MAROKO133 Hot ai: Washington Post Says It Will Continue AI-Generating Error Filled Podcast

📌 MAROKO133 Hot ai: Washington Post Says It Will Continue AI-Generating Error Fill

The outcry sparked by the Washington Post’s launch of AI-generated podcasts has gone in one ear of the newspaper’s leadership and out the other, which isn’t too dissimilar to what might happen to anyone listening to the slop it’s trying to peddle.

On Monday, the Jeff Bezos-owned publication doubled down on its personalized podcasts push, brushing aside the criticism from readers and its own reporters alike.

“This is how products get built and developed in the digital age: ideation, research, design and prototyping, development, and then Beta,” a WaPo spokesperson told TheWrap.  “Only if they prove to be successful for the customer do they then get launched. As stated clear on Your Personal Podcast, it is currently in Beta.”

WaPo launched its “Your Personal Podcast” feature last week, sparking immediate dissent in its ranks. Staffers were furious about the podcast AI inventing and misattributing quotes, Semafor reported Friday, and even sometimes editorializing on stories. Some staffers questioned the tech’s seemingly nonexistent guardrails, Status reported, while another described it as a “total disaster.” 

Further underscoring the staggering incompetence on display, followup reporting from Semafor revealed that WaPo had conducted its own tests prior to launching which showed that up to 84 percent of the AI-generated podcasts scripts didn’t meet the newspaper’s standards — and were therefore unpublishable.

There’s a significant disconnect between the company’s newsroom and its product team in charge of the AI rollout. The podcast’s product team sees the errors as a normal part of rolling out a new and still experimental feature, and said it would “iterate through the remaining issues.”

Similar jargon was on display in the paper’s statement to TheWrap, and it’s equal parts telling and jarring that such tech-minded rhetoric is being deployed in the context of journalism. The AI may not be doing the work of an actual journalist, but it’s taking over the role of packaging the news to listeners. Would a human news anchor be given this much leeway, and be allowed to screw up more details than they get right because they’re learning on the job, or in tech parlance, “iterating through it”?

The Post isn’t the only newsroom deploying AI. The New York Times uses it to help generate headlines, and Bloomberg’s website features an AI that summarizes its articles. Many publications have some form of AI chatbot trained on their archives. But WaPo has been particularly AI-evangelistic, signifying its transformation under Jeff Bezos’ ownership. Along with deploying AI summaries and an AI chatbot, it also put forth a plan for letting non-professional writers submit articles written with AI.

The AI podcasts, however, seem to really have struck a nerve with its staff.

“It is truly astonishing that this was allowed to go forward at all,” one WaPo editor fumed on Slack, per Semafor. “Never would I have imagined that the Washington Post would deliberately warp its own journalism and then push these errors out to our audience at scale.”

“If we were serious, we would pull this tool immediately,” the editor added.

More on AI: Grok Is Making Wildly Contradictory Claims About Rob Reiner’s Death

The post Washington Post Says It Will Continue AI-Generating Error Filled Podcasts as Its Own Editors Groan in Embarrassment 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