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

📌 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

📌 MAROKO133 Hot ai: Woman Asks ChatGPT for Powerball Numbers, Wins $150,000 Terbar

A Virginia woman won big at the Powerball lottery — using numbers picked by OpenAI’s ChatGPT, according to The New York Post, in a story that’s only possible in the 21st Century.

 “I’m like, ChatGPT, talk to me… Do you have numbers for me?” said Carrie Edwards, remembering how she requested the AI bot for help, during a press conference last week, as reported by the NY Post.

A few days later, the widow from Virginia got a phone notification that she won the Powerball drawing earlier this month.

Because she got the same four of the five numbers and the Powerball number, she got a $150,000 check that she plans to donate the winnings to several nonprofits: the Navy-Marine Corps Relief Society, Shalom Farms — a nonprofit food organization — and the Association for Frontotemporal Degeneration, which funds research on the form of dementia that killed her husband last year.

“As soon as that divine windfall happened and came down upon my shoulders, I knew exactly what I needed to do with it,” she said at the press conference. “And I knew I needed to give it all away, because I’ve been so blessed, and I want this to be an example of how other people, when they’re blessed, can bless other people.”

To be clear, ChatGPT obviously can’t predict winning lottery numbers. Instead, Edwards effectively used it as a random number generator, and then got lucky.

In our AI age, though, this isn’t the first time somebody’s claimed they used an AI bot to win the lotto. Back in 2023, a Thai man said he used ChatGPT to successfully guess numbers for a local sweepstakes, though only for a piddling sum of $59. 

And earlier this year, three university math students in Italy developed an algorithm that they claimed netted them more than $50,000 from a local drawing by spending just $350 on numbers that their algorithm said were overrepresented among winners; whether they actually beat the system like card counters at a game of blackjack is unclear, although hobbyists have occasionally found flukes in state lottery systems.

Reality check, though: you’re very unlikely to beat the odds using AI, even though the Apple store is already lousy with apps claiming to do exactly that.

Just remember that at the end of the day, lotteries and casinos exist for one reason — and the house always wins.

More on AI and gambling: Gannett Is Using AI to Pump Brainrot Gambling Content Into Newspapers Across the Country

The post Woman Asks ChatGPT for Powerball Numbers, Wins $150,000 appeared first on Futurism.

🔗 Sumber: futurism.com