MAROKO133 Update ai: The CDC Fired All Its Cruise Ship Inspectors Before the Hantavirus Ou

📌 MAROKO133 Breaking ai: The CDC Fired All Its Cruise Ship Inspectors Before the H

A deadly hantavirus outbreak tearing through a Dutch cruise ship has put health officials on alert, raising questions of virus preparedness as exposed travelers branch out all over the globe.

It’s also brought attention to an eyebrow-raising decision by the Trump administration made almost exactly a year ago. Targeted by sweeping cuts like other agencies as part of Elon Musk’s crusade to gut federal spending, the Centers for Disease Control and Prevention cleared out almost its entire Vessel Sanitation Program, a key group that ensures ships are properly sanitized to prevent them from becoming the type of plague frigate the world is now dealing with.

According to CBS News reporting back in April 2025, all full-time employees working on the VSP were fired, including the epidemiologist that led the CDC’s outbreak response on cruise ships.

Only a smaller group of twelve US Public Health Service officers stayed on board. And just a single epidemiologist, who was still in the early stages of their training, remained in the VSP team to investigate outbreaks at the time of reporting.

When People magazine reached out to the CDC about the layoffs in light of the cruise ship outbreak, a spokesperson insisted that the program was humming along.

“CDC’s Vessel Sanitation Program (VSP) remains fully staffed, including epidemiologists, and continues to carry out all core program activities for cruise ships under US jurisdiction,” the statement read. 

The meaning of “fully staffed” here, though, is vague. Have the ranks been refilled back to their original size, or is it “fully staffed” under the new normal following the new cost-cutting regime? 

In any case, it’s difficult to believe that it’s fully recovered from losing so much expertise. The CBS reporting notes it takes six months to train new cruise ship inspectors, a job that few are lining up for. One official describing the difficulties of recruiting for the positions said that inspectors have to follow a grueling travel schedule to inspect cruise ships and respond to outbreaks.

That the public health service officers who remained could pick up the slack was also dubious, given the amount of training required. On top of that, the program was already short-staffed to begin with, according to anonymous CDC officials who spoke to CBS.

The Dutch cruise ship, the MV Hondius, is not under US jurisdiction, so it wouldn’t have been inspected by the CDC program. Still, it’s an eerie reminder that health threats like these could end up on the US’ shore, making the questionable credentials of the country’s health officials all the more alarming.

With around 150 on board, three passengers have died on the ship since the hantavirus outbreak began, according to World Health Organization officials, with five others showing signs of being infected. The passengers were infected with an Andes strain of the virus, the only known strain that can spread from human to human.

As bad as the incident is, though, experts say it’s unlikely to spiral into a COVID-level disaster.

“This is not the start of an epidemic, this is not the start of a pandemic,” Maria Van Kerkhove, the WHO’s head of epidemic and pandemic preparedness, said Thursday.

More on pathogens: DOGE Made Drastic Cuts to a Global Vaccine Assistance Program. Now There’s a Deadly Measles Outbreak in Bangladesh

The post The CDC Fired All Its Cruise Ship Inspectors Before the Hantavirus Outbreak appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Update ai: MIT Researchers Unveil “SEAL”: A New Step Towards Self-Impr

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