MAROKO133 Update ai: Uncles Tremble as Man Invents Vaccine Delivered by Beer Hari Ini

📌 MAROKO133 Breaking ai: Uncles Tremble as Man Invents Vaccine Delivered by Beer T

We have some bad news for your conspiracy-brained antivaxxer uncle — one virologist claims he’s come up with a way to administer vaccines through a frothy mug of beer.

By day, virologist Chris Buck works for the National Cancer Institute (NCI) in Maryland, where he’s discovered four of the 13 polyomaviruses we know to affect humans, Science News reports. But by night, he runs Gusteau Research Corporation, a one-man shell company he established so he could experiment on his bubbly inoculation: an ingestible polyomavirus vaccine.

To make the beer, Buck engineered a special strain of yeast infused with polyomavirus-like particles. Similar particles, delivered via purified insect chitin, have successfully increased antibody levels in rhesus monkeys tested in India, according a 2023 research study published in the journal Vaccine.

Importantly, Buck’s engineered yeast doesn’t contain live viruses. Consensus among researchers is that they aren’t viable for building ingestible vaccines, as they would simply disintegrate when they make contact with stomach acids, per Science News.

Yet when the virologist and his team attached virus-like particles to live yeast, they discovered the organisms could carry the inoculation load well beyond the stomach of live mice. That had huge implications for inoculation against polyomaviruses, which are mostly found in the urinary track, Buck told Science News.

“We repeated this experiment [on mice] a couple of times. I was reluctant to believe it,” Buck said at the World Vaccine Congress Washington earlier this year. “It felt like an earthquake when I first saw the results emerging.”

Since then, Buck himself has chugged five pints of the brew, along with his brother and other family members.

Buck says that after drinking the experimental suds, antibodies for two of the four subtypes of BK polyomavirus in his blood have reached a safemedical threshold for transplant patients.

Buck’s approach has brewed upsome controversy, to be sure. Two separate panels of experts — a research and an ethics committee — with the National Institute of Health have come out against Buck experimenting on himself with his homebrew in his official capacity as a virologist (hence the shell company, which allows him to experiment as a private business owner).

Though a number of researchers canvassed by Science News agreed that Buck’s style of ingestible vaccine experiments are sorely needed, they worry his cavalier attitude might backfire, making certain anti-vaxxers even more paranoid than they already were. Just imagine: what’s to stop them from dumping vaccines into cans of Budweiser?

“Coming up with new modes of administration of vaccines is way overdue,” Arthur Caplan, former head of medical ethics at the NYU Grossman School of Medicine told Science News. Still, he added that the virologist’s homebrew could “take a good idea he has and ruin it… vaccine doubts and fears and anti-vaccine attitudes could easily undercut what could be something useful.”

Writing in a non-peer reviewed essay posted on his personal blog, Buck said that he doesn’t take the controversy personally. “The basic problem for vaccine scientists has been our collective failure to understand the anti-vaxxer viewpoint,” he wrote.

“Our response for the past half century has been to imagine that we can rebuild public trust in vaccines with displays of increasingly stringent FDA approval standards. This approach backfired,” Buck pontificates. “Imagine if I set out to do safety testing on a banana, and I dressed up in a hazmat suit and handled the banana with tongs… you’d think: ‘wow, it looks like bananas might be about as safe as nuclear waste.’ All the elaborate security theater we’ve been doing ended up putting anti-vaxxers in charge of the FDA.”

More on vaccines: Man Whose Daughter Died From Measles Stands by Failure to Vaccinate Her: “The Vaccination Has Stuff We Don’t Trust”

The post Uncles Tremble as Man Invents Vaccine Delivered by Beer appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Hot ai: Researchers from PSU and Duke introduce “Multi-Agent Systems A

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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