Welcome to Royfactory

Introduction TL;DR: Anthropic committed $50 billion to build new data centers across the US, creating 800 permanent jobs and 2,400 construction jobs starting from 2026. This marks a strategic shift to self-operated infrastructure for growing Claude AI demand and reflects intensifying global competition in AI investment and infrastructure. Anthropic’s Investment Rationale Anthropic announced on 2025-11-11 their $50 billion plan to construct dedicated AI data centers in Texas, New York, and additional US locations in cooperation with Fluidstack. This is part of a direct infrastructure strategy that reduces reliance on hyperscale cloud partners and meets surging computational demands from business clients. ...

Introduction TL;DR: As of 2025-11-12, Ant International has open-sourced its Falcon TST time-series AI forecasting model. The AI model achieves over 90% forecast accuracy and cuts FX costs by up to 60%. Antom, the MSME-focused app, is set to transform business operations and payments. Falcon TST Model Open Source: Explained Falcon TST is a proprietary AI model built with Mixture-of-Experts architecture and multi-patch tokenizers, comprising up to 2.5 billion parameters. It is deployed internally to optimize cashflow and FX management hourly, daily, and weekly, with demonstrated state-of-the-art results across long-range forecasting benchmarks.[6][7][3][1][2] ...

Introduction TL;DR The 2025 paper from Tsinghua University rigorously demonstrates that reinforcement learning (RL) with verifiable rewards (RLVR) increases the efficiency of sampling correct answers, but does not add new reasoning behaviors to language models. In pass@k evaluation (whether 1 of k samples is correct), RLVR-tuned models excel at low k but underperform base models at high k, indicating no expansion in reasoning capacity. All correct outputs from RL-trained models were already present in the base model’s distribution. The implication: Surpassing LLM reasoning limits likely requires a fundamentally new paradigm rather than more RL. RL and Reasoning: What the Study Says Tsinghua University researchers systematically evaluated RLVR in math, coding, and vision benchmarks across various LLM families. Their key assertion: RLVR “sharpens” the distribution, increasing efficiency without expanding the actual set of correct reasoning paths.[2][4][5][6][1][3] ...

Introduction TL;DR: AI psychology now investigates human-AI trust, ethics, and clinical efficacy in mental health, with recent trials confirming chatbot therapy’s value. Key risks, privacy, and regulation take precedence in global and Korean contexts. In 2025, core research focuses on cognitive modeling with AI, overcoming shortages in mental health care, and implementing robust ethical and legal safeguards. AI Psychology: Core Concepts Human-AI Interaction, Trust, and Cognitive Modeling Modern AI models can predict diverse human behaviors, simulate psychological experiments, and offer insights on trust and error propagation effects. AI “virtual labs” now empower researchers to expand experimental scope and precision across decision-making, memory, and problem-solving tasks. ...

Introduction TL;DR: Meta has open sourced Omnilingual ASR, a multilingual speech recognition system supporting over 1,600 spoken languages, including more than 500 previously unserved low-resource languages, as of 2025-11-10. Featuring in-context learning and a public dataset, it sets a new industry benchmark for accessible, high-accuracy ASR across the globe. The system leverages up to 7B parameter wav2vec 2.0 models, supports rapid user-driven language extension, and provides free, permissively licensed models and corpus for research and development. Key Takeaways Over 1,600 languages covered, including 500+ low-resource, via open-source release on 2025-11-10 In-context learning enables rapid expansion to new languages with only a few audio-text samples Models range from lightweight (300M) to high-performance (7B parameters), freely licensed Industry-best accuracy: char error rate <10% for 78% of supported languages Large-scale corpus (Omnilingual ASR Corpus) and model suite open for research and deployment Core Features 1,600+ languages (500+ low-resource) supported, overcoming prior ASR limitations Architecture: 7B parameter Omnilingual wav2vec 2.0 encoder with both CTC and transformer decoders In-context learning: add new languages with just a few user-provided samples Omnilingual ASR Corpus includes 350+ minority languages, all open sourced Apache 2.0 and CC-BY licensing, full model and dataset access for all Why it matters: Expands AI speech recognition to digitally marginalized communities and drives global language inclusion. ...

Introduction TL;DR: Google unveiled its 7th-generation AI chip Ironwood in November 2025, achieving 4x to 10x performance improvements over previous chips. Ironwood boasts 4,614 FP8 TFLOPS, 192GB HBM3E memory, 9,216-scale pod architecture, and market-leading efficiency. Anthropic secured access to up to one million Google TPUs in a multi-billion dollar deal, intensifying competition and AI infrastructure scale. The announcement marks Google’s ambitious attempt to outpace Nvidia and Microsoft/OpenAI partnerships in next-gen AI computing. Ironwood, the latest Google custom silicon for AI, is engineered for next-gen LLMs, multimodal AI, and massive-scale inference, representing a major leap in hardware for cloud and enterprise AI. This strategic move positions Google as a formidable competitor in the AI infrastructure market, directly challenging the dominance of traditional GPU manufacturers. 1. Ironwood: Core Features and Advancements 1.1. Specs & Architecture Ironwood (TPU v7) delivers 4,614 FP8 TFLOPS per chip, 192GB of HBM3E memory, 7.37TB/s bandwidth, and scales up to 9,216 chips per superpod via 9.6Tb/s ICI, enabling parallel training and ultra-low-latency inference of large-scale models. Its perf/watt is doubled versus Trillium (v6e) and up to 30x more efficient than the original 2018 TPU. Ironwood’s chief target: LLMs (like Claude), complex RL, and AI serving at massive scale. ...

Introduction TL;DR: Hyperparameter tuning refers to systematically adjusting external settings like learning rate, batch size, and regularization in machine/deep learning models prior to training. Optimal hyperparameters directly impact performance, training efficiency, and generalization. The main keywords here are “hyperparameter tuning,” “optimization,” and “AI model performance,” which are critical in any serious data science or engineering project. This process distinguishes successful production models from experimental prototypes, enabling teams to extract maximum value from their data and computational resources. 1. Difference Between Hyperparameters and Parameters 1.1. What Defines a Hyperparameter? 1.1. What Defines a Hyperparameter? Hyperparameters are user-specified model settings such as learning rate, batch size, number of epochs, regularization coefficients, dropout rate, and more. Parameters are learned (weights, biases) during model training. ...

Introduction TL;DR: IBM announced the Granite 4.0 Nano model family in October 2025. These open-source LLMs, ranging from 350M to 1.5B parameters, feature Hybrid-SSM and Transformer architecture for maximum efficiency, running locally or at the edge. All models are Apache 2.0 licensed and certified for ISO 42001 Responsible AI, enabling safe commercial and enterprise applications. Available via Hugging Face, Docker Hub, and major platforms, these models benchmark strongly versus larger LLMs, transforming modern inference strategy. This release marks a new era for scalable and responsible lightweight AI deployment. IBM’s strategic focus on ultra-efficient, enterprise-grade AI models addresses the growing demand for local and edge deployment scenarios while maintaining strict security and compliance standards. The Granite 4.0 Nano series represents a significant milestone in democratizing AI access for organizations with limited computational resources or stringent data privacy requirements. 1. Nano Model Overview and Features 1.1. Hybrid-SSM and Transformer Leap 1.1. Hybrid-SSM and Transformer Leap IBM Granite 4.0 Nano achieves ultra-efficient local performance by blending the Mamba-2 Hybrid-SSM and Transformer approaches. Models are engineered to run on edge devices, laptops, and browsers—the smallest (350M) even locally in a web browser. Apache 2.0 open license, ISO 42001 certification, and full resource transparency meet enterprise security and governance needs. ...

Introduction TL;DR: In October 2025, a Yahoo/YouGov poll found that 53% of U.S. adults believe AI is likely to destroy humanity someday. The share grew 10 percentage points from the previous year, indicating a steep rise in anxiety around AI development and influence. Concerns center on job losses, deepfake proliferation, loss of social interaction, and erosion of trust in institutions and information. A nationally representative survey of 1,770 American adults conducted by Yahoo and YouGov in October 2025 revealed unprecedented levels of AI-related anxiety. This poll marks a critical inflection point in public perception of artificial intelligence, showing both a broadening and deepening of existential fears. The findings reflect growing societal concerns about AI’s trajectory and its potential impact on humanity’s future. 1. Rising AI Anxiety: Recent Poll Findings 1.1. The 53% Threshold: A Watershed Moment A nationally representative survey of 1,770 American adults conducted by Yahoo and YouGov in October 2025 found that 53% see the threat of AI ultimately destroying humanity as “somewhat” or “very” likely. This marks a notable 10 percentage point increase over 2024, showing both a broadening and deepening of AI-related fears. ...

Introduction TL;DR: Moonshot AI’s Kimi K2 Thinking is an advanced open-source large language model featuring 1 trillion parameters in a mixture-of-experts (MoE) architecture, activating 32 billion parameters for inference. It supports a 256K token context window and can autonomously execute 200 to 300 sequential tool calls, outperforming or matching GPT-5 and Claude Sonnet 4.5 in reasoning, agentic tasks, and coding benchmarks. Its API pricing is approximately 90% cheaper than prevailing models, marking a significant milestone in cost-effective AI access and underscoring China’s emerging lead in AGI competition. Launched officially in November 2025, Moonshot AI’s Kimi K2 Thinking represents a watershed moment in the democratization of advanced AI capabilities. This model combines massive computational scale with innovative architectural design, offering capabilities that rival the best proprietary models while maintaining an open-weight ecosystem that enables community-driven innovation and customization. 1. Model Overview and Architecture 1.1. Scale and Design Philosophy Kimi K2 Thinking represents Moonshot AI’s latest breakthrough in large language models. Leveraging one trillion parameters with a mixture-of-experts (MoE) design, Kimi K2 activates 32 billion parameters per inference. This enables extensive long-range reasoning, supported by a 256,000-token context window, and the ability to perform complex multi-step tool interactions up to 300 times without human intervention. ...