MAROKO133 Hot ai: Doctor Says Trump Appears to Be Showing Signs of a Stroke Hari Ini

📌 MAROKO133 Breaking ai: Doctor Says Trump Appears to Be Showing Signs of a Stroke

When Donald Trump won his second term in the White House, he was the oldest man in the history of the United States to do so. Assuming he serves for his entire term, he’ll be 82 years and seven months old by the time he leaves office — breaking Joe Biden’s record of 82 years and two months.

And like Biden, some major questions are swirling around Trump as obvious signs of aging come to the fore. Speaking to the biographer Sidney Blumenthal and historian Sean Wilentz on their podcast The Court of History, Washington State University Professor of Medicine Bruce Davidson opined that Trump appears to have suffered a stroke that’s impacting his ability to move.

“My impression is that president Trump has had a stroke, and I think there’s several lines of evidence supporting that,” Davidson said. “I think his stroke was on the left side of the brain, which controls the right side of the body.” Based on what he’s seen, the doctor estimated the event happened “six months ago or more, earlier in 2025.”

“There’s video of him shuffling his feet, which is not what we’d seen him [doing], striding on the golf course,” the doctor continued. “He was garbling words, which he didn’t do previously, and which he’s improved upon more recently. And he’s also had marked episodes that have been noticed of daytime, excessive sleepiness, — medical term, hypersomnolence — which is characteristic of many patients after they’ve had a stroke.”

The most recent smoking gun, Davidson claimed, was a recent video of Trump gingerly walking down the stairs from Air Force One. As he descends the steps, the president grips the banister with his left hand, even though he’s right-handed.

“All of this is consistent with having had a stroke on the left side of his brain,” Davidson said.

While the assertion has some important implications for Trump himself, it would also mean that his entire administration has been lying to the American public about his health for months — echoing the health debacle surrounding Biden in his final years in office. That said, Davidson’s definitely not the first person to suggest Trump’s hiding a stroke in plain sight.

As questions of the president’s visible bruising and swollen ankles dominated headlines last July, the White House put out a statement admitting he had been diagnosed with chronic venous insufficiency. In mid-October, Trump was taken for a mysterious check-up at Walter Reed Medical Center, which the White House claimed was a routine physical — even though it turned out Trump had gotten an MRI scan.

Earlier this month, Trump contradicted the administration’s earlier claims, stating that the trip involved a CT scan, not an MRI, which he underwent to “definitively rule out any cardiovascular issues.”

Without being able to examine the president directly, even the best professional diagnoses amount to speculation at best — and given Trump’s history of offering up bizarre claims about his health, it’s unlikely the public will get any clarity any time soon.

More on Trump: Trump Administration Cancels Grotesquely Unethical Medical Study After Being Caught Red Handed

The post Doctor Says Trump Appears to Be Showing Signs of a Stroke appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Breaking ai: Adobe Research Unlocking Long-Term Memory in Video World

Video world models, which predict future frames conditioned on actions, hold immense promise for artificial intelligence, enabling agents to plan and reason in dynamic environments. Recent advancements, particularly with video diffusion models, have shown impressive capabilities in generating realistic future sequences. However, a significant bottleneck remains: maintaining long-term memory. Current models struggle to remember events and states from far in the past due to the high computational cost associated with processing extended sequences using traditional attention layers. This limits their ability to perform complex tasks requiring sustained understanding of a scene.

A new paper, “Long-Context State-Space Video World Models” by researchers from Stanford University, Princeton University, and Adobe Research, proposes an innovative solution to this challenge. They introduce a novel architecture that leverages State-Space Models (SSMs) to extend temporal memory without sacrificing computational efficiency.

The core problem lies in the quadratic computational complexity of attention mechanisms with respect to sequence length. As the video context grows, the resources required for attention layers explode, making long-term memory impractical for real-world applications. This means that after a certain number of frames, the model effectively “forgets” earlier events, hindering its performance on tasks that demand long-range coherence or reasoning over extended periods.

The authors’ key insight is to leverage the inherent strengths of State-Space Models (SSMs) for causal sequence modeling. Unlike previous attempts that retrofitted SSMs for non-causal vision tasks, this work fully exploits their advantages in processing sequences efficiently.

The proposed Long-Context State-Space Video World Model (LSSVWM) incorporates several crucial design choices:

1. Block-wise SSM Scanning Scheme: This is central to their design. Instead of processing the entire video sequence with a single SSM scan, they employ a block-wise scheme. This strategically trades off some spatial consistency (within a block) for significantly extended temporal memory. By breaking down the long sequence into manageable blocks, they can maintain a compressed “state” that carries information across blocks, effectively extending the model’s memory horizon.
2. Dense Local Attention: To compensate for the potential loss of spatial coherence introduced by the block-wise SSM scanning, the model incorporates dense local attention. This ensures that consecutive frames within and across blocks maintain strong relationships, preserving the fine-grained details and consistency necessary for realistic video generation. This dual approach of global (SSM) and local (attention) processing allows them to achieve both long-term memory and local fidelity.

The paper also introduces two key training strategies to further improve long-context performance:

• Diffusion Forcing: This technique encourages the model to generate frames conditioned on a prefix of the input, effectively forcing it to learn to maintain consistency over longer durations. By sometimes not sampling a prefix and keeping all tokens noised, the training becomes equivalent to diffusion forcing, which is highlighted as a special case of long-context training where the prefix length is zero. This pushes the model to generate coherent sequences even from minimal initial context.
• Frame Local Attention: For faster training and sampling, the authors implemented a “frame local attention” mechanism. This utilizes FlexAttention to achieve significant speedups compared to a fully causal mask. By grouping frames into chunks (e.g., chunks of 5 with a frame window size of 10), frames within a chunk maintain bidirectionality while also attending to frames in the previous chunk. This allows for an effective receptive field while optimizing computational load.

The researchers evaluated their LSSVWM on challenging datasets, including Memory Maze and Minecraft, which are specifically designed to test long-term memory capabilities through spatial retrieval and reasoning tasks.

The experiments demonstrate that their approach substantially surpasses baselines in preserving long-range memory. Qualitative results, as shown in supplementary figures (e.g., S1, S2, S3), illustrate that LSSVWM can generate more coherent and accurate sequences over extended periods compared to models relying solely on causal attention or even Mamba2 without frame local attention. For instance, on reasoning tasks for the maze dataset, their model maintains better consistency and accuracy over long horizons. Similarly, for retrieval tasks, LSSVWM shows improved ability to recall and utilize information from distant past frames. Crucially, these improvements are achieved while maintaining practical inference speeds, making the models suitable for interactive applications.

The Paper Long-Context State-Space Video World Models is on arXiv

The post Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space Models first appeared on Synced.

🔗 Sumber: syncedreview.com