Fighting in Gaza between the Israeli army and the armed faction of Hamas is a textbook example of modern asymmetric warfare. Whenever fighting ends, it will be studied by strategists and tacticians. The term "asymmetric warfare" has been used for less than 60 years, but the concept is much older. Asymmetric wars are usually bloodier and more savage than those between regular armies: In a state versus non-state conflict, the latter's fighters are not recognised as "proper" combatants and thus not considered protected by international conventions and laws of war. The regular army will use weapons and tactics that might be legally unacceptable in a "proper war".

For as little as $0.12 per record, data brokers in the US are selling sensitive private data about both active-duty military members and veterans, including their names, addresses, geolocation, net worth, and religion, and information about their children and health conditions. In an unsettling study published today, researchers from Duke University approached 12 data brokers and purchased thousands of records about American service members with minimal vetting. The study highlights the extreme privacy and national security risks created by data brokers. These companies are part of a shadowy multibillion-dollar industry that collects, aggregates, buys, and sells data, practices that are currently legal in the US, exacerbating the erosion of personal and consumer privacy. Last week, President Biden released his executive order on AI, a sweeping set of rules and guidelines designed to improve AI safety and security.

If Epic wins, it could mean more favorable terms for Android app developers. But that outcome is far from assured: Epic is going into the trial alone, after other plaintiffs dropped out. Last month, Match Group, the owner of dating apps Tinder and Hinge, dropped out as a plaintiff with Epic, after Google agreed for its users to make in-app purchases through other payment channels. A group of 52 state attorneys general reached a settlement with Google in September in a parallel case.

Russian forces have bombarded Ukraine's port city of Odesa with missiles and drones. Four missiles and 22 attack drones were launched from the occupied region of Crimea of Ukraine at the Black Sea port late on Sunday, Ukraine's air force reported on Monday. The attacks injured at least eight people, destroyed grain, and damaged the 124-year-old Odesa Fine Arts Museum. "Fifteen Shaheds and one Kh-59 air guided missile were shot down," the Ukrainian air force said, referring to the Iranian-designed kamikaze unmanned aerial vehicle. Ukrainian Presidential Chief of Staff Andriy Yermak posted images on social media of the aftermath of the strike, vowing retribution for the attack.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. The U.S. and other countries signed an agreement to collaborate and communicate on "frontier" artificial intelligence (AI) that will aim to limit the risks presented by the technology in the coming years. "We encourage all relevant actors to provide context-appropriate transparency and accountability on their plans to measure, monitor and mitigate potentially harmful capabilities and the associated effects that may emerge, in particular to prevent misuse and issues of control, and the amplification of other risks," the Bletchley Declaration, signed by 28 countries, including the U.S., China and members of the European Union. The international community has wrangled with the problem of AI, trying to balance the obvious and emerging risks associated with such advanced technology against what Britain's King Charles III called the "untold benefits." The Bletchley Declaration therefore lays out two key points: "identifying AI safety risks" and "building respective risk-based policies across our countries to ensure safety in light of such risks."

Elon Musk revealed his own artificial intelligence bot to challenge ChatGPT, claiming the prototype is already superior to ChatGPT 3.5 across several benchmarks. Dubbed Grok, it's the first product of Musk's xAI company and is now in testing with a limited group of U.S. users. Grok is being developed with data from Musk's X, formerly Twitter, and is thus better informed on the latest developments than alternative bots with static datasets, the company's website said. It's also designed to answer "with a bit of wit and has a rebellious streak," according to the announcement. Earlier this year, Musk was among the signatories of a petition calling for a pause in advancing AI models in order to allow for the development of shared safety protocols.

Model Predictive Control (MPC) is a popular strategy for controlling robots but is difficult for systems with contact due to the complex nature of hybrid dynamics. To implement MPC for systems with contact, dynamic models are often simplified or contact sequences fixed in time in order to plan trajectories efficiently. In this work, we extend Hybrid iterative Linear Quadratic Regulator to work in a MPC fashion (HiLQR MPC) by 1) modifying how the cost function is computed when contact modes do not align, 2) utilizing parallelizations when simulating rigid body dynamics, and 3) using efficient analytical derivative computations of the rigid body dynamics. The result is a system that can modify the contact sequence of the reference behavior and plan whole body motions cohesively -- which is crucial when dealing with large perturbations. HiLQR MPC is tested on two systems: first, the hybrid cost modification is validated on a simple actuated bouncing ball hybrid system. Then HiLQR MPC is compared against methods that utilize centroidal dynamic assumptions on a quadruped robot (Unitree A1). HiLQR MPC outperforms the centroidal methods in both simulation and hardware tests.

People primarily consult tables to conduct data analysis or answer specific questions. Text generation systems that can provide accurate table summaries tailored to users' information needs can facilitate more efficient access to relevant data insights. Motivated by this, we define a new query-focused table summarization task, where text generation models have to perform human-like reasoning and analysis over the given table to generate a tailored summary. We introduce a new benchmark named QTSumm for this task, which contains 7,111 human-annotated query-summary pairs over 2,934 tables covering diverse topics. We investigate a set of strong baselines on QTSumm, including text generation, table-to-text generation, and large language models. Experimental results and manual analysis reveal that the new task presents significant challenges in table-to-text generation for future research. Moreover, we propose a new approach named ReFactor, to retrieve and reason over query-relevant information from tabular data to generate several natural language facts. Experimental results demonstrate that ReFactor can bring improvements to baselines by concatenating the generated facts to the model input. Our data and code are publicly available at https://github.com/yale-nlp/QTSumm.

The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data. To maintain the usability of scientific data, error-bound lossy compression is proposed and developed as an essential technique for the size reduction of scientific data with constrained data distortion. Among the diverse datasets generated by various scientific simulations, certain datasets cannot be effectively compressed by existing error-bounded lossy compressors with traditional techniques. The recent success of Artificial Intelligence has inspired several researchers to integrate neural networks into error-bounded lossy compressors. However, those works still suffer from limited compression ratios and/or extremely low efficiencies. To address those issues and improve the compression on the hard-to-compress datasets, in this paper, we propose SRN-SZ, which is a deep learning-based scientific error-bounded lossy compressor leveraging the hierarchical data grid expansion paradigm implemented by super-resolution neural networks. SRN-SZ applies the most advanced super-resolution network HAT for its compression, which is free of time-costing per-data training. In experiments compared with various state-of-the-art compressors, SRN-SZ achieves up to 75% compression ratio improvements under the same error bound and up to 80% compression ratio improvements under the same PSNR than the second-best compressor.

Despite the recent success of large, pretrained neural language models (LLMs) on a variety of prompting tasks, these models can be alarmingly brittle to small changes in inputs or application contexts. To better understand such behavior and motivate the design of more robust LLMs, we provide a causal formulation of linguistic competence in the context of LLMs and propose a general framework to study and measure LLM competence. Our framework, CALM (Competence-based Analysis of Language Models), establishes the first quantitative measure of LLM competence, which we study by damaging models' internal representations of various linguistic properties in the course of performing various tasks using causal probing and evaluating models' alignment under these interventions with a given causal model. We also develop a novel approach for performing causal probing interventions using gradient-based adversarial attacks, which can target a broader range of properties and representations than existing techniques. We carry out a case study of CALM using these interventions to analyze BERT and RoBERTa's competence across a variety of lexical inference tasks, showing that the CALM framework and competence metric can be valuable tools for explaining and predicting their behavior across these tasks.