MAROKO133 Eksklusif ai: When Asked to Generate an Alphabet Poster for Preschoolers, the La

📌 MAROKO133 Update ai: When Asked to Generate an Alphabet Poster for Preschoolers,

On December 11, OpenAI release ChatGPT 5.2, the latest version of the widely used AI chatbot.

As it does every time it releases a minor update, the company hailed its latest version as a “significant improvement in general intelligence,” calling it the “best model yet for real-world, professional use.” In a further display of hubris, OpenAI even went so far as to claim 5.2 is their “first model that performs at or above a human expert level.”

Yet when we ran it through an incredibly simple prompt — to generate an alphabetized chart of animals for school children — the world-beating AI model came up laughably short.

The ABC deficiency was first noticed by BCA Research chief global strategist Peter Berezin. Using ChatGPT 5.1, which was released back in November, Berezin asked the AI to “draw [a] poster where you say A is for an animal that starts with the letter A, B for an animal that starts with B, all the way to Z.”

That version thought for six seconds, and spat out an image with 25 letters, as opposed to the standard English alphabet which uses 26. Already off to a rocky start, 5.1 does an okay job with “A” through “I,” but falls off the rails as soon as it gets to “K,” which it says “is for Lion.”

It goes on like this: “O is for jellyfish,” while “Q is for penguin” and “R is for snake.” By the time it gets to the end, “Z” is for “urba” — a turtle, apparently — followed by “B,” along with a picture of a pig.

“Still needs more capex,” Berezin quips, referencing the $1.15 trillion OpenAI has committed to spending on hardware in 2025 alone.

We were curious how ChatGPT 5.2 stacked up to its month-old predecessor — and sure enough, it didn’t disappoint.

Though the latest version of ChatGPT did a bit better with individual animals, it still only identified 24 letters in the English alphabet, forgetting “U” and “Z.” Instead, 5.2 listed “Y” for “Yak” right after “T.” This particular alphabet ends with “X,” which of course stands for “X-ray fish,” but is illustrated by a Zebra.

Its illustrations were also a bit suspect, with Kangaroos sporting a weird limb, an Iguana with two tails, a Narwal with a strange mash of eyes and fins as well as a bird’s beak, and a hedgehog with a cat’s face, to name a few weak points.

A follow-up prompt only spread the spill around. This time, there are 25 letters total. “Y” is still a problem area, taking the place of “U,” only this time it stands for “Unicorn,” which isn’t a real animal in the first place. By the end, there are two entries for “X,” one of which “is for fish,” followed by another “X,” again for “X-ray fish,” but with the same Zebra illustration.

The second poster also starts to scramble the instructions, injecting bits of the prompt into the poster title: “A is for alligator, B is for bear…”

As some users on X-formerly-Twitter pointed out, results are the same for Google’s Gemini, and as a quick glance at Grok shows, Elon Musk’s AI engine isn’t even close. But while ChatGPT might be the best at making an animal poster, it still comes up laughably short — and certainly not at the level of any “human expert” we’ve ever met.

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The post When Asked to Generate an Alphabet Poster for Preschoolers, the Latest Version of ChatGPT Just Flails Wildly 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