This report describes trade-offs in the design of international governance arrangements for civilian artificial intelligence (AI) and presents one approach in detail. This approach represents the extension of a standards, licensing, and liability regime to the global level. We propose that states establish an International AI Organization (IAIO) to certify state jurisdictions (not firms or AI projects) for compliance with international oversight standards. States can give force to these international standards by adopting regulations prohibiting the import of goods whose supply chains embody AI from non-IAIO-certified jurisdictions. This borrows attributes from models of existing international organizations, such as the International Civilian Aviation Organization (ICAO), the International Maritime Organization (IMO), and the Financial Action Task Force (FATF). States can also adopt multilateral controls on the export of AI product inputs, such as specialized hardware, to non-certified jurisdictions. Indeed, both the import and export standards could be required for certification. As international actors reach consensus on risks of and minimum standards for advanced AI, a jurisdictional certification regime could mitigate a broad range of potential harms, including threats to public safety.

Large language models (LLM), based on generative pre-trained transformers (GPT), such as ChatGPT show high efficiency in the analysis of complex texts. These days, we can observe the emerging of many new smaller open source LLMs, e.g. Llama, Falcon, GPT4All, GPT-J, etc. Open source LLMs can be fine-tuned for specific custom problems and deployed on custom servers, e.g. in cloud computing services such as AWS, GCP. LLMs have some new features as compared to conventional language models based on transformers. One of them is zero-shot and few-shot learning, which consists in good performance of the model when we show it only few training examples or even no examples at all, but only the instructions describing what should be done. Another important feature is the reasoning when a model can generate new patterns and conclusions which are based on an input prompt and facts known by the model and which were not included into it directly during a training process. So, the model can generate analytical texts with unexpected but useful chains of thoughts. One of the approaches of using LLMs is based on retrieval augmented generation (RAG), which uses the results from other services e.g.

We compare different methods for extracting The multimodal affective computing field has seen audio features as well as different fusion significant advances in feature extraction and multimodal network methods to combine audio and text signals fusion methodologies in recent years. By to identify the best-performing procedures. We find combining audio, text and visual signals, these that the addition of audio signals consistently improves models offer a more comprehensive, nuanced understanding performance and also that our transformer of human emotions. However, there fusion network further enhances results for most are still limitations: hand-crafted feature extraction metrics and achieves state-of-the-art results across algorithms often lack flexibility and generalization all datasets, indicating its efficacy in enhancing across diverse tasks. To overcome these limitations, cross-modality modeling and its potential for multimodal recent studies have proposed fully end-to-end models emotion detection. From multi-loss training, that optimize both feature extraction and learning we also observe that 1) using distinct labels for processes jointly (Dai et al., 2021). Our work each modality in multi-loss training significantly extracts feature representations from pre-trained benefits the models' performance, and 2) training models for different modalities and combines them on multimodal features improves not only the overall in an end-to-end manner, which provides a comprehensive model performance but also the model's accuracy and adaptable solution for multimodal on the single-modality subnet.

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and complement RLHF in practice; and (3) propose auditing and disclosure standards to improve societal oversight of RLHF systems. Our work emphasizes the limitations of RLHF and highlights the importance of a multi-faceted approach to the development of safer AI systems.

ABSTRACT Computational models help decision makers understand epidemic dynamics to optimize public health interventions. Agent-based simulation of disease spread in synthetic populations allows us to compare and contrast different effects across identical populations or to investigate the effect of interventions keeping every other factor constant between "digital twins". In particular, we can observe the behavior of two different diseases as they evolve from identical initial conditions in the same population. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an agent-based modeling system with a geospatial perspective using a synthetic population that is constructed based on the U.S. census data. Having synthetic data provides a baseline to get comparable results from different conditions and interventions. In this paper we show how Gaussian process regression can be used on FRED-synthesized data to infer the differing spatial dispersion of the epidemic dynamics for two disease conditions that start from the same initial conditions and spread among identical populations. Our results showcase the utility of agent-based simulations frameworks such as FRED for inferring differences between conditions where controlling for all confounding factors for such comparisons is next to impossible without synthetic data.

Simulating and predicting water levels in river systems is essential for flood warnings, hydraulic operations, and flood mitigations. In the engineering field, tools such as HEC-RAS, MIKE, and SWMM are used to build detailed physics-based hydrological and hydraulic computational models to simulate the entire watershed, thereby predicting the water stage at any point in the system. However, these physics-based models are computationally intensive, especially for large watersheds and for longer simulations. To overcome this problem, we train several deep learning (DL) models for use as surrogate models to rapidly predict the water stage. The downstream stage of the Miami River in South Florida is chosen as a case study for this paper. The dataset is from January 1, 2010, to December 31, 2020, downloaded from the DBHYDRO database of the South Florida Water Management District (SFWMD). Extensive experiments show that the performance of the DL models is comparable to that of the physics-based models, even during extreme precipitation conditions (i.e., tropical storms). Furthermore, we study the decline in prediction accuracy of the DL models with an increase in prediction lengths. In order to predict the water stage in the future, our DL models use measured variables of the river system from the recent past as well as covariates that can be reliably predicted in the near future. In summary, the deep learning models achieve comparable or better error rates with at least 1000x speedup in comparison to the physics-based models.

We give an algorithm that efficiently learns a quantum state prepared by Clifford gates and $O(\log(n))$ non-Clifford gates. Specifically, for an $n$-qubit state $\lvert \psi \rangle$ prepared with at most $t$ non-Clifford gates, we show that $\mathsf{poly}(n,2^t,1/\epsilon)$ time and copies of $\lvert \psi \rangle$ suffice to learn $\lvert \psi \rangle$ to trace distance at most $\epsilon$. This result follows as a special case of an algorithm for learning states with large stabilizer dimension, where a quantum state has stabilizer dimension $k$ if it is stabilized by an abelian group of $2^k$ Pauli operators. We also develop an efficient property testing algorithm for stabilizer dimension, which may be of independent interest.

We call on the Document AI (DocAI) community to reevaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding Dataset and Evaluation (DUDE) seeks to remediate the halted research progress in understanding visually-rich documents (VRDs). We present a new dataset with novelties related to types of questions, answers, and document layouts based on multi-industry, multi-domain, and multi-page VRDs of various origins, and dates. Moreover, we are pushing the boundaries of current methods by creating multi-task and multi-domain evaluation setups that more accurately simulate real-world situations where powerful generalization and adaptation under low-resource settings are desired. DUDE aims to set a new standard as a more practical, long-standing benchmark for the community, and we hope that it will lead to future extensions and contributions that address real-world challenges. Finally, our work illustrates the importance of finding more efficient ways to model language, images, and layout in DocAI.

Secretary of State Antony Blinken twice declined to criticize Elon Musk after the SpaceX founder said he refused to help the Ukrainian government access his Starlink internet service in order to attack Russia. Blinken was pressed by CNN's Jake Tapper to comment on details in a new book confirmed by Musk, including that he refused the Ukrainian government's requests to activate Starlink, a satellite internet service run by SpaceX, in Crimea so it could launch a submarine drone attack against Russian naval forces. "There was an emergency request from government authorities to activate Starlink all the way to Sevastopol," Musk posted Thursday on X. "The obvious intent being to sink most of the Russian fleet at anchor. If I had agreed to their request, then SpaceX would be explicitly complicit in a major act of war and conflict escalation." Tapper asked Blinken whether Musk should face repercussions after he "effectively sabotaged a military operation by Ukraine, a U.S. ally, against Russia, an aggressor country that invaded a U.S. ally."

A drone attack on an open market south of the Sudanese capital, Khartoum, has killed at least 40 people, activists and medical workers said, as the military and the paramilitary Rapid Support Forces (RSF) battle for control of the country. At least 70 others were injured in the attack in Khartoum's Mayo neighbourhood on Sunday, according to resistance committees and two healthcare workers at the Bashair University Hospital, where the casualties were treated. Many of them will require amputations. The group posted footage on social media showing bodies wrapped in white sheets in an open yard at the hospital. Reporting from Khartoum, Al Jazeera's Hiba Morgan said the drone attack was carried out by the Sudanese army.