A few past works approach this problem by adopting a self-training framework with a sample-selection mechanism.

In Arizona's borderlands, the desert is already deadly. But for years, another threat has stalked these routes: Armed vigilante groups who take it upon themselves to police the border – often violently, and outside the law. They have long undermined the work of humanitarian volunteers trying to save lives. Now, a new artificial intelligence platform is actively encouraging more people to join their ranks. ICERAID.us, recently launched in the United States, offers cryptocurrency rewards to users who upload photos of "suspicious activity" along the border. It positions civilians as front-line intelligence gatherers – doing the work of law enforcement, but without oversight.

For centuries, the Rub' al-Khali desert near Saudi Arabia and Dubai -- known as the Empty Quarter -- was dismissed as a lifeless sea of sand. In 2002, Sheikh Mohammed bin Rashid Al Maktoum, ruler of Dubai, spotted unusual dune formations and a large black deposit while flying over the desert. That led to the discovery of Saruq Al-Hadid, an archaeological site rich in remnants of copper and iron smelting, which is now believed to be part of a 5,000-year-old civilization buried beneath the sands. Researchers have now found traces of this ancient society approximately 10 feet beneath the desert surface, hidden in plain sight and long overlooked due to the harsh environment and shifting dunes of the Empty Quarter. This discovery brings fresh life to the legend of a mythical city known as'Atlantis of the Sands.'

"I feel that he (Putin) wants peace," said President Trump's personal envoy Steve Witkoff, adding: "I think that you're going to see in Saudi Arabia on Monday some real progress." Yet Dmitry Peskov, the Kremlin spokesman has dampened expectations. "We are only at the beginning of this path," he told Russian state TV. Kyiv suffered one of its heaviest attacks from Russian drones on Saturday night, with three people killed, including a five-year-old girl. "We need to push Putin to give a real order to stop the strikes," said Ukraine's President Volodymyr Zelensky in his evening address on Sunday.

We present a benchmark for large language models designed to tackle one of the most knowledge-intensive tasks in data science: writing feature engineering code, which requires domain knowledge in addition to a deep understanding of the underlying problem and data structure. The model is provided with a dataset description in a prompt and asked to generate code transforming it. The evaluation score is derived from the improvement achieved by an XGBoost model fit on the modified dataset compared to the original data. By an extensive evaluation of state-of-the-art models and comparison to well-established benchmarks, we demonstrate that the FeatEng of our proposal can cheaply and efficiently assess the broad capabilities of LLMs, in contrast to the existing methods. The reference implementation is available at https://github.com/FeatEng/FeatEng. The rapid evolution of LLMs has significantly expanded their capabilities in processing and generating human-like text. As these models become increasingly sophisticated, defining what constitutes a meaningful benchmark is becoming harder and harder, as it is much easier to distinguish between bad and good models than between good and better. Today, the limitations of LLMs are predominantly assessed using benchmarks focused on language understanding, world knowledge, code generation, or mathematical reasoning in separation. This setup, however, overlooks some critical capabilities that can be measured in scenarios requiring inregration of skills and verification of their instrumental value in complex, real-world problems. We argue that well-designed LLM benchmarks should embody the following qualities, each reflecting a fundamental aspect of problem-solving ability: 1. Practical Usability. We demand that tasks are grounded in real-world problems where solutions have high functional value. This ensures that improvements in the observed performance translates into tangible benefits, aligning with the pragmatist view on the instrumental value of knowledge and truth, meaning that the validity of an idea depends on its practical utility in achieving desired outcomes (James, 1907). We would value LLM's knowledge for its role in enabling reasoning, decision-making, and problem-solving. The benchmark should be designed to evaluate not only the breadth of a model's knowledge base but also, more importantly, its capacity to dynamically and effectively apply this knowledge within various functional contexts, similarly to how functionalism frames it (Block, 1980). We opt for assessing models concerning their ability to seamlessly combine various competencies, in contrast to measuring them in separation.

While distant supervision has been extensively explored and exploited in NLP tasks like named entity recognition, a major obstacle stems from the inevitable noisy distant labels tagged unsupervisedly. A few past works approach this problem by adopting a self-training framework with a sample-selection mechanism. In this work, we innovatively identify two types of biases that were omitted by prior work, and these biases lead to inferior performance of the distant-supervised NER setup. First, we characterize the noise concealed in the distant labels as highly structural rather than fully randomized. Second, the self-training framework would ubiquitously introduce an inherent bias that causes erroneous behavior in both sample selection and eventually prediction.

What's new: Roblox has announced plans to roll out a generative AI tool that will let creators make whole 3D scenes just using text prompts. Users will also be able to modify scenes or expand their scope--say, to change a daytime scene to night or switch the desert for a forest. How it works: Once it's up and running, developers on the hugely popular online game platform will be able to simply write "Generate a race track in the desert," for example, and the AI will spin one up. Why it's a big deal: Although developers can already create similar scenes like this manually in the platform's creator studio, Roblox claims its new generative AI model will make the changes happen in a fraction of the time. It also claims that it will give developers with minimal 3D art skills the ability to craft more compelling environments.

We begin with a useful lemma. Let X ESN(0,) and let a apple b apple 0, 0 apple c appleh d. The result for P(c apple X apple d) follows analogously. For the reader's convenience, we summarize in detail a few common techniques for defining OOD scores that measure the degree of ID-ness on the given sample. All the methods derive the score post hoc on neural networks trained with in-distribution data only.