MAROKO133 Breaking ai: Startup Reveals “Space Armor” to Protect Astronauts From Elon Musk’

📌 MAROKO133 Update ai: Startup Reveals “Space Armor” to Protect Astronauts From El

Never content to pollute only the Earth’s surface, oceans, and atmosphere, billionaire tech moguls like Elon Musk are increasingly flooding our planet’s orbit with debris from their for-profit space ventures.

SpaceX, for example, already has some 8,600 active Starlink satellites doing laps around the planet, a number that needs constant reinforcements as craft either tumble back down to Earth or join the 25,000 bits of identifiable junk already floating in orbit. Just this week, Musk’s SpaceX launched 21 relay satellites into low Earth orbit on behalf of the US Space Force, after sending another 28 Starlink craft to join his ever-growing constellation.

Though there are 25,000 tracked pieces of garbage floating through our skies, researchers estimate there may be as many as 170 million bits of smaller debris too small to track, but with the same capacity to wipe out critical infrastructure. In that increasingly crowded field, mid-space collisions are becoming more and more likely; as of late 2024, Business Insider reported that space-traffic controllers were issuing 1,000 collision warnings per day.

Now, one enterprising startup has developed a way to keep satellites and astronauts safe from all that garbage — at least until someone can clean it up.

It’s called “space armor,” a material made using a composite-to-resin method by the American aerospace manufacturing company, Atomic-6.

In a factsheet, Atomic-6 describes space armor is described as a series of lightweight tiles which protect craft and astronauts against “all untrackable debris” under 3mm in size, and 90 percent of the debris currently in low earth orbit.

“Satellites and astronauts are constantly threatened by millions of untrackable, hypervelocity particles in orbit,” the company told Space.com. “Like a loose pebble hitting your windshield on the highway, orbital debris can strike at any time to do significant damage to spacecraft.”

Though that was the only time the company brought up astronauts, Atomic-6 claims that space armor fixes a problem with traditional satellite shielding, which can break off into “secondary” rubble. While older-style space shields can absorb hits and protect craft in the short-term, they end up adding to the pile of junk zipping around our atmosphere, making them a poor long-term solution.

“It has taken around 18 months to take Space Armor tiles from an idea to a final product,” Atomic-6 CEO Trevor Smith told Space.com, noting that the tiles have withstood significant projectile testing on the ground. “We offer Space Armor in simple hex tiles, but we can technically make Space Armor into most any shape you want.”

In addition to dangerous debris, Atomic-6 claims its space armor is a solution to the “growing threat” of “adversarial spacecraft,” a reference to Russian and Chinese space programs. (To date, no kinetic attack on a rival spacecraft has ever been recorded, though spacefaring countries have scuttled their own satellites.)

Smith said satellites equipped with space armor will begin launching into orbit as soon as 2026, ready to defend craft from Musk’s increasingly toxic presence in space. Whether we’ll ever start seeing astronauts decked out in sheets of space armor? Only time will tell.

More on satellites: Researchers Alarmed to Discover Satellites Broadcasting Unencrypted Military Secrets

The post Startup Reveals “Space Armor” to Protect Astronauts From Elon Musk’s Orbital Trash appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Eksklusif ai: Researchers from PSU and Duke introduce “Multi-Agent Sys

Share My Research is Synced’s column that welcomes scholars to share their own research breakthroughs with over 2M global AI enthusiasts. Beyond technological advances, Share My Research also calls for interesting stories behind the research and exciting research ideas. 

Meet the author
Institutions: Penn State University, Duke University, Google DeepMind, University of Washington, Meta, Nanyang Technological University, and Oregon State University. The co-first authors are Shaokun Zhang of Penn State University and Ming Yin of Duke University.

In recent years, LLM Multi-Agent systems have garnered widespread attention for their collaborative approach to solving complex problems. However, it’s a common scenario for these systems to fail at a task despite a flurry of activity. This leaves developers with a critical question: which agent, at what point, was responsible for the failure? Sifting through vast interaction logs to pinpoint the root cause feels like finding a needle in a haystack—a time-consuming and labor-intensive effort.
 
This is a familiar frustration for developers. In increasingly complex Multi-Agent systems, failures are not only common but also incredibly difficult to diagnose due to the autonomous nature of agent collaboration and long information chains. Without a way to quickly identify the source of a failure, system iteration and optimization grind to a halt.
 
To address this challenge, researchers from Penn State University and Duke University, in collaboration with institutions including Google DeepMind, have introduced the novel research problem of “Automated Failure Attribution.” They have constructed the first benchmark dataset for this task, Who&When, and have developed and evaluated several automated attribution methods. This work not only highlights the complexity of the task but also paves a new path toward enhancing the reliability of LLM Multi-Agent systems.
The paper has been accepted as a Spotlight presentation at the top-tier machine learning conference, ICML 2025, and the code and dataset are now fully open-source.

Paper：https://arxiv.org/pdf/2505.00212
Code：https://github.com/mingyin1/Agents_Failure_Attribution
Dataset：https://huggingface.co/datasets/Kevin355/Who_and_When
 
 
Research Background and Challenges
LLM-driven Multi-Agent systems have demonstrated immense potential across many domains. However, these systems are fragile; errors by a single agent, misunderstandings between agents, or mistakes in information transmission can lead to the failure of the entire task.

Currently, when a system fails, developers are often left with manual and inefficient methods for debugging:
Manual Log Archaeology : Developers must manually review lengthy interaction logs to find the source of the problem.
Reliance on Expertise : The debugging process is highly dependent on the developer’s deep understanding of the system and the task at hand.
 
This “needle in a haystack” approach to debugging is not only inefficient but also severely hinders rapid system iteration and the improvement of system reliability. There is an urgent need for an automated, systematic method to pinpoint the cause of failures, effectively bridging the gap between “evaluation results” and “system improvement.”

Core Contributions
This paper makes several groundbreaking contributions to address the challenges above:
1. Defining a New Problem: The paper is the first to formalize “automated failure attribution” as a specific research task. This task is defined by identifying the

2. failure-responsible agent and the decisive error step that led to the task’s failure.

Constructing the First Benchmark Dataset: Who&When : This dataset includes a wide range of failure logs collected from 127 LLM Multi-Agent systems, which were either algorithmically generated or hand-crafted by experts to ensure realism and diversity. Each failure log is accompanied by fine-grained human annotations for:
Who: The agent responsible for the failure.
When: The specific interaction step where the decisive error occurred.
Why: A natural language explanation of the cause of the failure.

3. Exploring Initial “Automated Attribution” Methods : Using the Who&When dataset, the paper designs and assesses three distinct methods for automated failure attribution:
All-at-Once: This method provides the LLM with the user query and the complete failure log, asking it to identify the responsible agent and the decisive error step in a single pass. While cost-effective, it may struggle to pinpoint precise errors in long contexts.
Step-by-Step: This approach mimics manual debugging by having the LLM review the interaction log sequentially, making a judgment at each step until the error is found. It is more precise at locating the error step but incurs higher costs and risks accumulating errors.
Binary Search: A compromise between the first two methods, this strategy repeatedly divides the log in half, using the LLM to determine which segment contains the error. It then recursively searches the identified segment, offering a balance of cost and performance.
 
Experimental Results and Key Findings

Experiments were conducted in two settings: one where the LLM knows the ground truth answer to the problem the Multi-Agent system is trying to solve (With Ground Truth) and one where it does not (Without Ground Truth). The primary model used was GPT-4o, though other models were also tested. The systematic evaluation of these methods on the Who&When dataset yielded several important insights:

• A Long Way to Go: Current methods are far from perfect. Even the best-performing single method achieved an accuracy of only about 53.5% in identifying the responsible agent and a mere 14.2% in pinpointing the exact error step. Some methods performed even worse than random guessing, underscoring the difficulty of the task.
• No “All-in-One” Solution: Different methods excel at different aspects of the problem. The All-at-Once method is better at identifying “Who,” while the Step-by-Step method is more effective at determining “When.” The Binary Search method provides a middle-ground performance.

• Hybrid Approaches Show Promise but at a High Cost: The researchers found that combining different methods, such as using the All-at-Once approach to identify a potential agent and then applying the Step-by-Step method to find the error, can improve overall performance. However, this comes with a significant increase in computational cost.

• State-of-the-Art Models Struggle: Surprisingly, even the most advanced reasoning m…

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  🔗 Sumber: syncedreview.com

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