We introduce a new Multi-Agent System (MAS) - Allen, designed to address two core challenges in current MAS design: (1) improve system's policy autonomy, empowering agents to dynamically adapt their behavioral strategies, and (2) achieving the trade-off between collaborative efficiency, task supervision, and human oversight in complex network topologies. Our core insight is to redefine the basic execution unit in the MAS, allowing agents to autonomously form different patterns by combining these units. We have constructed a four-tier state architecture (Task, Stage, Agent, Step) to constrain system behavior from both task-oriented and execution-oriented perspectives. This achieves a unification of topological optimization and controllable progress. Allen grants unprecedented Policy Autonomy, while making a trade-off for the controllability of the collaborative structure. The project code has been open source at: https://github.com/motern88/Allen

Can we avoid wars at the crossroads of history? This question has been pursued by individuals, scholars, policymakers, and organizations throughout human history. In this research, we attempt to answer the question based on the recent advances of Artificial Intelligence (AI) and Large Language Models (LLMs). We propose \textbf{WarAgent}, an LLM-powered multi-agent AI system, to simulate the participating countries, their decisions, and the consequences, in historical international conflicts, including the World War I (WWI), the World War II (WWII), and the Warring States Period (WSP) in Ancient China. By evaluating the simulation effectiveness, we examine the advancements and limitations of cutting-edge AI systems' abilities in studying complex collective human behaviors such as international conflicts under diverse settings. In these simulations, the emergent interactions among agents also offer a novel perspective for examining the triggers and conditions that lead to war. Our findings offer data-driven and AI-augmented insights that can redefine how we approach conflict resolution and peacekeeping strategies. The implications stretch beyond historical analysis, offering a blueprint for using AI to understand human history and possibly prevent future international conflicts. Code and data are available at \url{https://github.com/agiresearch/WarAgent}.

We study the ability of LLMs to generate feedback for scientific papers and develop MARG, a feedback generation approach using multiple LLM instances that engage in internal discussion. By distributing paper text across agents, MARG can consume the full text of papers beyond the input length limitations of the base LLM, and by specializing agents and incorporating sub-tasks tailored to different comment types (experiments, clarity, impact) it improves the helpfulness and specificity of feedback. In a user study, baseline methods using GPT-4 were rated as producing generic or very generic comments more than half the time, and only 1.7 comments per paper were rated as good overall in the best baseline. Our system substantially improves the ability of GPT-4 to generate specific and helpful feedback, reducing the rate of generic comments from 60% to 29% and generating 3.7 good comments per paper (a 2.2x improvement).

We have talked extensively about some of the biggest changes brought on by artificial intelligence technology over the last few years. Two of the many fields that has been affected by breakthroughs in AI is web development and mobile application development. We previously talked about some of the benefits of using AI to create great mobile applications. However, these applications may not always meet your needs. Fortunately, AI technology has also led to the inception of a number of browser-based solutions that can be worthy alternatives.

Artificial intelligence is a game-changing development in the world of technology, and AI apps are regarded as one of the most significant breakthroughs in the field of information and technology. The AI App demonstrates the proclivity of human intelligence and its ability to take things to the next level in the world of science. To put it simply, artificial intelligence apps attempt to mimic the best way possible for humans to behave. These apps are said to shorten the time and effort required to complete a specific task or project. AI apps are used in a variety of applications ranging from business management to the healthcare system and so on. Regardless of their capabilities, you will find these Artificial Intelligence apps useful in making your work easier.

Not long ago, the scientific community laughed at the idea that animals might have their own languages. Today, researchers around the globe are using cutting-edge technology to listen in on animal "conversations" and even communicate with them. In her new book The Sounds of Life: How Digital Technology is Bringing Us Closer to the Worlds of Animals and Plants, University of British Columbia professor Karen Bakker outlines some of the most ground-breaking experiments in animal and plant communication. "Digital technologies, so often associated with our alienation from nature, are offering us an opportunity to listen to nonhumans in powerful ways, reviving our connection to the natural world," writes Bakker, a director at the UBC Institute for Resources, Environment, and Sustainability. She points out that digital listening posts are now being used to continuously record the sounds of ecosystems around the planet, from rainforests to the bottom of the ocean.

Let's look at the ROS system from a very high level view. No need to worry how any of the following works, we will cover that later. ROS starts with the ROS Master. The Master allows all other ROS pieces of software (Nodes) to find and talk to each other. That way, we do not have to ever specifically state "Send this sensor data to that computer at 127.0.0.1. We can simply tell Node 1 to send messages to Node 2. How do Nodes do this?

Imagine a team of autonomous drones equipped with advanced sensing equipment, searching for smoke as they fly high above the Sierra Nevada mountains. But what would happen if one or more leader robots was hacked by a malicious agent and began sending incorrect directions? As follower robots are led farther from the fire, how would they know they had been duped? The use of blockchain technology as a communication tool for a team of robots could provide security and safeguard against deception, according to a study by researchers at MIT and Polytechnic University of Madrid, which was published today in IEEE Transactions on Robotics. The research may also have applications in cities where multirobot systems of self-driving cars are delivering goods and moving people across town.

Google hasn't given up on its video game streaming service. In a blog post, the company announced a slew of small but welcome features that are rolling out now to Stadia. The biggest addition is messaging -- that's right, simple text-based messages -- with friends and party members. The feature started rolling out last week, but now it's available in every region that Stadia currently supports. If you're using the Stadia controller or any other gamepad, you should also be able to access predictive'smart replies.' Google has made it easier to share your best gaming moments, too. If you record a video clip, for instance, it will now be captured with anything you were barking into a microphone.

Invented by Geoffrey Hinton in 1985, Restricted Boltzmann Machine which falls under the category of unsupervised learning algorithms is a network of symmetrically connected neuron-like units that make stochastic decisions. This deep learning algorithm became very popular after the Netflix Competition where RBM was used as a collaborative filtering technique to predict user ratings for movies and beat most of its competition. It is useful for regression, classification, dimensionality reduction, feature learning, topic modelling and collaborative filtering. Restricted Boltzmann Machines are stochastic two layered neural networks which belong to a category of energy based models that can detect inherent patterns automatically in the data by reconstructing input. They have two layers visible and hidden.