MAROKO133 Hot ai: Tesla Tells Sleepy Drivers to Switch to Its Self-Driving Mode That Needs

📌 MAROKO133 Eksklusif ai: Tesla Tells Sleepy Drivers to Switch to Its Self-Driving

Let’s be clear: Tesla’s cars can’t actually drive themselves without close human supervision. Nonetheless, the automaker labels its most advanced driving mode “Full Self-Driving” (FSD), while its CEO and chief overpromiser Elon Musk explicitly says that they do, in fact, “drive themselves” seemingly every other week.

We shouldn’t even need to say that these misleading impressions of the tech’s capabilities are incredibly dangerous. But if Tesla has a long track record of being reckless, this latest move from it is actively begging for a horrific accident to happen. 

According to new reporting from Wired, the Musk-led company has started recommending drowsy motorists to switch over to FSD whenever it detects them falling asleep behind the wheel. These recommendations, per the magazine, are delivered as in-car messages that pop up on the infotainment screen, which owners began noticing earlier this month.

“Lane drift detected. Let FSD assist so you can stay focused,” reads one message cited by the magazine.

“Drowsiness detected. Stay focused with FSD,” read another.

Tesla has had lane-departure warnings for years, But now they’re an unashamed advertisement for FSD, encouraging already half-slumbering drivers to hand over the controls to an unreliable piece of software that lulls them into a false sense of security, if not back to sleep. It’s an approach fraught with contradictions, since, as Tesla warns in the fine print — as well as in the conspicuous “(Supervised)” label it tacked onto the mode’s name — FSD requires you to be constantly alert.

“Tesla is basically giving a series of conflicting instructions,” Alexandra Mueller, a senior research scientist at the Insurance Institute for Highway Safety who studies driver assistance technologies, told Wired. “When you suspect the driver is becoming drowsy, to remove even more of their physical engagement — that seems extremely counterproductive.”

The fundamental problem with any tech that does most of the work for you is that it makes you complacent. If it functions properly most of the time, you’re less likely to be ready to intervene when it does screw up. There’s a name for this phenomenon in aviation, Wired notes: the “out-of-the-loop performance problem,” in which pilots become so accustomed to a plane’s sophisticated systems taking care of everything that they gradually let their guard down and fail to notice when something goes wrong.

“As humans, as we get tired or we get fatigued, taking away more things that we need to do could actually backfire,“ warned Charlie Klauer, a researcher and engineer at the Virginia Tech Transportation Institute, to Wired.

But pilots are highly trained individuals who fly for a living, and they often have a copilot. Tesla drivers — specifically the kind that would use FSD — are Musk fans that believe in his overblown promises so much that they’re literally willing to put their life on the line to help iron out the kinks in a piece of software that has a habit of trying to drive straight into oncoming trains. Many of them, we should add, have already been caught on camera taking a nap at wheel.

It’s an especially eyebrow raising moment for Tesla to begin brazenly recommending FSD to drowsy drivers. In August, it was ordered to pay $329 million in damages after a car running an older version of its driving software, Autopilot, blazed through an intersection at 60 miles per hour and killed a young woman.

It’s also the subject of a federal investigation that launched after multiple reports of its cars crashing while running FSD — including one incident in which a Tesla’s onboard camera clearly shows it mowing down an elderly pedestrian on the side of the road while a beam of sunlight washed out its view.

And its misleading statements haven’t gone unnoticed by local regulators, either: the California DMV sued the EV company for false advertising, pointing to FSD’s misleading name. All the while, its struggling Robotaxi service that launched this summer in Austin, Texas, has only emphasized just how faulty its driving tech remains, with the purportedly autonomous cabs blundering into several accidents. Talk about a wake-up call.

More on Tesla: Tesla Fans Try Coast-to-Coast Self-Driving Trip, Crash Almost Immediately

The post Tesla Tells Sleepy Drivers to Switch to Its Self-Driving Mode That Needs to Be Monitored Constantly So It Doesn’t Cause a Fatal Accident appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Eksklusif ai: Researchers from PSU and Duke introduce “Multi-Agent Sys

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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