As the artificial intelligence (AI) supply chain grows more complex, AI systems and models are increasingly likely to incorporate externally-sourced ingredients such as datasets and other models. In such cases, determining whether or not an AI system or model complies with the EU AI Act will require gathering compliance-related metadata about both the AI system or model at-large as well as those externally-supplied ingredients. There must then be an analysis that looks across all of this metadata to render a prediction about the compliance of the overall AI system or model. Up until now, this process has not been automated. Thus, it has not been possible to make real-time compliance determinations in scenarios where doing so would be advantageous, such as the iterative workflows of today's AI developers, search and acquisition of AI ingredients on communities like Hugging Face, federated and continuous learning, and more. To address this shortcoming, we introduce a highly automated system for AI Act compliance analysis. This system has two key elements. First is an interlocking set of computational artifacts that capture compliance-related metadata about both: (1) the AI system or model at-large; (2) any constituent ingredients such as datasets and models. Second is an automated analysis algorithm that operates across those computational artifacts to render a run-time prediction about whether or not the overall AI system or model complies with the AI Act. Working together, these elements promise to enhance and accelerate AI Act compliance assessments.

Large language models such as ChatGPT often exhibit striking political biases. If users query them about political information, they might take a normative stance and reinforce such biases. To overcome this, we align LLMs with diverse political viewpoints from 100,000 comments written by candidates running for national parliament in Switzerland. Such aligned models are able to generate more accurate political viewpoints from Swiss parties compared to commercial models such as ChatGPT. We also propose a procedure to generate balanced overviews from multiple viewpoints using such models.

The Universal Dependencies (UD) project aims to create a cross-linguistically consistent dependency annotation for multiple languages, to facilitate multilingual NLP. It currently supports 114 languages. Dravidian languages are spoken by over 200 million people across the word, and yet there are only two languages from this family in UD. This paper examines some of the morphological and syntactic features of Dravidian languages and explores how they can be annotated in the UD framework.

Large language models (LLMs) are increasingly being used in human-centered social scientific tasks, such as data annotation, synthetic data creation, and engaging in dialog. However, these tasks are highly subjective and dependent on human factors, such as one's environment, attitudes, beliefs, and lived experiences. Thus, employing LLMs (which do not have such human factors) in these tasks may result in a lack of variation in data, failing to reflect the diversity of human experiences. In this paper, we examine the role of prompting LLMs with human-like personas and asking the models to answer as if they were a specific human. This is done explicitly, with exact demographics, political beliefs, and lived experiences, or implicitly via names prevalent in specific populations. The LLM personas are then evaluated via (1) subjective annotation task (e.g., detecting toxicity) and (2) a belief generation task, where both tasks are known to vary across human factors. We examine the impact of explicit vs. implicit personas and investigate which human factors LLMs recognize and respond to. Results show that LLM personas show mixed results when reproducing known human biases, but generate generally fail to demonstrate implicit biases. We conclude that LLMs lack the intrinsic cognitive mechanisms of human thought, while capturing the statistical patterns of how people speak, which may restrict their effectiveness in complex social science applications.

This paper focuses on a Vision-based Landing task and presents the design and the validation of a dataset that would comply with the Operational Design Domain (ODD) of a Machine-Learning (ML) system. Relying on emerging certification standards, we describe the process for establishing ODDs at both the system and image levels. In the process, we present the translation of high-level system constraints into actionable image-level properties, allowing for the definition of verifiable Data Quality Requirements (DQRs). To illustrate this approach, we use the Landing Approach Runway Detection (LARD) dataset which combines synthetic imagery and real footage, and we focus on the steps required to verify the DQRs. The replicable framework presented in this paper addresses the challenges of designing a dataset compliant with the stringent needs of ML-based systems certification in safety-critical applications.

Retrieval-Augmented Generation (RAG) improves pre-trained models by incorporating external knowledge at test time to enable customized adaptation. We study the risk of datastore leakage in Retrieval-In-Context RAG Language Models (LMs). We show that an adversary can exploit LMs' instruction-following capabilities to easily extract text data verbatim from the datastore of RAG systems built with instruction-tuned LMs via prompt injection. The vulnerability exists for a wide range of modern LMs that span Llama2, Mistral/Mixtral, Vicuna, SOLAR, WizardLM, Qwen1.5, and Platypus2, and the exploitability exacerbates as the model size scales up. Extending our study to production RAG models GPTs, we design an attack that can cause datastore leakage with a 100% success rate on 25 randomly selected customized GPTs with at most 2 queries, and we extract text data verbatim at a rate of 41% from a book of 77,000 words and 3% from a corpus of 1,569,000 words by prompting the GPTs with only 100 queries generated by themselves.

Stress and material deformation field predictions are among the most important tasks in computational mechanics. These predictions are typically made by solving the governing equations of continuum mechanics using finite element analysis, which can become computationally prohibitive considering complex microstructures and material behaviors. Machine learning (ML) methods offer potentially cost effective surrogates for these applications. However, existing ML surrogates are either limited to low-dimensional problems and/or do not provide uncertainty estimates in the predictions. This work proposes an ML surrogate framework for stress field prediction and uncertainty quantification for diverse materials microstructures. A modified Bayesian U-net architecture is employed to provide a data-driven image-to-image mapping from initial microstructure to stress field with prediction (epistemic) uncertainty estimates. The Bayesian posterior distributions for the U-net parameters are estimated using three state-of-the-art inference algorithms: the posterior sampling-based Hamiltonian Monte Carlo method and two variational approaches, the Monte-Carlo Dropout method and the Bayes by Backprop algorithm. A systematic comparison of the predictive accuracy and uncertainty estimates for these methods is performed for a fiber reinforced composite material and polycrystalline microstructure application. It is shown that the proposed methods yield predictions of high accuracy compared to the FEA solution, while uncertainty estimates depend on the inference approach. Generally, the Hamiltonian Monte Carlo and Bayes by Backprop methods provide consistent uncertainty estimates. Uncertainty estimates from Monte Carlo Dropout, on the other hand, are more difficult to interpret and depend strongly on the method's design.

Hezbollah chief Hassan Nasrallah has issued a stern warning to Israel, threatening a war with "no restraint and no rules and no ceilings" in case of a major Israeli offensive against Lebanon. Nasrallah's remarks on Wednesday come amid soaring tensions at the Lebanon-Israel border after Israeli officials reiterated that the country is ready for an all-out war against Hezbollah. "All what the enemy says and the threats and warnings the mediators bring – and what is being said in the Israeli media – about a war in Lebanon does not scare us," Nasrallah said in a speech via video feed. He said that Israel is the party that should be "scared". Israeli Foreign Israeli Foreign Minister Israel Katz on Tuesday raised the prospect of a major conflict with the Lebanese group after Hezbollah released surveillance drone footage showing major infrastructure and military sites in northern Israel.

On January 21, Patricia Gingrich was about to sit down for dinner when her landline phone rang. The New Hampshire voter picked up and heard a voice telling her not to vote in the upcoming presidential primary. "As I listened to it, I thought, gosh, that sounds like Joe Biden," Gingrich told Al Jazeera. "But the fact that he was saying to save your vote, don't use it in this next election -- I knew Joe Biden would never say that." The voice may have sounded like the United States president, but it wasn't him: It was a deepfake, generated by artificial intelligence (AI).

Former Secretary of State Mike Pompeo joins'The Story' to discuss what the U.S. can do to mitigate the threat Hezbollah poses to Israel. The Israel Defense Forces' announcement that it has approved plans for an "offensive in Lebanon" is "another step towards a significant escalation" against the terrorist group Hezbollah, a former IDF spokesperson tells Fox News Digital. The IDF released a photo this week showing a meeting of top generals discussing war strategy, noting that "as part of the situational assessment, operational plans for an offensive in Lebanon were approved and validated, and decisions were taken on the continuation of increasing the readiness of troops in the field." "What this means is another step towards a significant escalation. The IDF has been preparing troops for combat scenarios in Lebanon for months and what it means that now it has also approved plans, strategic maneuver plans, of how to attack Hezbollah in Lebanon on the ground and of course in the air," Jonathan Conricus, a senior fellow at the Foundation for Defense of Democracies and a former IDF spokesman, told Fox News Digital on Wednesday.