Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, protein structures, and connectomes. How do we evaluate the samples these models generate? This work aims to provide an accessible entry point to understanding popular notions of statistical distances, requiring only foundational knowledge in mathematics and statistics. We focus on four commonly used notions of statistical distances representing different methodologies: Using low-dimensional projections (Sliced-Wasserstein; SW), obtaining a distance using classifiers (Classifier Two-Sample Tests; C2ST), using embeddings through kernels (Maximum Mean Discrepancy; MMD), or neural networks (Fr\'echet Inception Distance; FID). We highlight the intuition behind each distance and explain their merits, scalability, complexity, and pitfalls. To demonstrate how these distances are used in practice, we evaluate generative models from different scientific domains, namely a model of decision making and a model generating medical images. We showcase that distinct distances can give different results on similar data. Through this guide, we aim to help researchers to use, interpret, and evaluate statistical distances for generative models in science.

Bernie Moreno, a Republican U.S. Senate candidate from Ohio, discusses the GOP's eagerness to retake the Senate in November, the illegal immigration crisis and Nikki Haley's refusal to drop out of the primary race. Republican Ohio Senate candidate Bernie Moreno is blasting the Associated Press after a story published days before the primary election linking him to an adult online dating site, which a former intern has taken credit for creating, was called into question by the dating site's founder. On Friday, a post on X from one of the founders of the online site Adult Friend Finder, who says he wrote "most of the early code," seemingly rejected a key aspect of an Associated Press report days earlier that suggested "geolocation data," which is commonly understood as involving an IP address or GPS, linked the account to the area of a Moreno family home. "I reviewed all the available information and it showed that the account had only a single visit, no activity, no profile photo, consistent with a prank or someone just checking out the site," Andrew Conru, the engineer who founded Adult Friend Finder, wrote on social media. "The AP report seeming to claim that the available data proves the account was created in Florida is inaccurate, as location information is manually entered during the signup (sic) process. In reality, there appears to be no public geolocation data tied to the account."

It's hard to know whether this performative strain in tech culture reflects something essential about the industry. Maybe its leaders are just unusually visible, because the legacy media are more interested in them, or because they figure so prominently on the social-media platforms that they operate. Or maybe a few outlier personalities--Musk in particular--are responsible for most of the soap-opera vibes. It could also be the general cultural atmosphere. Over the past 20 years, a fashion for aggrieved and confrontational behavior has migrated out of reality television into the wider entertainment and business worlds, and also into politics, in the person of Donald Trump. If the tech titans weren't so self-serious, their bad behavior might simply blend into this broader coarsening.

Google has "interfered" with major elections in the United States 41 times over the last 16 years, according to a new study from the Media Research Center. "MRC researchers have found 41 times where Google interfered in elections over the last 16 years, and its impact has surged dramatically, making it evermore harmful to democracy. In every case, Google harmed the candidates – regardless of party – who threatened its left-wing candidate of choice," MRC Free Speech America vice president Dan Schneider and editor Gabriela Pariseau wrote in a summary of their findings. "From the mouths of Google executives, the tech giant let slip what was never meant to be made public: That Google uses its "great strength and resources and reach" to advance its leftist values," they continued. "Google's outsized influence on information technology, the body politic and American elections became evident in 2008. After failing to prevent then-candidate for president Donald Trump from being inaugurated following the 2016 election, Google has since made clear to any discerning observer that it has been -- and will continue -- interfering in America's elections."

Rep. Jay Obernolte was selected to lead the House task force on AI. Fox News Digital speaks with the California Republican about his goals for the panel and his own thoughts about the rapidly advancing technology. EXCLUSIVE: The chairman of the House of Representatives' new AI Task Force said his panel will likely hold hearings on artificial intelligence as Congress seeks to get ahead of the rapidly advancing technology. "Our number one task is to, by the end of the year, issue a report that details a regulatory framework for artificial intelligence. That framework is going to have a number of different pillars. And those pillars will come out of the things that our task force members are concerned about," Rep. Jay Obernolte, R-Calif., told Fox News Digital.

South Korean President Yoon Suk-yeol on Monday called fake news and disinformation based on AI and digital technology threats to democracy, as some officials attending a global summit accused Russia and China of conducting malicious propaganda campaigns. Speaking at the opening of the Summit for Democracy being held in Seoul, Yoon said countries had a duty to share experiences and wisdom so that artificial intelligence and technology could be employed to promote democracy. "Fake news and disinformation based on artificial intelligence and digital technology not only violates individual freedom and human rights but also threatens democratic systems," Yoon said.

Neural Radiance Fields (NeRFs) quickly evolved as the new de-facto standard for the task of novel view synthesis when trained on a set of RGB images. In this paper, we conduct a comprehensive evaluation of neural scene representations, such as NeRFs, in the context of multi-modal learning. Specifically, we present four different strategies of how to incorporate a second modality, other than RGB, into NeRFs: (1) training from scratch independently on both modalities; (2) pre-training on RGB and fine-tuning on the second modality; (3) adding a second branch; and (4) adding a separate component to predict (color) values of the additional modality. We chose thermal imaging as second modality since it strongly differs from RGB in terms of radiosity, making it challenging to integrate into neural scene representations. For the evaluation of the proposed strategies, we captured a new publicly available multi-view dataset, ThermalMix, consisting of six common objects and about 360 RGB and thermal images in total. We employ cross-modality calibration prior to data capturing, leading to high-quality alignments between RGB and thermal images. Our findings reveal that adding a second branch to NeRF performs best for novel view synthesis on thermal images while also yielding compelling results on RGB. Finally, we also show that our analysis generalizes to other modalities, including near-infrared images and depth maps. Project page: https://mert-o.github.io/ThermalNeRF/.

Recent methods using Reinforcement Learning (RL) have proven to be successful for training intelligent agents in unknown environments. However, RL has not been applied widely in real-world robotics scenarios. This is because current state-of-the-art RL methods require large amounts of data to learn a specific task, leading to unreasonable costs when deploying the agent to collect data in real-world applications. In this paper, we build from existing work that reshapes the reward function in RL by introducing a Control Lyapunov Function (CLF), which is demonstrated to reduce the sample complexity. Still, this formulation requires knowing a CLF of the system, but due to the lack of a general method, it is often a challenge to identify a suitable CLF. Existing work can compute low-dimensional CLFs via a Hamilton-Jacobi reachability procedure. However, this class of methods becomes intractable on high-dimensional systems, a problem that we address by using a system decomposition technique to compute what we call Decomposed Control Lyapunov Functions (DCLFs). We use the computed DCLF for reward shaping, which we show improves RL performance. Through multiple examples, we demonstrate the effectiveness of this approach, where our method finds a policy to successfully land a quadcopter in less than half the amount of real-world data required by the state-of-the-art Soft-Actor Critic algorithm.

There is currently considerable excitement within government about the potential of artificial intelligence to improve public service productivity through the automation of complex but repetitive bureaucratic tasks, freeing up the time of skilled staff. Here, we explore the size of this opportunity, by mapping out the scale of citizen-facing bureaucratic decision-making procedures within UK central government, and measuring their potential for AI-driven automation. We estimate that UK central government conducts approximately one billion citizen-facing transactions per year in the provision of around 400 services, of which approximately 143 million are complex repetitive transactions. We estimate that 84% of these complex transactions are highly automatable, representing a huge potential opportunity: saving even an average of just one minute per complex transaction would save the equivalent of approximately 1,200 person-years of work every year. We also develop a model to estimate the volume of transactions a government service undertakes, providing a way for government to avoid conducting time consuming transaction volume measurements. Finally, we find that there is high turnover in the types of services government provide, meaning that automation efforts should focus on general procedures rather than services themselves which are likely to evolve over time. Overall, our work presents a novel perspective on the structure and functioning of modern government, and how it might evolve in the age of artificial intelligence.

The problem of online social network manipulation for community canvassing is of real concern in today's world. Motivated by the study of voter models, opinion and polarization dynamics on networks, we model community canvassing as a dynamic process over a network enabled via gradient-based attacks on GNNs. Existing attacks on GNNs are all single-step and do not account for the dynamic cascading nature of information diffusion in networks. We consider the realistic scenario where an adversary uses a GNN as a proxy to predict and manipulate voter preferences, especially uncertain voters. Gradient-based attacks on the GNN inform the adversary of strategic manipulations that can be made to proselytize targeted voters. In particular, we explore $\textit{minimum budget attacks for community canvassing}$ (MBACC). We show that the MBACC problem is NP-Hard and propose Dynamic Multi-Step Adversarial Community Canvassing (MAC) to address it. MAC makes dynamic local decisions based on the heuristic of low budget and high second-order influence to convert and perturb target voters. MAC is a dynamic multi-step attack that discovers low-budget and high-influence targets from which efficient cascading attacks can happen. We evaluate MAC against single-step baselines on the MBACC problem with multiple underlying networks and GNN models. Our experiments show the superiority of MAC which is able to discover efficient multi-hop attacks for adversarial community canvassing. Our code implementation and data is available at https://github.com/saurabhsharma1993/mac.