📌 TOPINDIATOURS Eksklusif ai: Subsurface map of Antarctica reveals hidden terrain

What lies beneath Antarctica’s immense and impenetrable ice sheet? Until now, we didn’t fully know, but a new, groundbreaking study reached deeper than any other to discover a hidden world that might hold the key to predicting the future of the continent, as well as our world.

In a new, groundbreaking study published in Science, authors explained that “less is known about the topography beneath the ice of Antarctica than any other planetary surface in the inner solar system.”

As the least studied region in the known universe, this mysterious area, cloaked by the Antarctic Ice Sheet, “offers critical insights into its geological history and influences how the ice reacts to climate changes,” as study authors continued.

Scientists ventured into our planet’s icy frontier with satellite data and physics. They deciphered the complex movements of the ice as if reading a secret language, creating a map of a subglacial wonderland of mountains, deep canyons, and rugged hills, as per the BBC.

And this sub-surface map of Antarctica would aid climate scientists in understanding how glaciers will move as the ice continues to melt under the threat of climate change. The urgency surrounding the speed of their disappearance is one of the most significant uncertainties confronting us today.

Did these scientists just break new ground?

Is Narnia actually under Antarctica?

Antarctica has long sparked the imagination, characterized as the Earth’s southernmost, coldest, and driest polar desert. Ancient glaciers, expansive ice shelves, and towering mountain ranges create an awe-inspiring landscape. It also serves as a sanctuary for unique wildlife, including penguins and seals, while being a hub for extensive international scientific research.

Although previous surveys have provided insights, a lead climate scientist said to the BBC that the latest map was “a really useful product” because it fills crucial gaps that have persisted for decades. No researcher had penetrated these depths or mapped the ice sheet’s underlying terrain in such vivid, thrilling detail.

The research team analyzed the surface using high-resolution satellite imagery, followed by a technique in physics known as Ice Flow Perturbation Analysis (IFPA). They traced the landscape by studying how the ice moved around it.

Beneath a colossal 5.4 million square miles of ice sheet, the study documented a staggering 71,997 hills and mapped a valley that stretched 248.5 miles within the Maud Subglacial Basin. Beneath Antarctica, river channels stretched hundreds of miles, and some regions even evoked alpine landscapes, Live Science reported. Detected transitions between highland plateaus and low-lying basins revealed tectonic boundaries that hint at the dynamic geological processes at play.

Indeed, an entire world awaited researchers beneath the surface that exercised a direct influence on the surface.

The key to climate change

The wealth of valuable information unearthed by the study stands to become as immense in significance as the map, as the rapidly melting Antarctic ice remains one of the era’s biggest concerns.

As the BBC elaborated, this hidden topography will significantly influence the movement of glaciers. But most importantly, the speed at which they might disappear and displace themselves.

With the map, scientists can improve their predictions on how swiftly the Antarctic ice will contribute to global sea-level rise.

Read the study in Science.

🔗 Sumber: interestingengineering.com

📌 TOPINDIATOURS Eksklusif ai: Nous Research's NousCoder-14B is an open-source

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

Buried in Li's <a href="https://nousresearch.com/nouscoder-14b-a-co…

Konten dipersingkat otomatis.

🔗 Sumber: venturebeat.com