📌 TOPINDIATOURS Hot ai: World-1st laptop cooled by dielectric barrier discharge pl

A New Jersey-based company is set to unveil its noiseless laptop cooled with DBD (Dielectric Barrier Discharge) plasma actuators.

YPlasma will host the world premiere of the first-ever application of dielectric barrier discharge (DBD) plasma actuators for consumer electronics cooling—a revolutionary solution designed to replace mechanical fans and traditional ionic wind devices.

Traditional cooling methods are reaching their physical limits

The company underlines that as electronics become thinner and AI-driven processing demands more power, traditional cooling methods are reaching their physical limits.

YPlasma’s solid-state technology addresses these challenges by using cold plasma to generate high-velocity “ionic wind” without a single moving part.

DBD technology has been miniaturized

YPlasma claims that this is for the first time that the DBD technology has been miniaturized into a form factor that redefines hardware design. YPlasma’s actuators are essentially thin films, measuring as little as 200 microns in thickness. This paper-thin profile allows them to be integrated directly onto heat sinks, chassis walls, or internal components, enabling ultra-thin laptop designs that were previously impossible to cool.

Furthermore, these actuators are the first in the world capable of producing both cooling and heating within the same device, offering unprecedented thermal versatility.

First laptop cooled with our DBD plasma actuators

“Unveiling the first laptop cooled with our DBD plasma actuators marks a historic moment — not just for YPlasma, but for the entire electronics industry,” said David García Pérez, CEO and Co-Founder of YPlasma.

“We’re excited to engage with global partners and demonstrate what our technology can achieve.”

While many have explored “corona discharge” for ionic cooling, YPlasma’s DBD technology reportedly represents a fundamental leap forward in safety, reliability, and acoustics.

Noiseless operation

It performs truly noiseless operation by operating at an ultra-quiet 17dBA. YPlasma’s system is virtually inaudible to the human ear, eliminating the “fan whine” common in high-performance laptops.

Unlike corona discharge devices, which can produce harmful ozone byproducts, YPlasma’s DBD system uses a dielectric barrier to limit discharge, ensuring it is safe for consumer use in enclosed spaces.

The company also claims that the DBD eliminates “tip erosion,” the primary failure point for corona needles. YPlasma’s protected electrode design ensures the cooling system lasts the entire lifetime of the device.

“The AI era requires a complete rethink of how we manage heat and air,” García Pérez continued. “With our engineering teams in Madrid and Newark, we are bringing space-grade technology—packaged in a 200-micron film—to everything from your laptop to the next generation of aircraft.”

The company also highlighted that beyond consumer electronics, YPlasma’s DBD technology serves as a versatile platform for several critical industries. It delivers active flow control, which enhances safety for road vehicles, aircraft, and wind turbines to improve fuel efficiency and reduce drag. The technology can also help develop next-generation propulsion systems for UAVs and space exploration.

🔗 Sumber: interestingengineering.com

📌 TOPINDIATOURS Breaking ai: MIT Researchers Unveil “SEAL”: A New Step Towards Sel

The concept of AI self-improvement has been a hot topic in recent research circles, with a flurry of papers emerging and prominent figures like OpenAI CEO Sam Altman weighing in on the future of self-evolving intelligent systems. Now, a new paper from MIT, titled “Self-Adapting Language Models,” introduces SEAL (Self-Adapting LLMs), a novel framework that allows large language models (LLMs) to update their own weights. This development is seen as another significant step towards the realization of truly self-evolving AI.

The research paper, published yesterday, has already ignited considerable discussion, including on Hacker News. SEAL proposes a method where an LLM can generate its own training data through “self-editing” and subsequently update its weights based on new inputs. Crucially, this self-editing process is learned via reinforcement learning, with the reward mechanism tied to the updated model’s downstream performance.

The timing of this paper is particularly notable given the recent surge in interest surrounding AI self-evolution. Earlier this month, several other research efforts garnered attention, including Sakana AI and the University of British Columbia’s “Darwin-Gödel Machine (DGM),” CMU’s “Self-Rewarding Training (SRT),” Shanghai Jiao Tong University’s “MM-UPT” framework for continuous self-improvement in multimodal large models, and the “UI-Genie” self-improvement framework from The Chinese University of Hong Kong in collaboration with vivo.

Adding to the buzz, OpenAI CEO Sam Altman recently shared his vision of a future with self-improving AI and robots in his blog post, “The Gentle Singularity.” He posited that while the initial millions of humanoid robots would need traditional manufacturing, they would then be able to “operate the entire supply chain to build more robots, which can in turn build more chip fabrication facilities, data centers, and so on.” This was quickly followed by a tweet from @VraserX, claiming an OpenAI insider revealed the company was already running recursively self-improving AI internally, a claim that sparked widespread debate about its veracity.

Regardless of the specifics of internal OpenAI developments, the MIT paper on SEAL provides concrete evidence of AI’s progression towards self-evolution.

Understanding SEAL: Self-Adapting Language Models

The core idea behind SEAL is to enable language models to improve themselves when encountering new data by generating their own synthetic data and optimizing their parameters through self-editing. The model’s training objective is to directly generate these self-edits (SEs) using data provided within the model’s context.

The generation of these self-edits is learned through reinforcement learning. The model is rewarded when the generated self-edits, once applied, lead to improved performance on the target task. Therefore, SEAL can be conceptualized as an algorithm with two nested loops: an outer reinforcement learning (RL) loop that optimizes the generation of self-edits, and an inner update loop that uses the generated self-edits to update the model via gradient descent.

This method can be viewed as an instance of meta-learning, where the focus is on how to generate effective self-edits in a meta-learning fashion.

A General Framework

SEAL operates on a single task instance (C,τ), where C is context information relevant to the task, and τ defines the downstream evaluation for assessing the model’s adaptation. For example, in a knowledge integration task, C might be a passage to be integrated into the model’s internal knowledge, and τ a set of questions about that passage.

Given C, the model generates a self-edit SE, which then updates its parameters through supervised fine-tuning: θ′←SFT(θ,SE). Reinforcement learning is used to optimize this self-edit generation: the model performs an action (generates SE), receives a reward r based on LMθ′’s performance on τ, and updates its policy to maximize the expected reward.

The researchers found that traditional online policy methods like GRPO and PPO led to unstable training. They ultimately opted for ReST^EM, a simpler, filtering-based behavioral cloning approach from a DeepMind paper. This method can be viewed as an Expectation-Maximization (EM) process, where the E-step samples candidate outputs from the current model policy, and the M-step reinforces only those samples that yield a positive reward through supervised fine-tuning.

The paper also notes that while the current implementation uses a single model to generate and learn from self-edits, these roles could be separated in a “teacher-student” setup.

Instantiating SEAL in Specific Domains

The MIT team instantiated SEAL in two specific domains: knowledge integration and few-shot learning.

• Knowledge Integration: The goal here is to effectively integrate information from articles into the model’s weights.
• Few-Shot Learning: This involves the model adapting to new tasks with very few examples.

Experimental Results

The experimental results for both few-shot learning and knowledge integration demonstrate the effectiveness of the SEAL framework.

In few-shot learning, using a Llama-3.2-1B-Instruct model, SEAL significantly improved adaptation success rates, achieving 72.5% compared to 20% for models using basic self-edits without RL training, and 0% without adaptation. While still below “Oracle TTT” (an idealized baseline), this indicates substantial progress.

For knowledge integration, using a larger Qwen2.5-7B model to integrate new facts from SQuAD articles, SEAL consistently outperformed baseline methods. Training with synthetically generated data from the base Qwen-2.5-7B model already showed notable improvements, and subsequent reinforcement learning further boosted performance. The accuracy also showed rapid improvement over external RL iterations, often surpassing setups using GPT-4.1 generated data within just two iterations.

Qualitative examples from the paper illustrate how reinforcement learning leads to the generation of more detailed self-edits, resulting in improved performance.

While promising, the researchers also acknowledge some limitations of the SEAL framework, including aspects related to catastrophic forgetting, computational overhead, and context-dependent evaluation. These are discussed in detail in the original paper.

Original Paper: https://arxiv.org/pdf/2506.10943

Project Site: https://jyopari.github.io/posts/seal

Github Repo: https://github.com/Continual-Intelligence/SEAL

The post MIT Researchers Unveil “SEAL”: A New Step Towards Self-Improving AI first appeared on Synced.

🔗 Sumber: syncedreview.com