MAROKO133 Update ai: Samsung unveils smallest-ever 200-megapixel image sensor for smartpho

📌 MAROKO133 Breaking ai: Samsung unveils smallest-ever 200-megapixel image sensor

Samsung Semiconductor opened CES 2026 with a major imaging milestone, unveiling the ISOCELL HP5, the smallest 200-megapixel image sensor ever built for mobile devices.

The launch highlights how sensor miniaturization continues to reshape smartphone camera capabilities without forcing larger camera modules.

Roughly half the size of a penny, the ISOCELL HP5 packs 200 million pixels into an extremely compact footprint.

Each pixel measures just 0.5 micrometers, making it about one-hundredth the width of a human hair. Samsung designed the sensor to push physical limits while maintaining strong light absorption and signal accuracy.

Smaller sensor footprint

The 0.5-micrometer pixel size allows the ISOCELL HP5 to fit into a 1/1.56-inch optical format. Smartphone makers typically reserve this format for 50-megapixel sensors.

By keeping the same optical size, manufacturers can upgrade to 200 megapixels without redesigning camera modules.

This approach simplifies adoption for premium smartphone brands. It also enables slimmer camera bumps while still delivering higher resolution.

Samsung positions the HP5 as a drop-in upgrade rather than a disruptive hardware change.

Beyond still photography, the sensor supports 8K video recording at 30 frames per second.

This capability allows users to capture ultra-high-resolution video while preserving fine detail. Samsung says the sensor targets users who want professional-grade imaging from mobile devices.

Advanced pixel engineering

Shrinking pixels creates major sensitivity challenges. Samsung addressed these issues early in development by redesigning the sensor structure.

Engineers used a Deep Trench Isolation Center Cut design to improve pixel separation and reduce signal interference.

The company also introduced High Sensitivity DTI, or High-S, to improve light reflection inside each pixel.

Titanium dioxide materials and high-refractive-index lenses further enhance light absorption.

Together, these changes help maintain Full Well Capacity despite the tiny pixel size.

Samsung says these structural improvements allow the HP5 to overcome traditional low-light limitations.

The goal was to deliver consistent image quality rather than trade resolution for brightness.

High dynamic range remains a core focus of the ISOCELL HP5. The sensor supports 13-bit output, allowing richer color representation and smoother tonal transitions.

This depth enables more accurate color reproduction across complex scenes.

The HP5 also uses a 1:8 conversion gain structure to widen dynamic range. This design helps the sensor capture bright highlights and deep shadows in the same frame.

The sensor can record long and short exposures simultaneously, improving sharpness when subjects move.

Samsung highlighted the HP5’s industry recognition at CES. The company earned seven CES Innovation Awards this year, with the ISOCELL HP5 recognized in the Imaging category.

Samsung positions the sensor as a key step toward next-generation mobile photography.

With the ISOCELL HP5, Samsung reinforces its strategy of advancing camera performance through semiconductor innovation rather than larger hardware alone.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Hot ai: Nous Research's NousCoder-14B is an open-source coding mo

Nous Research, the open-source artificial intelligence startup backed by crypto venture firm Paradigm, released a new competitive programming model on Monday that it says matches or exceeds several larger proprietary systems — trained in just four days using 48 of Nvidia's latest B200 graphics processors.

The model, called NousCoder-14B, is another entry in a crowded field of AI coding assistants, but arrives at a particularly charged moment: Claude Code, the agentic programming tool from rival Anthropic, has dominated social media discussion since New Year's Day, with developers posting breathless testimonials about its capabilities. The simultaneous developments underscore how quickly AI-assisted software development is evolving — and how fiercely companies large and small are competing to capture what many believe will become a foundational technology for how software gets written.

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NousCoder-14B achieves a 67.87 percent accuracy rate on LiveCodeBench v6, a standardized evaluation that tests models on competitive programming problems published between August 2024 and May 2025. That figure represents a 7.08 percentage point improvement over the base model it was trained from, Alibaba's Qwen3-14B, according to Nous Research's technical report published alongside the release.

"I gave Claude Code a description of the problem, it generated what we built last year in an hour," wrote Jaana Dogan, a principal engineer at Google responsible for the Gemini API, in a viral post on X last week that captured the prevailing mood around AI coding tools. Dogan was describing a distributed agent orchestration system her team had spent a year developing — a system Claude Code approximated from a three-paragraph prompt.

The juxtaposition is instructive: while Anthropic's Claude Code has captured imaginations with demonstrations of end-to-end software development, Nous Research is betting that open-source alternatives trained on verifiable problems can close the gap — and that transparency in how these models are built matters as much as raw capability.

How Nous Research built an AI coding model that anyone can replicate

What distinguishes the NousCoder-14B release from many competitor announcements is its radical openness. Nous Research published not just the model weights but the complete reinforcement learning environment, benchmark suite, and training harness — built on the company's Atropos framework — enabling any researcher with sufficient compute to reproduce or extend the work.

"Open-sourcing the Atropos stack provides the necessary infrastructure for reproducible olympiad-level reasoning research," noted one observer on X, summarizing the significance for the academic and open-source communities.

The model was trained by Joe Li, a researcher in residence at Nous Research and a former competitive programmer himself. Li's technical report reveals an unexpectedly personal dimension: he compared the model's improvement trajectory to his own journey on Codeforces, the competitive programming platform where participants earn ratings based on contest performance.

Based on rough estimates mapping LiveCodeBench scores to Codeforces ratings, Li calculated that NousCoder-14B's improvemen t— from approximately the 1600-1750 rating range to 2100-2200 — mirrors a leap that took him nearly two years of sustained practice between ages 14 and 16. The model accomplished the equivalent in four days.

"Watching that final training run unfold was quite a surreal experience," Li wrote in the technical report.

But Li was quick to note an important caveat that speaks to broader questions about AI efficiency: he solved roughly 1,000 problems during those two years, while the model required 24,000. Humans, at least for now, remain dramatically more sample-efficient learners.

Inside the reinforcement learning system that trains on 24,000 competitive programming problems

NousCoder-14B's training process offers a window into the increasingly sophisticated techniques researchers use to improve AI reasoning capabilities through reinforcement learning.

The approach relies on what researchers call "verifiable rewards" — a system where the model generates code solutions, those solutions are executed against test cases, and the model receives a simple binary signal: correct or incorrect. This feedback loop, while conceptually straightforward, requires significant infrastructure to execute at scale.

Nous Research used Modal, a cloud computing platform, to run sandboxed code execution in parallel. Each of the 24,000 training problems contains hundreds of test cases on average, and the system must verify that generated code produces correct outputs within time and memory constraints — 15 seconds and 4 gigabytes, respectively.

The training employed a technique called DAPO (Dynamic Sampling Policy Optimization), which the researchers found performed slightly better than alternatives in their experiments. A key innovation involves "dynamic sampling" — discarding training examples where the model either solves all attempts or fails all attempts, since these provide no useful gradient signal for learning.

The researchers also adopted "iterative context extension," first training the model with a 32,000-token context window before expanding to 40,000 tokens. During evaluation, extending the context further to approximately 80,000 tokens produced the best results, with accuracy reaching 67.87 percent.

Perhaps most significantly, the training pipeline overlaps inference and verification — as soon as the model generates a solution, it begins work on the next problem while the previous solution is being checked. This pipelining, combined with asynchronous training where multiple model instances work in parallel, maximizes hardware utilization on expensive GPU clusters.

The looming data shortage that could slow AI coding model progress

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Konten dipersingkat otomatis.

🔗 Sumber: venturebeat.com