MAROKO133 Update ai: Saturn’s Moon Shows Major Signs of Life Terbaru 2025

📌 MAROKO133 Hot ai: Saturn’s Moon Shows Major Signs of Life Hari Ini

Astronomers have found that Saturn’s moon, Enceladus, is spewing out copious amounts of complex organic molecules, suggesting it’s an even more promising place to look for extraterrestrial life than previously thought.

As detailed in a new study published in the journal Nature Astronomy, researchers examined data collected by NASA and the European Space Agency’s Cassini spacecraft, which visited the tiny moon between 2005 and 2015. They found evidence that its suspected underground ocean is home to complex chemical reactions, which on Earth are closely associated with life.

The research builds on existing findings that jets of water are bursting forth from Cassini’s underground ocean, shooting ice particles straight into space, some of which find their way back onto the moon’s surface.

“Cassini was detecting samples from Enceladus all the time as it flew through Saturn’s E ring,” said lead author and Freie Universität Berlin researcher Nozair Khawaja in a statement. “We had already found many organic molecules in these ice grains, including precursors for amino acids.”

Specifically, the team examined ocean spray data collected by Cassini’s Cosmic Dust Analyzer in 2008.

“The ice grains contain not just frozen water, but also other molecules, including organics,” Nozair explained. Since these particular particles traveled fast enough — roughly 11.2 miles per second — when hitting the instrument, they didn’t “cluster,” giving the researchers a “chance to see these previously hidden signals,” per Nozair.

They found evidence that the organic molecules originated in Enceladus’ ocean, including ones never observed before, such as signs of nitrogen- and oxygen-bearing compounds, ethers, and esters.

These organic molecules are involved in the kinds of chemical reactions that are believed to have eventually led to the formation of life on Earth, making it a particularly exciting finding.

“There are many possible pathways from the organic molecules we found in the Cassini data to potentially biologically relevant compounds, which enhances the likelihood that the moon is habitable,” Nozair said.

However, we’re still far from being able to confirm there’s life on Enceladus, something that would require a standalone mission to its surface.

“There is much more in the data that we are currently exploring, so we are looking forward to finding out more in the near future,” Nozair said.

Excitingly, the ESA is considering sending a spacecraft to the moon since it “ticks all the boxes to be a habitable environment.”

“Even not finding life on Enceladus would be a huge discovery, because it raises serious questions about why life is not present in such an environment when the right conditions are there,” Nozair concluded.

More on Saturn’s moons: Scientists Puzzled by Mysterious Motion in Atmosphere of Saturn’s Moon

The post Saturn’s Moon Shows Major Signs of Life appeared first on Futurism.

🔗 Sumber: futurism.com

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

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