible for blood glucose regulation. Many factors affect a person's blood glucose and therefore it can be a challenging and burdensome task to select the correct insulin dose for a given scenario. Current artificial pancreas devices provide automated insulin dosing but are limited by their simplistic decision-making algorithms.

San Francisco – Billionaire entrepreneur Elon Musk expects his brain-chip startup Neuralink to start its first human trial this year, he said on Friday in France. Speaking at the VivaTech event in Paris, co-founder Musk said Neuralink plans to implant a tetraplegic or paraplegic patient during a webcast. While Musk didn't specify how many patients his company would implant or for how long, "it's looking like the first case will be later this year," said Musk, who is also CEO of electric carmaker Tesla, social media platform Twitter and the SpaceX rocket launch company. Last month, Neuralink said it received U.S. Food and Drug Administration (FDA) clearance for its first-in-human clinical trial, a critical milestone for the startup as it faces U.S. probes over its handling of animal experiments. The FDA acknowledged in an earlier statement that the agency cleared Neuralink to use its brain implant and surgical robot for trials but declined to provide more details.

Our research introduces an innovative Natural Language Generation (NLG) approach that aims to optimize user experience and alleviate the workload of human customer support agents. Our primary objective is to generate informal summaries for online articles and posts using an offline reinforcement learning technique. In our study, we compare our proposed method with existing approaches to text generation and provide a comprehensive overview of our architectural design, which incorporates crawling, reinforcement learning, and text generation modules. By presenting this original approach, our paper makes a valuable contribution to the field of NLG by offering a fresh perspective on generating natural language summaries for online content. Through the implementation of Empowering NLG, we are able to generate higher-quality replies in the online domain. The experimental results demonstrate a significant improvement in the average "like" score, increasing from 0.09954378 to 0.5000152. This advancement has the potential to enhance the efficiency and effectiveness of customer support services and elevate the overall user experience when consuming online content.

We introduce variational sequential Optimal Experimental Design (vsOED), a new method for optimally designing a finite sequence of experiments under a Bayesian framework and with information-gain utilities. Specifically, we adopt a lower bound estimator for the expected utility through variational approximation to the Bayesian posteriors. The optimal design policy is solved numerically by simultaneously maximizing the variational lower bound and performing policy gradient updates. We demonstrate this general methodology for a range of OED problems targeting parameter inference, model discrimination, and goal-oriented prediction. These cases encompass explicit and implicit likelihoods, nuisance parameters, and physics-based partial differential equation models. Our vsOED results indicate substantially improved sample efficiency and reduced number of forward model simulations compared to previous sequential design algorithms.

Self-training (ST) has come to fruition in language understanding tasks by producing pseudo labels, which reduces the labeling bottleneck of language model fine-tuning. Nevertheless, in facilitating semi-supervised controllable language generation, ST faces two key challenges. First, augmented by self-generated pseudo text, generation models tend to over-exploit the previously learned text distribution, suffering from mode collapse and poor generation diversity. Second, generating pseudo text in each iteration is time-consuming, severely decelerating the training process. In this work, we propose KEST, a novel and efficient self-training framework to handle these problems. KEST utilizes a kernel-based loss, rather than standard cross entropy, to learn from the soft pseudo text produced by a shared non-autoregressive generator. We demonstrate both theoretically and empirically that KEST can benefit from more diverse pseudo text in an efficient manner, which allows not only refining and exploiting the previously fitted distribution but also enhanced exploration towards a larger potential text space, providing a guarantee of improved performance. Experiments on three controllable generation tasks demonstrate that KEST significantly improves control accuracy while maintaining comparable text fluency and generation diversity against several strong baselines.

Cyber attacks deceive machines into believing something that does not exist in the first place. However, there are some to which even humans fall prey. One such famous attack that attackers have used over the years to exploit the vulnerability of vision is known to be a Homoglyph attack. It employs a primary yet effective mechanism to create illegitimate domains that are hard to differentiate from legit ones. Moreover, as the difference is pretty indistinguishable for a user to notice, they cannot stop themselves from clicking on these homoglyph domain names. In many cases, that results in either information theft or malware attack on their systems. Existing approaches use simple, string-based comparison techniques applied in primary language-based tasks. Although they are impactful to some extent, they usually fail because they are not robust to different types of homoglyphs and are computationally not feasible because of their time requirement proportional to the string length. Similarly, neural network-based approaches are employed to determine real domain strings from fake ones. Nevertheless, the problem with both methods is that they require paired sequences of real and fake domain strings to work with, which is often not the case in the real world, as the attacker only sends the illegitimate or homoglyph domain to the vulnerable user. Therefore, existing approaches are not suitable for practical scenarios in the real world. In our work, we created GlyphNet, an image dataset that contains 4M domains, both real and homoglyphs. Additionally, we introduce a baseline method for a homoglyph attack detection system using an attention-based convolutional Neural Network. We show that our model can reach state-of-the-art accuracy in detecting homoglyph attacks with a 0.93 AUC on our dataset.