MAROKO133 Eksklusif ai: Mark Zuckerberg’s Former Top AI Scientist Reveals Exactly Why He Q

📌 MAROKO133 Hot ai: Mark Zuckerberg’s Former Top AI Scientist Reveals Exactly Why

In a new interview with The Financial Times, Yann LeCun, one of the so-called godfathers of AI, finally dished on his abrupt exit from Meta in November.

From how he tells it, most of it boils down to his increasingly fraught relationship with CEO Mark Zuckerberg — and his new golden boy, Alexandr Wang, who ended up bossing LeCun around even though he’s nearly four decades younger.

LeCun had been at Zuckerberg’s company for over a decade, where, as chief AI scientist, he had the freedom to carry out all kinds of esoteric AI research without necessarily having to worry about developing a profitable product. LeCun described Meta, then Facebook, as a “tabula rasa with a carte blanche.” “Money was clearly not going to be a problem,” he told the FT.

Then, in November 2022, ChatGPT came out,  and the whole world went bananas for AI chatbots. AI chatbots and their human-like capabilities for conversation are powered by large language models, something LeCun helped pioneer with his foundational work on neural networks. When Zuckerberg ordered LeCun develop Meta’s own LLM, he agreed under the condition that Llama would be open source and free. 

The Llama models “changed the entire industry,” LeCun said, and were a hit with AI researchers because of their power and open source nature.

The success didn’t last, though; the latest Llama 4 model, released last April, was dead on arrival and reviled as an instantly-outdated flop. LeCun blames the failure on Zuckerberg pressuring LeCun’s unit to accelerate AI development.

“We had a lot of new ideas and really cool stuff that they should implement. But they were just going for things that were essentially safe and proved,” LeCun told the FT. “When you do this, you fall behind.”

The rift, however, goes deeper. LeCun views LLMs as a “dead end” for building even more powerful, “superintelligent” models that rival or surpass human capabilities. An entirely different architecture called “world models” which seek to understand the physical world, not just language, is needed to make the next major leap in the tech.

According to LeCun, Zuckerberg actually liked LeCun’s world model research, but didn’t put his money where his mouth is. Instead, Zuckerberg launched a new LLM-focused Superintelligence Labs last year, separate from LeCun’s lab, and offered several hundred million dollar contracts to attract top talent. All the talent that came in, LeCun complained, have been “completely LLM-pilled.”

Zuckerberg’s marquee new-hire was Alexandr Wang, the founder and former CEO of the AI data annotation startup Scale AI, which provides an essential service for training AI models, but doesn’t build or design them. Zuckerberg poured $14 billion into Scale AI to buy a 49 percent stake and, as part of that deal, Wang left and joined Meta to lead the new Superintelligence Labs. As a consequence, LeCun was forced to start reporting to Wang.

The move raised questions from the get-go, including whether Wang, 29, had the experience and background to build massive AI models, something his company didn’t do. LeCun doesn’t leave us wondering where he stands on Wang’s hiring, calling him “young” and “inexperienced.” 

Be that as it may, LeCun, considered to be a godfather of the entire field, was now taking orders from Wang. LeCun seemed cool about this at first when the interviewer brought up the new hierarchy. “The average age of a Facebook engineer at the time was 27,” Lecun told the FT. “I was twice the age of the average engineer.”

But when the interviewer pointed out that the younger generation weren’t telling him what to do until the 29-year-old Wang showed up, LeCun seemingly let his true feelings be known.

“Alex isn’t telling me what to do either,” LeCun sneered. “You don’t tell a researcher what to do. You certainly don’t tell a researcher like me what to do.”

He’ll be his own boss going forward. LeCun has launched a new world-model-focused startup called Advanced Machine Intelligence Labs, which is targeting a $3 billion valuation. LeCun will serve as executive chairman, allowing him a similar degree of freedom to pursue research he once enjoyed at Meta, according to the FT.

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🔗 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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