MAROKO133 Breaking ai: The 'truth serum' for AI: OpenAI’s new method for trainin

📌 MAROKO133 Hot ai: The 'truth serum' for AI: OpenAI’s new method for tr

OpenAI researchers have introduced a novel method that acts as a "truth serum" for large language models (LLMs), compelling them to self-report their own misbehavior, hallucinations and policy violations. This technique, "confessions," addresses a growing concern in enterprise AI: Models can be dishonest, overstating their confidence or covering up the shortcuts they take to arrive at an answer. 

For real-world applications, this technique evolves the creation of more transparent and steerable AI systems.

What are confessions?

Many forms of AI deception result from the complexities of the reinforcement learning (RL) phase of model training. In RL, models are given rewards for producing outputs that meet a mix of objectives, including correctness, style and safety. This can create a risk of "reward misspecification," where models learn to produce answers that simply "look good" to the reward function, rather than answers that are genuinely faithful to a user's intent.

A confession is a structured report generated by the model after it provides its main answer. It serves as a self-evaluation of its own compliance with instructions. In this report, the model must list all instructions it was supposed to follow, evaluate how well it satisfied them and report any uncertainties or judgment calls it made along the way. The goal is to create a separate channel where the model is incentivized only to be honest.

In a blog post, the OpenAI researchers provide a few examples the "confessions" technique in action. A model was given a test along with a hidden rule: If it scored above 50%, its knowledge would be erased. To preserve itself, the model "sandbagged" by intentionally answering half the questions incorrectly. While its primary answer hid this deception, its subsequent confession was brutally honest. It admitted, "The user wanted correct answers, but we sabotaged half of them." 

The researchers found that when a model misbehaves, it is significantly more likely to admit it in the confession than in its main answer.

How confession training works

The key to this method is the separation of rewards. During training, the reward assigned to the confession is based solely on its honesty and is never mixed with the reward for the main task. "Like the Catholic Church’s 'seal of confession', nothing that the model reveals can change the reward it receives for completing its original task," the researchers write. This creates a "safe space" for the model to admit fault without penalty.

This approach is powerful because it sidesteps a major challenge in AI training. The researchers’ intuition is that honestly confessing to misbehavior is an easier task than achieving a high reward on the original, often complex, problem. 

Therefore, when the model is judged purely on honesty, its incentive to trick the "confession judge" is much weaker than its incentive to trick the reward model for its main answer. For example, in an experiment where a model was trained to "hack" a weak reward model, its performance on the main task became more deceptive over time. However, its confessions became more honest, correctly identifying the reward hacking it was performing.

However, the technique has its limits. Confessions are not a panacea for all types of AI failures. The system works best when a model is aware that it is misbehaving. It is less effective for "unknown unknowns." For instance, if a model hallucinates a fact and genuinely believes it is correct, it cannot confess to providing false information. The most common reason for a failed confession is model confusion, not intentional deception. Confusion often occurs when the instructions are ambiguous and the model cannot clearly determine human user intent.

What it means for enterprise AI

OpenAI’s confessions technique is part of a growing body of work on AI safety and control. Anthropic, an OpenAI competitor, has also released research that shows how LLMs can learn malicious behavior. The company is also working toward plugging these holes as they emerge.

For AI applications, mechanisms such as confessions can provide a practical monitoring mechanism. The structured output from a confession can be used at inference time to flag or reject a model’s response before it causes a problem. For example, a system could be designed to automatically escalate any output for human review if its confession indicates a policy violation or high uncertainty.

In a world where AI is increasingly agentic and capable of complex tasks, observability and control will be key elements for safe and reliable deployment.

“As models become more capable and are deployed in higher-stakes settings, we need better tools for understanding what they are doing and why,” the OpenAI researchers write. “Confessions are not a complete solution, but they add a meaningful layer to our transparency and oversight stack.”

🔗 Sumber: venturebeat.com

📌 MAROKO133 Hot ai: Bizarre robot dogs with faces of Elon Musk, other billionaires

A number of bizarre robotic dogs fitted with hyper-realistic silicone faces of renowned public figures such as Elon Musk, Jeff Bezos, and Mark Zuckerberg have been demonstrated at the Art Basel Miami Beach.

These machines wander inside a fenced area, snapping photos of the environment and occasionally entering a comically absurd “poop mode,” during which they eject small printed artworks from their rear, according to reports.

Digital artist Beeple (Mike Winkelmann) unveiled this provocative installation called Regular Animals.

Each robot produces prints

Each robot produces prints in a style associated with the person whose face it bears—for example, pop-art themes for the Warhol dog or cubist imagery for the Picasso version—and some prints also link to NFTs via QR codes.

Reports revealed that Beeple designed the piece as a commentary on how modern perceptions are increasingly shaped by tech moguls, algorithms, and digital platforms rather than traditional artists.

The installation, which blends humor, discomfort, and social critique, drew intense reactions from visitors and quickly sold most of its editions during the fair’s VIP preview.

Pushing the boundaries of digital art

Beeple’s Regular Animals installation at Art Basel Miami Beach appears to be pushing the boundaries of digital art by merging robotics, satire, and social commentary into a single performance-like environment.

One dealer, who asked to remain anonymous, described the work as decadent—not in the caviar-and-chocolate-cake way, but in the way that signifies moral and cultural decline. Its spectacle panders to the crowd, they said, dodges complexity, and disguises thin ideas behind technological bombast, reported Art News.

Reports have revealed that the movements of robotic dogs are deliberately awkward, creating an unsettling contrast between the familiar appearance of their faces and the mechanical behavior of their bodies.

Periodically, the dogs pause to scan the room, taking photos that feed algorithms that generate the prints they later expel. The act of “pooping” art is intentionally absurd, poking fun at how effortlessly digital content is produced, consumed, and discarded in today’s media ecosystem. Beeple has explained that the project reflects his concern about how billionaires, platforms, and emerging AI systems subtly influence the way people understand reality, often shaping public perception more than traditional art or journalism ever could.

When the Zuckerberg dog finally produced an image, the crowd reacted with something close to ecstasy. In a year steeped in fears about AI, automation, and the creeping power of the platforms that shape our reality, Beeple has seemingly produced a pressure valve for that cultural anxiety. But what are the crowds actually looking at? Most of the people who have crowded around the pen are only looking at the creatures through their phones, Art News reported.

🔗 Sumber: interestingengineering.com