MAROKO133 Hot ai: Trump’s Chip Embargo Against China Is Backfiring Spectacularly Hari Ini

📌 MAROKO133 Update ai: Trump’s Chip Embargo Against China Is Backfiring Spectacula

In a probably-predictable twist of geopolitical irony, America’s effort to block China’s access to cutting-edge AI chips hasn’t succeeded in stifling the People’s Republic — but instead forged a more self-reliant alternative to Silicon Valley that has investors jumping ship.

As reported by Reuters, global investors are putting more of their money into Chinese tech companies due to concerns over the size of Wall Street’s AI bubble, which continues to grow unabated.

Though large language models (LLMs) built by Chinese tech firms lag modestly behind the capabilities of those made by US companies, investors aren’t necessarily approaching them as a plan B. As Reuters points out, growing demand for Chinese tech stocks is being fueled by Beijing lawmakers’ drive for tech independence just as much as it is the American AI bubble.

A UBS Global Wealth Management report from earlier this month rated Chinese tech as “most attractive” to investors, the highest rating it dishes out in its global asset class assessments. The UBS researchers note that tech financiers are drawn by China’s “strong policy backing, technological self-reliance, and rapid AI-monetization.”

“China’s tech sector ramped up innovation in 2025, with notable advances across the AI value chain,” the report stated. “New Chinese AI models have shown tech leadership, and supportive policy is reinforcing ecosystem resilience.”

Sure enough, Reuters notes that institutional investment firms like the UK’s Ruffer are increasing their investments in Chinese tech giants like Alibaba, while chasing a strategy of “deliberately limited exposure” to the top US tech giants.

“While the US remains the leader in frontier AI, China is rapidly narrowing the gap,” Gemma Cairns-Smith, investment specialist at Ruffer told Reuters. “The moat may not be as wide, or as deep, as many think… The competitive landscape is shifting.”

Notably, the shift in attitude comes after years of anti-China trade policy courtesy of US presidents Joe Biden and Donald Trump. The executive efforts, meant to limit Chinese tech company’s access to AI chips built by Nvidia — the most powerful on the market — have hit a fever pitch during the second Trump administration.

Back in April, Trump imposed new trade restrictions on the sale of certain Nvidia AI chips to the People’s Republic. This included the H20 chip, which the company had previously nerfed for the Chinese market in order to appease US lawmakers. Beijing lawmakers soon retaliated by banning top tech companies from importing Nvidia chips, giving a huge boost to Chinese chip makers.

In a further desperate bid to outmaneuver Beijing, Trump quietly reversed the decision on H20 chips in early December, though the damage may already be done. In a technological competition with razor-thin margins, Trump’s maneuvers may be too little and too late to win anxious investors back to Silicon Valley.

More on China: Nvidia CEO Says China Is “Going to Win” the AI Race

The post Trump’s Chip Embargo Against China Is Backfiring Spectacularly appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Eksklusif ai: Which Agent Causes Task Failures and When?Researchers fr

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Meet the authors
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 failure-responsible agent and the decisive error step that led to the task’s failure.
2. 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 models, like OpenAI o1 and DeepSeek R1, find this task challenging.- This h…

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🔗 Sumber: syncedreview.com