MAROKO133 Eksklusif ai: Scientists announce major breakthrough in attempts to bring back 3

📌 MAROKO133 Eksklusif ai: Scientists announce major breakthrough in attempts to br

The quest for dodo’s de-extinction has gathered steam again.

Colossal Biosciences has announced a significant breakthrough in its attempts to bring the flightless bird back to life, 300 years after it became extinct. The Texas-based company has grown pigeon primordial cells for the first time; they are also regarded as precursor cells to sperm and eggs.

Announcing this breakthrough on Wednesday, the biotech firm also revealed that they have developed gene-edited chickens, which will act as surrogates for the dodos. Scientists will inject these chickens with special cells from Nicobar pigeons – the dodo’s closest relatives.

With gene edits, Colossal believes these chickens could one day breed dodos.

“This is the really important step for the dodo project, but also for bird conservation, more broadly,” said Beth Shapiro, Colossal’s chief science officer.

“This was a negating step for the dodo project. We needed this to move on, and now that we have it, really, we’re off and running,” he stated.

The process of resurrection

After growing the primordial germ cells, the Colossal scientists plan to edit these with dodo-specific genes. The edited cells will then be placed into sterile chicken embryos.

When these chickens breed, their offspring could produce eggs and sperm carrying dodo traits. Over generations, this process could yield living birds that resemble the long-extinct dodo.

Ben Lamm, Colossal’s chief executive, revealed how much time it would be before the world sees the results of their experiment.

“Rough ballpark, we think it’s still five to seven years out, but it’s not 20 years out,” he said.

Colossal is working with wildlife groups to identify safe, rat-free sites in Mauritius where the species could breathe again.

A timeline for the project

The genetic engineering company made headlines when it bagged a $150 million grant in 2023 to support its de-extinction efforts. The current progress collides with an additional $120 million funding extension, bringing Colossal’s total amount to $555 million in funding.

And, these efforts aren’t limited only to the dodos.

In the past, Colossal has also revealed plans to revive other species of extinct animals like the Tasmanian tiger, New Zealand’s giant moa, and woolly mammoths.

Earlier in April this year, Colossal claimed to have brought dire wolves out of extinction using a novel iterative genome assembly method. After embarking on the quest to bring back dodos, the company also teamed up with the Mauritian Wildlife Foundation to create native habitats in which the bird survived before its extinction.

Is ‘de-extinction’ possible?

It’s hard to believe something dead centuries ago could be brought back alive. Colossal’s researchers have faced a fair amount of criticism for their efforts, and they don’t exactly shy away from it either.

While critics believe it’s not possible to resurrect an extinct animal, the company has already cleared the air around this debate. It has said that the experiments don’t aim to bring back something that’s 100 percent genetically identical to the extinct species, but only to create copies with their key traits.

While that sounds believable, other risks are involved regarding the protection of the species used for such experiments, according to conservationists. It remains to be seen if Colossal can actually succeed and have the world in awe of science yet again.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Breaking ai: Researchers from PSU and Duke introduce “Multi-Agent Syst

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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