The explosive growth of complex datasets across various modalities necessitates advanced analytical tools that not only group data effectively but also provide human-understandable insights into the discovered structures. We introduce HERCULES (Hierarchical Embedding-based Recursive Clustering Using LLMs for Efficient Summarization), a novel algorithm and Python package designed for hierarchical k-means clustering of diverse data types, including text, images, and numeric data (processed one modality per run). HERCULES constructs a cluster hierarchy by recursively applying k-means clustering, starting from individual data points at level 0. A key innovation is its deep integration of Large Language Models (LLMs) to generate semantically rich titles and descriptions for clusters at each level of the hierarchy, significantly enhancing interpretability. The algorithm supports two main representation modes: `direct' mode, which clusters based on original data embeddings or scaled numeric features, and `description' mode, which clusters based on embeddings derived from LLM-generated summaries. Users can provide a `topic\_seed' to guide LLM-generated summaries towards specific themes. An interactive visualization tool facilitates thorough analysis and understanding of the clustering results. We demonstrate HERCULES's capabilities and discuss its potential for extracting meaningful, hierarchical knowledge from complex datasets.

Recently, radars have been widely featured in robotics for their robustness in challenging weather conditions. Two commonly used radar types are spinning radars and phased-array radars, each offering distinct sensor characteristics. Existing datasets typically feature only a single type of radar, leading to the development of algorithms limited to that specific kind. In this work, we highlight that combining different radar types offers complementary advantages, which can be leveraged through a heterogeneous radar dataset. Moreover, this new dataset fosters research in multi-session and multi-robot scenarios where robots are equipped with different types of radars. In this context, we introduce the HeRCULES dataset, a comprehensive, multi-modal dataset with heterogeneous radars, FMCW LiDAR, IMU, GPS, and cameras. This is the first dataset to integrate 4D radar and spinning radar alongside FMCW LiDAR, offering unparalleled localization, mapping, and place recognition capabilities. The dataset covers diverse weather and lighting conditions and a range of urban traffic scenarios, enabling a comprehensive analysis across various environments. The sequence paths with multiple revisits and ground truth pose for each sensor enhance its suitability for place recognition research. We expect the HeRCULES dataset to facilitate odometry, mapping, place recognition, and sensor fusion research. The dataset and development tools are available at https://sites.google.com/view/herculesdataset.

The China fight is on. Last week, the House of Representatives stepped up to the Herculean task of passing 25 bills targeting Chinese intrusions into America's economy and technology. This first-ever "China Week" took aim at drones, bad Chinese network routers, batteries and federal biotech contracts with Chinese firms. "House indulges in Mad Hatter's Tea Party," screamed state-run China Daily on Thursday, lamenting "40 years of mutually beneficial relationships. President, Xi Jinping waves as he leaves after speaking at a press event on Oct. 23, 2022, in Beijing, China. Rep. John Moolenaar, R-Mich., chairman of the House Select Committee on China, put it clearly. "This week, we will draw a line in the sand.

Retrieval-augmented generation (RAG) has received much attention for Open-domain question-answering (ODQA) tasks as a means to compensate for the parametric knowledge of large language models (LLMs). While previous approaches focused on processing retrieved passages to remove irrelevant context, they still rely heavily on the quality of retrieved passages which can degrade if the question is ambiguous or complex. In this paper, we propose a simple yet efficient method called question and passage augmentation via LLMs for open-domain QA. Our method first decomposes the original questions into multiple-step sub-questions. By augmenting the original question with detailed sub-questions and planning, we are able to make the query more specific on what needs to be retrieved, improving the retrieval performance. In addition, to compensate for the case where the retrieved passages contain distracting information or divided opinions, we augment the retrieved passages with self-generated passages by LLMs to guide the answer extraction. Experimental results show that the proposed scheme outperforms the previous state-of-the-art and achieves significant performance gain over existing RAG methods.

Kevin Sorbo and wife Sam Sorbo shared their concerns about artificial intelligence and why the actors strike is necessary as negotiations continue to be at a standstill between SAG-AFTRA and the AMPTP.

When it comes to tackling the complex questions of humanity and morality, can AI make the world more moral? Morality is one of the most deeply human considerations in existence. The very nature of the human condition pushes us to try to distinguish right from wrong, and the existence of other humans pushes us to treat others by those values. What is good and what is right are questions usually reserved for philosophers and religious or cultural leaders. But as artificial intelligence weaves itself into nearly every aspect of our lives, it is time to consider the implications of AI on morality, and morality on AI.

The underwater lake, discovered 3,300 feet below the surface of the Gulf of Mexico, is a pit of super-salty water and dissolved methane that kills any critter unlucky enough to fall inside. The discovery was made last year by a San Pedro-based research vessel, the E/V Nautilus. In the video, scientists excitedly navigate a remotely operated vehicle, the Hercules, above the circular pool. They point out the "pickled crabs" that succumbed to the elements. "These larger organisms really don't like to be in this fluid -- or maybe they just come here to die," Scott Wankel, a marine chemist, says on the video.

The question of what it meant to be human obsessed the ancient Greeks. The beloved myths of Hercules, Jason and the Argonauts, the sorceress Medea, the engineer Daedalus, the inventor-god Hephaestus, and the tragically inquisitive Pandora all raised the basic question of the boundaries between human and machine. Today, developments in biotechnology and advances in artificial intelligence (AI) bring a new urgency to questions about the implications of combining the biological and the technological. It's a discussion that we might say the ancient Greeks began. Medea, the mythic sorceress whose name means'to devise', knew many arcane arts. These included secrets of rejuvenation. To demonstrate her powers, Medea first appeared to Jason and the Argonauts as a stooped old woman, only to transform herself into a beautiful young princess. Jason fell under her spell and became her lover. He asked Medea to restore the youthful vigour of his aged father, Aeson.