Senator Charles Schumer (D-NY) once again played host to Silicon Valley's AI leaders on Tuesday as the US Senate reconvened its AI Insights Forum for a second time. On the guest list this go around: manifesto enthusiast Marc Andreessen and venture capitalist John Doerr, as well as Max Tegmark of the Future of Life Institute and NAACP CEO Derrick Johnson. On the agenda: "the transformational innovation that pushes the boundaries of medicine, energy, and science, and the sustainable innovation necessary to drive advancements in security, accountability, and transparency in AI," according to a release from Sen. Schumer's office. Upon exiting the meeting Tuesday, Schumer told the assembled press, "it is clear that American leadership on AI can't be done on the cheap. Almost all of the experts in today's Forum called for robust, sustained federal investment in private and public sectors to achieve our goals of American-led transformative and sustainable innovation in AI. Per National Security AI Commission estimates, paying for that could cost around $32 billion a year. However, Schumer believes that those funding challenges can be addressed by "leveraging the private sector by employing new and innovative funding mechanisms – like the Grand Challenges prize idea." "We must prioritize transformational innovation, to help create new vistas, unlock new cures, improve education, reinforce national security, protect the global food supply, and more," Schumer remarked. But in doing so, we must act sustainably in order to minimize harms to workers, civil society and the environment. "We need to strike a balance between transformational and sustainable innovation," Schumer said. "Finding this balance will be key to our success." Senators Brian Schatz (D-HI) and John Kennedy (R-LA) also got in on the proposed regulatory action Tuesday, introducing legislation that would provide more transparency on AI-generated content by requiring clear labeling and disclosures. Such technology could resemble the Content Credentials tag that the C2PA and CAI industry advocacy groups are developing. "Our bill is simple," Senator Schatz said in a press statement. "If any content is made by artificial intelligence, it should be labeled so that people are aware and aren't fooled or scammed." The Schatz-Kennedy AI Labeling Act, as they're calling it, would require generative AI system developers to clearly and conspicuously disclose AI-generated content to users. Those developers, and their licensees, would also have to take "reasonable steps" to prevent "systematic publication of content without disclosures." The bill would also establish a working group to create non-binding technical standards to help social media platforms automatically identify such content as well. "It puts the onus where it belongs: on the companies and not the consumers," Schatz said on the Senate floor Tuesday. "Labels will help people to be informed.

Russian Defence Minister Sergei Shoigu has visited a command post near the front lines in eastern Ukraine as fighting in the region intensifies in advance of the harsh winter season. He travelled to the "Vostok" command post in the east to be briefed on developments at the front as Russian forces stepped up attacks, according to footage posted by the Ministry of Defence on Wednesday. The minister was briefed on preparations for combat for the forthcoming winter and the training of drone operators, the Ministry of Defence said, according to AFP. "The situation today suggests the enemy has fewer and fewer opportunities. And they will continue to be reduced, thanks exclusively to your combat work," Shoigu told Russian soldiers as he sought to raise morale. The Ministry of Defence, whose video showed Shoigu arriving at the post via helicopter, added that the minister "drew special attention to the timely and sufficient provision of new winter uniforms and insulated footwear for all personnel" before winter, when temperatures plunge below freezing.

Russian Defence Minister Sergei Shoigou has visited a command post near the front lines in eastern Ukraine as fighting in the region intensifies in advance of the harsh winter season. He travelled to the "Vostok" command post in the east to be briefed on developments at the front as Russian forces stepped up attacks, according to footage posted by the Ministry of Defence on Wednesday. The minister was briefed on preparations for combat for the forthcoming winter and the training of drone operators, the Ministry of Defence said, according to AFP. "The situation today suggests the enemy has fewer and fewer opportunities. And they will continue to be reduced, thanks exclusively to your combat work," Shoigou told Russian soldiers as he sought to raise morale. The Ministry of Defence, whose video showed Shoigou arriving at the post via helicopter, added that the minister "drew special attention to the timely and sufficient provision of new winter uniforms and insulated footwear for all personnel" before winter, when temperatures plunge below freezing.

Robocalls are rampant, using AI and other tools to disrupt day-to-day life and scam Americans out of their money through impersonations of family members, phone providers and more. On October 24, the Senate Commerce Committee's Subcommittee on Communications, Media, and Broadband heard the latest issue and solution floating around: AI. Currently, bad actors are using AI to steal people's voices and repurpose them in calls to loved ones -- often presenting a state of distress. This advancement goes beyond seemingly real calls from banks and credit card companies, providing a disturbing and jarring experience: not knowing if you're speaking to someone you know. The financial repercussions (not to mention potential mental distress) are tremendous. Senator Ben Ray Luján, chair of the subcommittee, estimates that individuals nationwide receive 1.5 billion to 3 billion scam calls monthly, defrauding Americans out of $39 billion in 2022.

The "worst nightmares" about artificial intelligence-generated child sexual abuse images are coming true and threaten to overwhelm the internet, a safety watchdog has warned. The Internet Watch Foundation (IWF) said it had found nearly 3,000 AI-made abuse images that broke UK law. The UK-based organisation said existing images of real-life abuse victims were being built into AI models, which then produce new depictions of them. It added that the technology was also being used to create images of celebrities who have been "de-aged" and then depicted as children in sexual abuse scenarios. Other examples of child sexual abuse material (CSAM) included using AI tools to "nudify" pictures of clothed children found online.

With the advent of vehicles equipped with advanced driver-assistance systems, such as adaptive cruise control (ACC) and other automated driving features, the potential for cyberattacks on these automated vehicles (AVs) has emerged. While overt attacks that force vehicles to collide may be easily identified, more insidious attacks, which only slightly alter driving behavior, can result in network-wide increases in congestion, fuel consumption, and even crash risk without being easily detected. To address the detection of such attacks, we first present a traffic model framework for three types of potential cyberattacks: malicious manipulation of vehicle control commands, false data injection attacks on sensor measurements, and denial-of-service (DoS) attacks. We then investigate the impacts of these attacks at both the individual vehicle (micro) and traffic flow (macro) levels. A novel generative adversarial network (GAN)-based anomaly detection model is proposed for real-time identification of such attacks using vehicle trajectory data. We provide numerical evidence {to demonstrate} the efficacy of our machine learning approach in detecting cyberattacks on ACC-equipped vehicles. The proposed method is compared against some recently proposed neural network models and observed to have higher accuracy in identifying anomalous driving behaviors of ACC vehicles.

This study explores the capabilities of prompt-driven Large Language Models (LLMs) like ChatGPT and GPT-4 in adhering to human guidelines for dialogue summarization. Experiments employed DialogSum (English social conversations) and DECODA (French call center interactions), testing various prompts: including prompts from existing literature and those from human summarization guidelines, as well as a two-step prompt approach. Our findings indicate that GPT models often produce lengthy summaries and deviate from human summarization guidelines. However, using human guidelines as an intermediate step shows promise, outperforming direct word-length constraint prompts in some cases. The results reveal that GPT models exhibit unique stylistic tendencies in their summaries. While BERTScores did not dramatically decrease for GPT outputs suggesting semantic similarity to human references and specialised pre-trained models, ROUGE scores reveal grammatical and lexical disparities between GPT-generated and human-written summaries. These findings shed light on the capabilities and limitations of GPT models in following human instructions for dialogue summarization.

Both approaches enable sampling and density estimation of conditional probability distributions, which are core tasks in Bayesian inference. Our methods represent the target conditional distributions as transformations of a tractable reference distribution and, therefore, fall into the framework of measure transport. COT maps are a canonical choice within this framework, with desirable properties such as uniqueness and monotonicity. However, the associated COT problems are computationally challenging, even in moderate dimensions. To improve the scalability, our numerical algorithms leverage neural networks to parameterize COT maps. Our methods exploit the structure of the static and dynamic formulations of the COT problem. PCP-Map models conditional transport maps as the gradient of a partially input convex neural network (PICNN) and uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives. COT-Flow models conditional transports via the flow of a regularized neural ODE; it is slower to train but offers faster sampling. We demonstrate their effectiveness and efficiency by comparing them with state-of-the-art approaches using benchmark datasets and Bayesian inverse problems.

Despite their groundbreaking performance for many generative modeling tasks, diffusion models have fallen short on discrete data domains such as natural language. Crucially, standard diffusion models rely on the well-established theory of score matching, but efforts to generalize this to discrete structures have not yielded the same empirical gains. In this work, we bridge this gap by proposing score entropy, a novel discrete score matching loss that is more stable than existing methods, forms an ELBO for maximum likelihood training, and can be efficiently optimized with a denoising variant. We scale our Score Entropy Discrete Diffusion models (SEDD) to the experimental setting of GPT-2, achieving highly competitive likelihoods while also introducing distinct algorithmic advantages. In particular, when comparing similarly sized SEDD and GPT-2 models, SEDD attains comparable perplexities (normally within $+10\%$ of and sometimes outperforming the baseline). Furthermore, SEDD models learn a more faithful sequence distribution (around $4\times$ better compared to GPT-2 models with ancestral sampling as measured by large models), can trade off compute for generation quality (needing only $16\times$ fewer network evaluations to match GPT-2), and enables arbitrary infilling beyond the standard left to right prompting.

Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this paper we propose the tensor mixed-membership blockmodel, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the identifiability of our model and propose a computationally efficient estimation procedure based on the higher-order orthogonal iteration algorithm (HOOI) for tensor SVD composed with a simplex corner-finding algorithm. We then demonstrate the consistency of our estimation procedure by providing a per-node error bound, which showcases the effect of higher-order structures on estimation accuracy. To prove our consistency result, we develop the $\ell_{2,\infty}$ tensor perturbation bound for HOOI under independent, possibly heteroskedastic, subgaussian noise that may be of independent interest. Our analysis uses a novel leave-one-out construction for the iterates, and our bounds depend only on spectral properties of the underlying low-rank tensor under nearly optimal signal-to-noise ratio conditions such that tensor SVD is computationally feasible. Whereas other leave-one-out analyses typically focus on sequences constructed by analyzing the output of a given algorithm with a small part of the noise removed, our leave-one-out analysis constructions use both the previous iterates and the additional tensor structure to eliminate a potential additional source of error. Finally, we apply our methodology to real and simulated data, including applications to two flight datasets and a trade network dataset, demonstrating some effects not identifiable from the model with discrete community memberships.