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HyperAI Super Neural

Deconstructing the sophistication and universality of technology, covering cutting-edge AI for Science case studies.

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Latest from HyperAI Super Neural

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HyperAI Super Neural

Jan 28, 2026 · Artificial Intelligence

EDEN Models Leverage a Million Species and 10‑Billion‑Scale Gene Data to Reach SOTA Genome & Protein Prediction

The EDEN series of foundation models, trained on the massive BaseData macro‑genomic dataset covering over one million species and 9.7 trillion nucleotides, achieve state‑of‑the‑art genome and protein prediction while enabling functional recombinase design, antimicrobial peptide generation, and synthetic microbiome construction with minimal task‑specific data.

AISynthetic Biologyfoundation models

0 likes · 15 min read

EDEN Models Leverage a Million Species and 10‑Billion‑Scale Gene Data to Reach SOTA Genome & Protein Prediction

HyperAI Super Neural

Jan 27, 2026 · Artificial Intelligence

How Microsoft’s Open‑Source TRELLIS.2 Generates Full‑Texture 3D Assets in 3 Seconds

The article analyzes the fundamental challenges of 3D generative AI, compares existing NeRF, voxel, and single‑view methods, and explains how Microsoft’s open‑source TRELLIS.2 uses a field‑free O‑Voxel representation and 16× compression to produce 512³ full‑texture assets in about three seconds, with a step‑by‑step HyperAI demo.

3D generationHigh‑resolution assetsHyperAI

0 likes · 6 min read

How Microsoft’s Open‑Source TRELLIS.2 Generates Full‑Texture 3D Assets in 3 Seconds

HyperAI Super Neural

Jan 23, 2026 · Artificial Intelligence

Embodied AI Resources: Datasets, Modeling, Papers (Nvidia, ByteDance, Xiaomi)

This article compiles a comprehensive set of embodied AI resources, including large‑scale robot learning datasets such as BC‑Z (32 GB) and DexGraspVLA (7 GB), interactive world‑modeling frameworks like HY‑World 1.5, open‑source LLM deployments, and recent research papers from Nvidia, ByteDance, Xiaomi and leading universities, each with download links and brief summaries.

AI research papersEmbodied AIOpen-source models

0 likes · 14 min read

Embodied AI Resources: Datasets, Modeling, Papers (Nvidia, ByteDance, Xiaomi)

HyperAI Super Neural

Jan 23, 2026 · Artificial Intelligence

Weekly AI Paper Digest: New Transformer Advances in Sparsity, Memory, and Reasoning

This article reviews five recent Transformer papers—including Engram's conditional memory, STEM's embedding‑based scaling, SeedFold's biomolecular structure prediction, a critique of Transformers for time‑series forecasting, and reasoning models as societies of thought—highlighting their methods, datasets, and performance gains.

Biomolecular Structure PredictionMemory MechanismsStructural Sparsity

0 likes · 7 min read

Weekly AI Paper Digest: New Transformer Advances in Sparsity, Memory, and Reasoning

HyperAI Super Neural

Jan 22, 2026 · Artificial Intelligence

Mapping the Human E3 Ubiquitin Ligase Landscape with Metric Learning

A German research team integrated protein sequences, domain architectures, 3D structures, functional annotations and expression profiles to build a multi‑scale, metric‑learning classification of the human E3 ubiquitin ligase repertoire, revealing family hierarchies, essential enzymes for cell viability and new drug‑target opportunities.

CRISPR screeningE3 ligasesdrug discovery

0 likes · 14 min read

Mapping the Human E3 Ubiquitin Ligase Landscape with Metric Learning

HyperAI Super Neural

Jan 19, 2026 · Artificial Intelligence

AI Boosts Scientists’ Careers by 1.37 Years While Shrinking Research Scope by 4.63%

A Nature study by Tsinghua and the University of Chicago analyzes 41.3 million papers and 5.37 million scientists, showing that AI tools dramatically increase individual researchers’ output and citation impact but simultaneously contract the overall knowledge breadth and collaborative density of science.

AIBERTKnowledge breadth

0 likes · 13 min read

AI Boosts Scientists’ Careers by 1.37 Years While Shrinking Research Scope by 4.63%

HyperAI Super Neural

Jan 15, 2026 · Artificial Intelligence

97% Accuracy: MOFSeq‑LMM Uses LLMs to Efficiently Predict MOF Synthesizability

A joint Princeton and Colorado School of Mines team introduced MOFSeq‑LMM, a large‑language‑model‑based framework that leverages a million‑scale MOF dataset and a novel string representation to predict free energy with MAE 0.789 kJ/mol and synthesizeability with 97% F1, dramatically accelerating high‑throughput MOF screening.

LLMMOFsMachine Learning

0 likes · 15 min read

97% Accuracy: MOFSeq‑LMM Uses LLMs to Efficiently Predict MOF Synthesizability

HyperAI Super Neural

Jan 14, 2026 · Artificial Intelligence

How OpenAI’s Circuit Sparsity Makes Large Language Model Reasoning Transparent

The article explains OpenAI’s 0.4B‑parameter Circuit Sparsity model, which zeros 99.9% of weights and uses dynamic forced sparsity, activation sparsity, and custom components to turn a dense transformer into an interpretable sparse circuit, and also highlights recent multilingual, portrait‑enhancement, and instruction‑tuned models with online demos.

Circuit SparsityLoRA portrait enhancementOpenAI

0 likes · 8 min read

How OpenAI’s Circuit Sparsity Makes Large Language Model Reasoning Transparent

HyperAI Super Neural

Jan 13, 2026 · Industry Insights

ChatGPT’s Billion Users, <10% Paying: Can AI Convert Compute Spend into Profit?

The article analyzes the widening gap between massive AI investment and modest commercial returns, showing how cloud giants like AWS and Google Cloud face shrinking margins despite soaring capex, while consumer‑facing AI products such as ChatGPT struggle to convert billions of users into paying customers, highlighting systemic profitability challenges across both B2B and B2C AI markets.

AI investmentB2B AIB2C AI

0 likes · 16 min read

ChatGPT’s Billion Users, <10% Paying: Can AI Convert Compute Spend into Profit?

HyperAI Super Neural

Jan 9, 2026 · Artificial Intelligence

How HY-MT1.5 Achieves 1 GB Mobile Translation with a 1.8B Model

The article explains how Tencent's open‑source HY‑MT1.5 tackles the high‑cost, large‑parameter barrier of neural machine translation by offering a 1.8 B‑parameter model that runs on roughly 1 GB of RAM, processes 50 tokens in 0.18 s, supports 33 languages, and uses on‑policy distillation to retain top‑tier accuracy, while providing a step‑by‑step online demo and free compute credits for new users.

HY-MT1.5Tencentlarge language models

0 likes · 5 min read

How HY-MT1.5 Achieves 1 GB Mobile Translation with a 1.8B Model