A fully automated warehouse system is changing the way we shop for groceries. Imagine a grocery store where your entire order is picked, packed and ready for delivery in just five minutes without a single human hand touching your food. This is exactly what's happening inside Ocado's revolutionary Hive, a fully automated warehouse system that's changing the way we shop for groceries. At the core of Ocado's Customer Fulfilment Centres, or CFCs, is The Hive, a massive 3D grid filled with thousands of grocery products. GET SECURITY ALERTS & EXPERT TECH TIPS -- SIGN UP FOR KURT'S THE CYBERGUY REPORT NOW Picture fleets of robots or "bots" zipping around at speeds up to about 9 miles per hour, all coordinated by an AI-powered "air traffic control" system that talks to each bot ten times every second.

The Ride-Pool Matching Problem (RMP) is central to on-demand ride-pooling services, where vehicles must be matched with multiple requests while adhering to service constraints such as pickup delays, detour limits, and vehicle capacity. Most existing RMP solutions assume passengers are picked up and dropped off at their original locations, neglecting the potential for passengers to walk to nearby spots to meet vehicles. This assumption restricts the optimization potential in ride-pooling operations. In this paper, we propose a novel matching method that incorporates extended pickup and drop-off areas for passengers. We first design a tree-based approach to efficiently generate feasible matches between passengers and vehicles. Next, we optimize vehicle routes to cover all designated pickup and drop-off locations while minimizing total travel distance. Finally, we employ dynamic assignment strategies to achieve optimal matching outcomes. Experiments on city-scale taxi datasets demonstrate that our method improves the number of served requests by up to 13\% and average travel distance by up to 21\% compared to leading existing solutions, underscoring the potential of leveraging passenger mobility to significantly enhance ride-pooling service efficiency.

Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. A promising but largely under-explored area is their potential to facilitate human coordination with many agents. Such capabilities would be useful in domains including disaster response, urban planning, and real-time strategy scenarios. In this work, we introduce (1) a real-time strategy game benchmark designed to evaluate these abilities and (2) a novel framework we term HIVE. HIVE empowers a single human to coordinate swarms of up to 2,000 agents using natural language dialog with an LLM. We present promising results on this multi-agent benchmark, with our hybrid approach solving tasks such as coordinating agent movements, exploiting unit weaknesses, leveraging human annotations, and understanding terrain and strategic points. However, our findings also highlight critical limitations of current models, including difficulties in processing spatial visual information and challenges in formulating long-term strategic plans. This work sheds light on the potential and limitations of LLMs in human-swarm coordination, paving the way for future research in this area. The HIVE project page, which includes videos of the system in action, can be found here: hive.syrkis.com.

"This work represents a significant step forward in strengthening our information advantage as we combat sophisticated disinformation campaigns and synthetic-media threats," says Bustamante. Hive was chosen out of a pool of 36 companies to test its deepfake detection and attribution technology with the DOD. The contract could enable the department to detect and counter AI deception at scale. "This is the evolution of cyberwarfare." Hive's technology has been trained on a large amount of content, some AI-generated and some not.

The advent of generative AI images has completely disrupted the art world. Distinguishing AI generated images from human art is a challenging problem whose impact is growing over time. A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery. It is also critical for content owners to establish copyright, and for model trainers interested in curating training data in order to avoid potential model collapse. There are several different approaches to distinguishing human art from AI images, including classifiers trained by supervised learning, research tools targeting diffusion models, and identification by professional artists using their knowledge of artistic techniques. In this paper, we seek to understand how well these approaches can perform against today's modern generative models in both benign and adversarial settings. We curate real human art across 7 styles, generate matching images from 5 generative models, and apply 8 detectors (5 automated detectors and 3 different human groups including 180 crowdworkers, 4000+ professional artists, and 13 expert artists experienced at detecting AI). Both Hive and expert artists do very well, but make mistakes in different ways (Hive is weaker against adversarial perturbations while Expert artists produce higher false positives). We believe these weaknesses will remain as models continue to evolve, and use our data to demonstrate why a combined team of human and automated detectors provides the best combination of accuracy and robustness.

As tedious as the incessant requests are for longer and harder-to-remember passwords, experts say there's good reason for the nuisance. It's gotten easier and easier for hackers to guess your password as computer processing speeds have gotten faster. With sprawling cloud-based computer power now available for rent to anyone -- and massive supercomputers out there, like the system that trained ChatGPT -- cyber security firm Hive Systems says that a truly professional hacker could access your secrets almost instantly. The company has produced a new table showing just how safe or vulnerable your password is, based on its character count and the diversity of characters you've used. They say you'll need a fully random password, that's at least 12-characters long, with a mixture of numbers, special symbols, upper- and lowercase letters, if you want to keep even just an amateur hacker out of your account, thanks to the power of today's consumer desktop tech.

Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional image editing models, where outputs are generated based on an input image and an editing instruction, could similarly benefit from human feedback, as their outputs may not adhere to the correct instructions and preferences of users. In this paper, we present a novel framework to harness human feedback for instructional visual editing (HIVE). Specifically, we collect human feedback on the edited images and learn a reward function to capture the underlying user preferences. We then introduce scalable diffusion model fine-tuning methods that can incorporate human preferences based on the estimated reward. Besides, to mitigate the bias brought by the limitation of data, we contribute a new 1M training dataset, a 3.6K reward dataset for rewards learning, and a 1K evaluation dataset to boost the performance of instructional image editing. We conduct extensive empirical experiments quantitatively and qualitatively, showing that HIVE is favored over previous state-of-the-art instructional image editing approaches by a large margin.

Numberly is recognized as one of the world's leading data marketing specialists with nearly 500 employees and 8 offices worldwide serving more than 500 blue-chip clients (L'Oréal, P&G, Groupe Seb, HSBC...). By putting technology to work for brands and consumers, Numberly is at the heart of business growth and everyone's desire for more responsible and relevant marketing. Numberly is looking for a Data Engineer to join its dedicated team Data. Create and maintain pipeline jobs that transfer client data to/from our database diverse infrastructure (Hive, MS SQL Server, MongoDB, ScyllaDB). Nurture our large Hadoop cluster, optimize distributed Data Operations and Storage.

I first stumbled upon ChatGPT a month ago, but I never paid attention to it. It was @kenny-crane who became instrumental in me appreciating the power of this AI when I asked a question about practical steps to do to help improve the financial standing of our institution. Hive is a data warehousing system built on top of Hadoop that provides a SQL-like interface for querying and managing large datasets stored in the Hadoop Distributed File System (HDFS) or other storage systems supported by Hadoop. It allows users to create and query tables, insert data into tables, and manage the metadata for the data stored in the warehouse. Hive also supports user-defined functions, which allow developers to extend its functionality with custom code.

When you run a major app, all it takes is one mistake to put countless people at risk. Such is the case with Diksha, a public education app run by India's Ministry of Education that exposed the personal information of around 1 million teachers and millions of students across the country. The data, which included things like full names, email addresses, and phone numbers, was publicly accessible for at least a year and likely longer, potentially exposing those impacted to phishing attacks and other scams. Speaking of cybercrime, the LockBit ransomware gang has long operated under the radar, thanks to its professional operation and choice of targets. But over the past year, a series of missteps and drama have thrust it into the spotlight, potentially threatening its ability to continue operating with impunity.