MAROKO133 Breaking ai: Crypto Market Descending Into Chaos Edisi Jam 04:47

📌 MAROKO133 Eksklusif ai: Crypto Market Descending Into Chaos Edisi Jam 04:47

Since October of last year, the global crypto market has lost nearly $2 trillion in value. Much of that was driven by everybody’s favorite blockchain currency, Bitcoin, which fell from a high of over $126,000 in early October to below $63,000 in February.

Bitcoin has since clawed back some of those losses, and has been hovering around the $70,000 mark over the last month, give or take a few thousand. While the crypto free-fall seems to have paused, the extreme price swings remain. Yet in our deranged modern world of microbets and prediction markets, even volatility itself can become a financial vehicle.

Enter the five-minute crypto price prediction. Basically, prediction bettors can now wager on whether the price swings of cryptocurrencies like Bitcoin, Ethereum, and XRP will end higher or lower than they did five minutes ago.

On Polymarket’s tab for Bitcoin, a line graph crawls along like a seismograph, showing the price to beat as the total cost fluctuates by hundreds of dollars a second. In the top-right corner, a red timer counts down the seconds until the market becomes resolved, at which case the line graph freezes and the next five-minute segment opens for business.

Trading data supplied by the analytics firm Dune and flagged by Fortune shows just how popular this new format has become. Each day, the volume of money traded on five-minute markets reaches into the tens of millions, the majority of which is on Bitcoin. So far, the highest single trading day was March 4th, when gamblers wagered over $60 million combined.

That’s just Polymarket. Kalshi, the other big prediction market, offers 15-minute crypto bets for those who prefer a slower burn. While the trading activity shows a high volume of small bets worth a few cents a pop, there are definitely sharks in the water.

So when crypto gets too turbulent for your blood, just know the fun doesn’t have to end: there is, increasingly, a market for that.

More on crypto: Sad! Trump’s Meme Coin Has Turned Into a Total Disaster

The post Crypto Market Descending Into Chaos 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