XorDDoS malware targeted 71.3% of U.S. systems in latest wave; Docker, IoT, and Linux bots fuel rise.
Category: robotics/AI – Page 10
Saronic Technologies has unveiled the 150-foot Marauder MUSV, a fully autonomous drone ship with a 90,000-pound payload capability.
“Vaping and dabbing were most common among young adults aged 18–24 years. Trends in both of these routes of use have increased among adolescents and young adults.”
How does the average U.S. adult consume marijuana? This is what a recent report published by the Centers for Disease Control and Prevention (CDC) hopes to address as a team of CDC researchers investigated the range of cannabis products that are used by U.S. adults and which products are used more than others. This report has the potential to help scientists, legislators, and the public better understand cannabis use throughout the United States and develop educational and preventive measures for its use.
The report states, “As the availability and types of cannabis products expand, less is known about how persons consume cannabis. Historically, cannabis has most often been smoked; however, additional routes of use are available, including oral ingestion, vaping, and more recently, dabbing (i.e., inhalation of highly concentrated THC-based oils often heated using a blowtorch).”
For the study, the researchers conducted a survey of 138,625 participants to identify how both the frequency and method of cannabis use and broken up into several age groups. In the end, they found that 14,044 (15.3%) used cannabis with 6,848 (7.9%) using it daily. They found that 79.4% smoked cannabis while eating, vaping, and dabbing comprised 41.6%, 30.3%, and 14.6%, respectively. Additionally, 29.3% of non-Hispanic American Indian or Alaska Native (AI/AN) individuals were found to participate in dabbing, along with 23% of individuals without a high school diploma.
The rise of large language models like ChatGPT that can churn out computer code has led to a new term — vibe coding — for people who create software by asking AI to do it for them
Programmers can now use large language models (LLMs) to generate computer code more quickly. However, this only makes programmers’ lives easier if that code follows the rules of the programming language and doesn’t cause a computer to crash.
How Amazon is revolutionizing warehouse automation with cutting-edge robotics and AI, driving efficiency and innovation.
OpenAI met with more than 20 companies in the AI coding domain, according to a person familiar with the matter.
Gödel’s incompleteness theorem is used by both advocates and adversaries of strong AI to show that computers can(not) perform the same feats as humans. This article extends the construction through which Gödel proved his theorem, in order to allow a broader interpretation, showing that neither side has exploited its arguments to the fullest extend, and that the evidence can never be conclusive.
Dr.ir. C.J.B. Jongeneel & prof.dr. H. Koppelaar, Delft University of Technology, Faculty of Technical Mathematics and Informatics, Section of Knowledge Based Systems.
1 Introduction
This paper introduces an adaptive multi-agent framework to enhance collaborative reasoning in large language models (LLMs). The authors address the challenge of effectively scaling collaboration and reasoning in multi-agent systems (MAS), which is an open question despite recent advances in test-time scaling (TTS) for single-agent performance.
The core methodology revolves around three key contributions:
1. **Dataset Construction:** The authors create a high-quality dataset, M500, comprising 500 multi-agent collaborative reasoning traces. This dataset is generated automatically using an open-source MAS framework (AgentVerse) and a strong reasoning model (DeepSeek-R1). To ensure quality, questions are selected based on difficulty, diversity, and interdisciplinarity. The generation process involves multiple agents with different roles collaborating to solve challenging problems. Data filtering steps are applied to ensure consensus among agents, adherence to specified formats (e.g., using tags like “ and ‘boxed{}‘), and correctness of the final answer. The filtering criteria are based on Consensus Reached, Format Compliance, and Correctness. The data generation is described in Algorithm 1 in the Appendix.