MAROKO133 Update ai: Large Study Finds That Replacing Workers With AI Is Backfiring Badly

📌 MAROKO133 Breaking ai: Large Study Finds That Replacing Workers With AI Is Backf

As AI continues to weave its way into every corner of daily life, one of the public’s chief fears is what it will mean in the workplace.

They’re not irrational to worry. Many name-brand big tech companies have already sacked thousands of workers in favor of the technology, from Meta to Square — a trend that sets up a natural experiment: are these AI layoffs actually resulting in positive business outcomes?

That’s why a new study from Gartner immediately caught our eye. As Fortune reports, the research and advisory firm surveyed 350 global business executives whose companies are pulling in at least $1 billion annually to investigate whether all these AI layoffs are paying off in the real world.

The first takeaway is that the trend is real, with a total of 80 percent admitted to trimming their human staff to make investments in AI or autonomous technology. But they say they had no idea if AI would actually generate any benefits — they were simply buying into the promise of automation via AI.

That’s where things get interesting. The Gartner survey found that execs who slashed staff to invest in AI have seen the same financial gains as those who held onto their employees. In othe words, attempting to replace workers with AI isn’t showing any detectable returns for these companies. And to make matters worse, many of these businesses specifically reduced their headcount to free up the cash needed for AI technology, meaning they sacrificed valuable institutional knowledge and employee goodwill for nothing.

The findings aren’t entirely surprising. An MIT study last year found that AI is failing to generate meaningful revenue growth at the vast majority of companies that embrace it.

Still, not everyone believes that all investment in AI is destined to backfire. Gartner analyst Helen Poitevin told Fortune that these seemingly drastic moves by execs may simply be attempts to trial AI, not to structurally reset the whole company.

“It seems to us to be a kind of one-time exercise by many in small amounts, but not what translates to getting full ROI from their AI investment,” Poitevin told Fortune.

So which companies are seeing an actual bump from AI?

The Gartner survey found that companies leveraging AI as a form of “people amplification” — meaning they give their employees AI tools to boost efficiency, instead of replacing them outright — are seeing the most significant gains. Even that strategy is fraught, though: previous research has suggested that the majority of employees aren’t keen on using AI just yet, with one survey revealing 54 percent avoid using in-house AI tools altogether.

More on labor: Tech Workers Are in Deep, Deep Trouble

The post Large Study Finds That Replacing Workers With AI Is Backfiring Badly 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