MAROKO133 Breaking ai: Robin Williams’ Daughter Disgusted by AI Slop of Her Father Edisi J

📌 MAROKO133 Update ai: Robin Williams’ Daughter Disgusted by AI Slop of Her Father

Zelda Williams, daughter of the late Hollywood comedy icon Robin Williams, has had enough of people sending her AI slop videos of her father.

“Please, just stop sending me AI videos of Dad,” the director wrote in a Stories post on Instagram. “Stop believing I wanna see it or that I’ll understand, I don’t and I won’t.”

“If you’re just trying to troll me, I’ve seen way worse, I’ll restrict and move on,” she added. “But please, if you’ve got any decency, just stop doing this to him and to me, to everyone, even, full stop.”

Williams’ comments come a week after ChatGPT maker OpenAI launched Sora 2, a TikTok-like text-to-video generating app that serves up AI slop to the masses.

Users got to work, immediately generating photorealistic videos of deceased celebrities, including pop icon Michael Jackson, cosmologist Stephen Hawking, and painter Bob Ross. 

Whether dead celebs are fair game on Sora 2 remains unclear. OpenAI promised to “block depictions of public figures” in its user policy. However, a spokesperson told PCMag that it does “allow the generation of historical figures.”

“To watch the legacies of real people be condensed down to ‘this vaguely looks and sounds like them so that’s enough,’ just so other people can churn out horrible TikTok slop puppeteering them is maddening,” Williams wrote.

“You’re not making art, you’re making disgusting, over-processed hotdogs out of the lives of human beings, out of the history of art and music, and then shoving them down someone else’s throat hoping they’ll give you a little thumbs up and like it,” she added. “Gross.”

Williams’ comments highlight growing disillusionment at companies shoving AI into every aspect of users’ lives. Particularly when it comes to the substitution of human creativity, critics have balked at the emergence of entire social media apps dedicated to pumping out slop, like Sora 2, Meta’s Vibes, and YouTube, which has encouraged users to AI-generate short-form videos.

“AI is just badly recycling and regurgitating the past to be reconsumed,” Williams wrote. “You are taking in the Human Centipede of content, and from the very, very end of the line, all while the folks at the front laugh and laugh, consume and consume.”

It’s not the first time Williams has spoken out against the use of AI in creative fields. In 2023, she spoke out in support of striking Hollywood actors, who were fighting for protections against AI.

At the time, tech to recreate an actor’s voice was hitting its stride, triggering a fiery debate surrounding an artist’s ownership over their own voice.

“I am not an impartial voice in [the Screen Actors’ Guild’s] fight against AI,” she wrote at the time. “I’ve witnessed for YEARS how many people want to train these models to create/recreate actors who cannot consent, like Dad.”

“This isn’t theoretical, it is very, very real,” she added. “I’ve already heard AI used to get his ‘voice’ to say whatever people want and while I find it personally disturbing, the ramifications go far beyond my own feelings.”

Williams isn’t the first to protest her parents’ legacy being turned into slop. Last year, Kelly Carlin, the daughter of late comedian George Carlin, similarly took aim at the tech.

“My dad spent a lifetime perfecting his craft from his very human life, brain and imagination,” she tweeted at the time, responding to the announcement of an hour-long comedy special that featured an AI version of her father’s voice.

“No machine will ever replace his genius,” she wrote. “These AI-generated products are clever attempts at trying to recreate a mind that will never exist again.”

“Let’s let the artist’s work speak for itself,” she added. “Here’s an idea, how about we give some actual living human comedians a listen to?”

More on AI slop: Taylor Swift Fans Furious as She’s Caught Using Sloppy AI in Video for New Album

The post Robin Williams’ Daughter Disgusted by AI Slop of Her Father appeared first on Futurism.

🔗 Sumber: futurism.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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