The United States military said a helicopter accident in northeast Syria has left 22 US service members injured, though no enemy fire was reported at the time of the "mishap". "The service members are receiving treatment for their injuries and 10 have been evacuated to higher care facilities outside of the CENTCOM AOR [US Central Command Area of Responsibility]", the US military said in a statement on Monday. An investigation is under way to determine the cause of the accident on Sunday in northeast Syria, US Central Command said in a statement. The statement added that "no enemy fire was reported" at the time of the accident. US forces operating in Syria since 2015 have come under sporadic attacks from ISIL (ISIS) fighters and Iranian-backed groups operating in the country.

Investigative reporter Michael Shellenberger shares details from sources who allege that the U.S. government has "non-human spacecraft" on "Jesse Watters Primetime." Members of the House Armed Services Committee are demanding several updates from the Pentagon on whether it is effectively using artificial intelligence to defend against growing threats from China and other adversaries. The committee on Monday released its annual proposal for the defense policy bill known as the National Defense Authorization Act, which will be considered by various subcommittees starting Tuesday. The section of the bill that deals with cyber and information technology was accompanied by several demands for updates on the Defense Department's efforts to incorporate AI into its national security posture. One of these demands relates directly to China, which the committee said has an increasing presence in disputed waters around the world that threatens U.S. national security.

"The questions asked don't reflect the reality we exist in," says Abdelhamad, a father of two who makes 250 dinars ($353) a month and struggles to make ends meet, as quoted in the report. Takaful also reinforces existing gender-based discrimination by relying on sexist legal codes. The cash assistance is provided to Jordanian citizens only, and one indicator the algorithm takes into account is the size of a household. Although Jordanian men who marry a noncitizen can pass on citizenship to their spouse, Jordanian women who do so cannot. For such women, this results in a lower reportable household size, making them less likely to receive assistance.

Among the Chinese companies invited were several that have landed on American government blacklists for allegations that their activities contribute to the surveillance of Chinese ethnic minorities -- limiting their ability to do business with American firms. These included SenseTime -- an artificial intelligence firm specializing in facial recognition -- and BGI Group, a genomics company. The U.S. Department of Defense also classified a unit of BGI Group last year as one of the "Chinese military companies operating in the United States," even though BGI says its technology was developed for civilian purposes. Both firms deny the allegations behind their blacklisting, and at the forum, they spoke warmly of their business relationships with the Saudi government, which for BGI Group included setting up laboratories in the kingdom during the coronavirus pandemic. Despite official claims to the contrary, many Saudis can't help but frame their growing ties with China in contrast to the waning influence of the United States in the kingdom.

Rayleigh-B\'enard convection (RBC) is a recurrent phenomenon in several industrial and geoscience flows and a well-studied system from a fundamental fluid-mechanics viewpoint. However, controlling RBC, for example by modulating the spatial distribution of the bottom-plate heating in the canonical RBC configuration, remains a challenging topic for classical control-theory methods. In the present work, we apply deep reinforcement learning (DRL) for controlling RBC. We show that effective RBC control can be obtained by leveraging invariant multi-agent reinforcement learning (MARL), which takes advantage of the locality and translational invariance inherent to RBC flows inside wide channels. The MARL framework applied to RBC allows for an increase in the number of control segments without encountering the curse of dimensionality that would result from a naive increase in the DRL action-size dimension. This is made possible by the MARL ability for re-using the knowledge generated in different parts of the RBC domain. We show in a case study that MARL DRL is able to discover an advanced control strategy that destabilizes the spontaneous RBC double-cell pattern, changes the topology of RBC by coalescing adjacent convection cells, and actively controls the resulting coalesced cell to bring it to a new stable configuration. This modified flow configuration results in reduced convective heat transfer, which is beneficial in several industrial processes. Therefore, our work both shows the potential of MARL DRL for controlling large RBC systems, as well as demonstrates the possibility for DRL to discover strategies that move the RBC configuration between different topological configurations, yielding desirable heat-transfer characteristics. These results are useful for both gaining further understanding of the intrinsic properties of RBC, as well as for developing industrial applications.

Estimating heterogeneous treatment effects from observational data is a crucial task across many fields, helping policy and decision-makers take better actions. There has been recent progress on robust and efficient methods for estimating the conditional average treatment effect (CATE) function, but these methods often do not take into account the risk of hidden confounding, which could arbitrarily and unknowingly bias any causal estimate based on observational data. We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on the level of hidden confounding. We derive the B-Learner by adapting recent results for sharp and valid bounds of the average treatment effect (Dorn et al., 2021) into the framework given by Kallus & Oprescu (2023) for robust and model-agnostic learning of conditional distributional treatment effects. The B-Learner can use any function estimator such as random forests and deep neural networks, and we prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods. Semi-synthetic experimental comparisons validate the theoretical findings, and we use real-world data to demonstrate how the method might be used in practice.

The detection of political fake statements is crucial for maintaining information integrity and preventing the spread of misinformation in society. Historically, state-of-the-art machine learning models employed various methods for detecting deceptive statements. These methods include the use of metadata (W. Wang et al., 2018), n-grams analysis (Singh et al., 2021), and linguistic (Wu et al., 2022) and stylometric (Islam et al., 2020) features. Recent advancements in large language models, such as GPT-3 (Brown et al., 2020) have achieved state-of-the-art performance on a wide range of tasks. In this study, we conducted experiments with GPT-3 on the LIAR dataset (W. Wang et al., 2018) and achieved higher accuracy than state-of-the-art models without using any additional meta or linguistic features. Additionally, we experimented with zero-shot learning using a carefully designed prompt and achieved near state-of-the-art performance. An advantage of this approach is that the model provided evidence for its decision, which adds transparency to the model's decision-making and offers a chance for users to verify the validity of the evidence provided.

We introduce a method that dramatically reduces fine-tuning VRAM requirements and rectifies quantization errors in quantized Large Language Models. First, we develop an extremely memory-efficient fine-tuning (EMEF) method for quantized models using Low-Rank Adaptation (LoRA), and drawing upon it, we construct an error-correcting algorithm designed to minimize errors induced by the quantization process. Our method reduces the memory requirements by up to 5.6 times, which enables fine-tuning a 7 billion parameter Large Language Model (LLM) on consumer laptops. At the same time, we propose a Low-Rank Error Correction (LREC) method that exploits the added LoRA layers to ameliorate the gap between the quantized model and its float point counterpart. Our error correction framework leads to a fully functional INT2 quantized LLM with the capacity to generate coherent English text. To the best of our knowledge, this is the first INT2 Large Language Model that has been able to reach such a performance. The overhead of our method is merely a 1.05 times increase in model size, which translates to an effective precision of INT2.1. Also, our method readily generalizes to other quantization standards, such as INT3, INT4, and INT8, restoring their lost performance, which marks a significant milestone in the field of model quantization. The strategies delineated in this paper hold promising implications for the future development and optimization of quantized models, marking a pivotal shift in the landscape of low-resource machine learning computations.

This paper introduces a deep reinforcement learning (RL) framework for optimizing the operations of power plants pairing renewable energy with storage. The objective is to maximize revenue from energy markets while minimizing storage degradation costs and renewable curtailment. The framework handles complexities such as time coupling by storage devices, uncertainty in renewable generation and energy prices, and non-linear storage models. The study treats the problem as a hierarchical Markov Decision Process (MDP) and uses component-level simulators for storage. It utilizes RL to incorporate complex storage models, overcoming restrictions of optimization-based methods that require convex and differentiable component models. A significant aspect of this approach is ensuring policy actions respect system constraints, achieved via a novel method of projecting potentially infeasible actions onto a safe state-action set. The paper demonstrates the efficacy of this approach through extensive experiments using data from US and Indian electricity markets, comparing the learned RL policies with a baseline control policy and a retrospective optimal control policy. It validates the adaptability of the learning framework with various storage models and shows the effectiveness of RL in a complex energy optimization setting, in the context of multi-market bidding, probabilistic forecasts, and accurate storage component models.

Neural networks in modern communication systems can be susceptible to internal numerical errors that can drastically effect decision results. Such structures are composed of many sections each of which generally contain weighting operations and activation function evaluations. The safe use comes from methods employing number based codes that can detect arithmetic errors in the network's processing steps. Each set of operations generates parity values dictated by a code in two ways. One set of parities is obtained from a section's outputs while a second comparable set is developed directly from the original inputs. The parity values protecting the activation functions involve a Taylor series approximation to the activation functions. We focus on using long numerically based convolutional codes because of the large size of data sets. The codes are based on Discrete Fourier Transform kernels and there are many design options available. Mathematical program simulations show our error-detecting techniques are effective and efficient.