MAROKO133 Hot ai: AI denial is becoming an enterprise risk: Why dismissing “slop” obscures

📌 MAROKO133 Breaking ai: AI denial is becoming an enterprise risk: Why dismissing

Three years ago, ChatGPT was born. It amazed the world and ignited unprecedented investment and excitement in AI. Today, ChatGPT is still a toddler, but public sentiment around the AI boom has turned sharply negative. The shift began when OpenAI released GPT-5 this summer to mixed reviews, mostly from casual users who, unsurprisingly, judged the system by its surface flaws rather than its underlying capabilities.

Since then, pundits and influencers have declared that AI progress is slowing, that scaling has “hit the wall,” and that the entire field is just another tech bubble inflated by blusterous hype. In fact, many influencers have latched onto the dismissive phrase “AI slop” to diminish the amazing images, documents, videos and code that frontier AI models generate on command.

This perspective is not just wrong, it is dangerous.

It makes me wonder, where were all these “experts” on irrational technology bubbles when electric scooter startups were touted as a transportation revolution and cartoon NFTs were being auctioned for millions? They were probably too busy buying worthless land in the metaverse or adding to their positions in GameStop. But when it comes to the AI boom, which is easily the most significant technological and economic transformation agent of the last 25 years, journalists and influencers can’t write the word “slop” enough times. 

Doth we protest too much?  After all, by any objective measure AI is wildly more capable than the vast majority of computer scientists predicted only five years ago and it is still improving at a surprising pace. The impressive leap demonstrated by Gemini 3 is only the latest example. At the same time, McKinsey recently reported that 20% of organizations already derive tangible value from genAI. Also, a recent survey by Deloitte indicates that 85% of organizations boosted their AI investment in 2025, and 91% plan to increase again in 2026.

This doesn’t fit the “bubble” narrative and the dismissive “slop” language. As a computer scientist and research engineer who began working with neural networks back in 1989 and tracked progress through cold winters and hot booms ever since, I find myself amazed almost every day by the rapidly increasing capabilities of frontier AI models. When I talk with other professionals in the field, I hear similar sentiments. If anything, the rate of AI advancement leaves many experts feeling overwhelmed and frankly somewhat scared.  

The dangers of AI denial

So why is the public buying into the narrative that AI is faltering, that the output is “slop,” and that the AI boom lacks authentic use cases? Personally, I believe it’s because we’ve fallen into a collective state of AI denial, latching onto the narratives we want to hear in the face of strong evidence to the contrary. Denial is the first stage of grief and thus a reasonable reaction to the very disturbing prospect that we humans may soon lose cognitive supremacy here on planet earth. In other words, the overblown AI bubble narrative is a societal defense mechanism.  

Believe me, I get it. I’ve been warning about the destabilizing risks and demoralizing impact of superintelligence for well over a decade, and I too feel AI is getting too smart too fast. The fact is, we are rapidly headed towards a future where widely available AI systems will be able to outperform most humans in most cognitive tasks, solving problems faster, more accurately and yes, more creatively than any individual can. I emphasize “creativity” because AI denialists often insist that certain human qualities (particularly creativity and emotional intelligence) will always be out of reach of AI systems. Unfortunately, there is little evidence supporting this perspective.

On the creativity front, today’s AI models can generate content faster and with more variation than any individual human. Critics argue that true creativity requires inner motivation. I resonate with that argument but find it circular — we're defining creativity based on how we experience it rather than the quality, originality or usefulness of the output. Also, we just don’t know if AI systems will develop internal drives or a sense of agency. Either way, if AI can produce original work that rivals most human professionals, the impact on creative jobs will still be quite devastating.

The AI manipulation problem

Our human edge around emotional intelligence is even more precarious. It’s likely that AI will soon be able to read our emotions faster and more accurately than any human, tracking subtle cues in our micro-expressions, vocal patterns, posture, gaze and even breathing. And as we integrate AI assistants into our phones, glasses and other wearable devices, these systems will monitor our emotional reactions throughout our day, building predictive models of our behaviors. Without strict regulation, which is increasingly unlikely, these predictive models could be used to target us with individually optimized influence that maximizes persuasion.

This is called the AI manipulation problem and it suggests that emotional intelligence may not give humanity an advantage. In fact, it could be a significant weakness, fostering an asymmetric dynamic where AI systems can read us with superhuman accuracy, while we can’t read AI at all. When you talk with photorealistic AI agents (and you will) you’ll see a smiling façade designed to appear warm, empathic and trustworthy. It will look and feel human, but that’s just an illusion, and it could easily sway your perspectives. After all, our emotional reactions to faces are visceral reflexes shaped by millions of years of evolution on a planet where every interactive human face we encountered was actually human. Soon, that will no longer be true.

We are rapidly heading toward a world where many of the faces we encounter will belong to AI agents<a href="https://www….

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🔗 Sumber: venturebeat.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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