A disc-shaped, completely silent UFO was caught on camera by Ukrainian troops in the war-torn country, in footage shared exclusively with DailyMail.com. 'What the f-[expletive] is this? Why isn't it moving?' the men with Ukraine's 406th Battalion can be heard debating as they witnessed the deadly calm UFO hovering over their warzone. While the size, altitude, and shape of the object remain a mystery, the drone's own altitude indicates that the apparent object could be a large craft over 30 miles away. The eerie footage was captured by the 406th Battalion this month via one of the over 300 'heat vision' quadcopter drones used by the Ukrainian Armed Forces (UAF) in their effort to defend the nation from a now two-years long invasion by Russia.

Microsoft Corp.'s Mistral AI investment is set to be analyzed by the European Union's competition watchdog at the same time that its deep ties to OpenAI Inc come under regulatory scrutiny. Mistral announced a "strategic partnership" with Microsoft on Monday that includes making the startup's latest artificial intelligence models available to customers of Microsoft's Azure cloud. Microsoft said the investment amounted to 15 million ( 16.3 million.) Mistral develops algorithmic models similar to those from OpenAI used for chatbots and other AI services, but Mistral models are shared openly. Microsoft's investments will convert into equity as part of Mistral's next funding round.

Gen. Qassim Suleimani, the high-level Iranian general killed by an American drone strike in 2020, kept the Shiite militias in Iraq and Syria on a tight leash. That was largely because, for most of his tenure, war was raging in both countries, and he commanded the militia to fight Americans and then Islamic State terrorist groups. But when Brig. Gen. Esmail Ghaani succeeded him, most of those conflicts had settled, and General Ghaani assumed a hands-off leadership style, setting only broad directions, according to analysts. General Ghaani, commander in chief of the Quds Forces, the branch of the Islamic Revolutionary Guards Corps tasked with overseeing the proxies, has nonetheless been involved in coordinating the strategy toward Israel and the United States for the various militias during the current war in Gaza. He led a series of emergency meetings in late January in Tehran and Baghdad with strategists, senior commanders of the Revolutionary Guards and senior commanders of the militia to redraw plans and avert war with the United States, according to two Iranians affiliated with the Guards, one of them a military strategist.

The Japan government-backed research group developing semiconductors will partner with U.S. startup Tenstorrent on the design of its first advanced AI chip. Tenstorrent, led by Tesla and Apple veteran Jim Keller, will license its design for part of Japan's artificial intelligence accelerator and also co-design the overall chip, the U.S. company said at a joint event in Tokyo on Tuesday. Working with the open-source RISC-V standard, Tenstorrent aims to provide customers with an alternative to the leaders Nvidia and Arm, who have their own so-called instruction sets to communicate between hardware and software. The Japanese government is funding a range of projects from research to advanced chip manufacturing, making an ambitious 67 billion bid to reclaim a central role in the semiconductor industry. The Tenstorrent agreement has the potential to advance those efforts, with the goal of producing the jointly designed AI chips at the government-backed startup Rapidus.

The US military has ramped up its use of artificial intelligence tools after the October 7 Hamas attacks on Israel, based on a new report by Bloomberg. Schuyler Moore, US Central Command's chief technology officer, told the news organization that machine learning algorithms helped the Pentagon identify targets for more than 85 air strikes in the Middle East this month. US bombers and fighter aircraft carried out those air strikes against seven facilities in Iraq and Syria on February 2, fully destroying or at least damaging rockets, missiles, drone storage facilities and militia operations centers. The Pentagon had also used AI systems to find rocket launchers in Yemen and surface combatants in the Red Sea, which it had then destroyed through multiple air strikes in the same month. The machine learning algorithms used to narrow down targets were developed under Project Maven, Google's now-defunct partnership the Pentagon.

With increasing numbers of people in Japan getting married later in life or not at all, local governments are turning to a new weapon in their fight to reverse the trend -- artificial intelligence. Authorities in many regions have been organizing traditional konkatsu matchmaking events with AI sifting for compatibilities between potential partners. They say it has sometimes led to people who would never have imagined being together tying the knot. Even the central government is now lending its support to such moves as depopulation progresses throughout the country. Subsidies for publicly run AI matchmaking events have been expanding since fiscal 2021.

Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, we focus on open foundation models, defined here as those with broadly available model weights (e.g. Llama 2, Stable Diffusion XL). We identify five distinctive properties (e.g. greater customizability, poor monitoring) of open foundation models that lead to both their benefits and risks. Open foundation models present significant benefits, with some caveats, that span innovation, competition, the distribution of decision-making power, and transparency. To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk. Across several misuse vectors (e.g. cyberattacks, bioweapons), we find that current research is insufficient to effectively characterize the marginal risk of open foundation models relative to pre-existing technologies. The framework helps explain why the marginal risk is low in some cases, clarifies disagreements about misuse risks by revealing that past work has focused on different subsets of the framework with different assumptions, and articulates a way forward for more constructive debate. Overall, our work helps support a more grounded assessment of the societal impact of open foundation models by outlining what research is needed to empirically validate their theoretical benefits and risks.

We address causal reasoning in multivariate time series data generated by stochastic processes. Existing approaches are largely restricted to static settings, ignoring the continuity and emission of variations across time. In contrast, we propose a learning paradigm that directly establishes causation between events in the course of time. We present two key lemmas to compute causal contributions and frame them as reinforcement learning problems. Our approach offers formal and computational tools for uncovering and quantifying causal relationships in diffusion processes, subsuming various important settings such as discrete-time Markov decision processes. Finally, in fairly intricate experiments and through sheer learning, our framework reveals and quantifies causal links, which otherwise seem inexplicable.

It is often desirable for Large Language Models (LLMs) to capture multiple objectives when providing a response. In document-grounded response generation, for example, agent responses are expected to be relevant to a user's query while also being grounded in a given document. In this paper, we introduce Proxy Metric-based Self-Refinement (ProMiSe), which enables an LLM to refine its own initial response along key dimensions of quality guided by external metrics feedback, yielding an overall better final response. ProMiSe leverages feedback on response quality through principle-specific proxy metrics, and iteratively refines its response one principle at a time. We apply ProMiSe to open source language models Flan-T5-XXL and Llama-2-13B-Chat, to evaluate its performance on document-grounded question answering datasets, MultiDoc2Dial and QuAC, demonstrating that self-refinement improves response quality. We further show that fine-tuning Llama-2-13B-Chat on the synthetic dialogue data generated by ProMiSe yields significant performance improvements over the zero-shot baseline as well as a supervised fine-tuned model on human annotated data.

Contemporary language models enable new opportunities for structured reasoning with text, such as the construction and evaluation of intuitive, proof-like textual entailment trees without relying on brittle formal logic. However, progress in this direction has been hampered by a long-standing lack of a clear protocol for determining what valid compositional entailment is. This absence causes noisy datasets and limited performance gains by modern neuro-symbolic engines. To address these problems, we formulate a consistent and theoretically grounded approach to annotating decompositional entailment datasets, and evaluate its impact on LLM-based textual inference. We find that our resulting dataset, RDTE (Recognizing Decompositional Textual Entailment), has a substantially higher internal consistency (+9%) than prior decompositional entailment datasets, suggesting that RDTE is a significant step forward in the long-standing problem of forming a clear protocol for discerning entailment. We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in a modern neuro-symbolic reasoning engine significantly improves results (both accuracy and proof quality) over other entailment classifier baselines, illustrating the practical benefit of this advance for textual inference.