MAROKO133 Update ai: Smart Ring Swells Up, Crushes Man’s Finger and Sends Him to the Hospi

📌 MAROKO133 Breaking ai: Smart Ring Swells Up, Crushes Man’s Finger and Sends Him

A Samsung Galaxy Ring user ended up in emergency room after the wearable swelled up so badly on his finger that he couldn’t get it off.

Zone of Tech YouTuber Daniel Rotar was entering his 48th hour of travel when his smart ring battery began to ripple and swell, becoming so tight around his finger he was unable to remove it. Under pressure from the shrinking ring, his finger subsequently began to swell as well.

Worse yet, Rotar was in transit, live-tweeting the debacle from the airport gate as he was waiting to board a flight home.

He shared photos of the ring on his bloated finger as he and airline crew members tried to remove the gadget with soap and water — which caused the battery to swell even more before he was ultimately disallowed to board his flight and sent to the hospital. There, medical staff iced his hand to reduce the swelling of his finger before leveraging medical grade lube to slide it off.

On X-formerly-Twitter, he shared a photo of the buckled battery and said he “won’t be wearing a smart ring ever again.“ His finger also showed chafing on the site of the injury.

At the time of the incident, Rotar says, his ring’s battery wasn’t even charged.

It doesn’t sound like he was the only person affected. After his own digging around on Reddit, he found other users were similarly experiencing waning battery life and at least on other swelling battery. Redditors said the brand has been offering free replacement batteries.

The issue makes intuitive sense. Batteries in other devices like laptops and phones are notorious for swelling, which is a common warning that they could be about to combust.

Samsung’s customer service page offers guidance on removing rings from swollen fingers, without any mention of what to do should the device’s battery start to squeeze its user. Rotar was traveling to Hawaii for the Snapdragon Summit, and aside from the battery life seeming substantially shorter — lasting only one and half days as opposed to the normal seven — reported no other issues with the health tracker.

Upon arriving home, Rotar posted another update to X, sharing that Samsung had reached out to him, compensated him for the unexpected hotel stay, booked him a car to drive home, and collected the ring for further investigation.

More on wearables: New Wearable for Exceptionally Pathetic Men Detects When Their Wife Is Cheating

The post Smart Ring Swells Up, Crushes Man’s Finger and Sends Him to the Hospital appeared first on Futurism.

🔗 Sumber: futurism.com

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

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