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AI2ML AI to Machine Learning
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AI2ML AI to Machine Learning

Original articles on artificial intelligence and machine learning, deep optimization. Less is more, life is simple! Shi Chunqi

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Recent Articles

Latest from AI2ML AI to Machine Learning

57 recent articles
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 1, 2025 · Artificial Intelligence

2025 Large Model Engineering Breakthroughs: Cutting Costs, Boosting Performance, and Extending Context

The 2025 open‑source reports reveal major advances in large‑model engineering, including drastic cost cuts such as DeepSeek‑V3 training for $5.57 M, performance gains where Gemma 3 4B matches Gemma 2 27B, memory efficiencies like 85 % KV‑cache reduction, and a suite of new techniques—from loss‑free MoE balancing to multi‑token prediction—that together push context lengths to one million tokens and enable multimodal, aligned, and industry‑specific models.

Large Language ModelsReinforcement Learningattention mechanisms
0 likes · 13 min read
2025 Large Model Engineering Breakthroughs: Cutting Costs, Boosting Performance, and Extending Context
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 30, 2025 · Artificial Intelligence

Dynamic Multimodal Video Generation: Prioritizing Stability and High Quality

The article surveys the evolution of video generation models—from early GANs and DCGAN to diffusion‑based approaches like Stable Diffusion and DiT—highlighting how stability, high quality, massive compute, and multimodal data pipelines are shaping the current and future paths of dynamic multimodal video generation.

Diffusion ModelsLatent DiffusionStable Diffusion
0 likes · 7 min read
Dynamic Multimodal Video Generation: Prioritizing Stability and High Quality
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 28, 2025 · Artificial Intelligence

Core Metrics for Enterprise Large‑Model Engineering

The article outlines the five essential engineering domains—application, model, compute, knowledge, and data—in the era of large models, and details concrete scale, efficiency, service, value, quality, and security metrics that enterprises should track to drive intelligent outcomes.

AI EngineeringBusiness ValueModel Performance
0 likes · 7 min read
Core Metrics for Enterprise Large‑Model Engineering
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 24, 2025 · Artificial Intelligence

Key Points for Evaluating AI Agents

The article explains how Coze's Compass introduces a flexible evaluation system for AI agents, outlines a four‑dimensional submodule assessment (planning, tool use, self‑reflection, memory), and details specific testing criteria and challenges for web, scientific, dialogue, and programming agents.

AI agentsAgent TestingBenchmarking
0 likes · 6 min read
Key Points for Evaluating AI Agents
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 22, 2025 · Big Data

Why AI‑Native Big Data Platforms Are About to Explode

The article examines how large‑model limitations in accuracy, explainability, and stability have stalled decision‑support use, prompting industry leaders to champion AI‑Ready data infrastructures, Data 4.0 concepts, and AI‑generated service code as the next wave of AI‑native big data platforms.

AIAI-nativeData 4.0
0 likes · 6 min read
Why AI‑Native Big Data Platforms Are About to Explode
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 11, 2025 · Industry Insights

Key Takeaways from Asset Management Leaders on Large‑Model AI at the Bund Conference

The article compiles senior asset‑management executives' perspectives on applying large‑model AI—covering vertical versus generic models, integration strategies, talent and cost considerations, innovative C2C development, AI‑native platforms, and the practical challenges of using LLMs in investment research.

AI applicationsC2C developmentLarge Language Models
0 likes · 5 min read
Key Takeaways from Asset Management Leaders on Large‑Model AI at the Bund Conference
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Sep 2, 2025 · Artificial Intelligence

Why Enterprise Large‑Model Digitalization Is So Hard: Key Challenges and Capabilities

The article analyzes why enterprise‑wide large‑model AI projects face steep hurdles, outlining required human capabilities, historical labor shifts, current hot technologies such as RAG, Agent, CoT and multimodal, their limits, a three‑stage implementation roadmap, typical case pitfalls, and the key success factors for sustainable digital transformation.

AgentCoTLarge Language Models
0 likes · 15 min read
Why Enterprise Large‑Model Digitalization Is So Hard: Key Challenges and Capabilities
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Aug 25, 2025 · Artificial Intelligence

Decoding OpenAI’s Multi‑Level AGI Roadmap

The article analyzes OpenAI’s five‑layer AGI roadmap, compares it with DeepMind’s ECEVS framework, and examines the technical progress from L1 to L5—including RL‑enhanced chain‑of‑thought, ReAct agents, deep research, and upcoming innovations—while highlighting the commercial implications of each stage.

AGIDeepMindOpenAI
0 likes · 7 min read
Decoding OpenAI’s Multi‑Level AGI Roadmap
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Jul 24, 2025 · Artificial Intelligence

Exploring Recent Large‑Model Agent Papers: Insights and Analyses

This article reviews a series of recent research papers on large‑model agents, covering topics such as reinforcement‑learning‑driven ML agents, premise‑critique ability of LLMs, long‑term tool‑augmented LLM evaluation, agentic RAG, set‑based retrieval for multi‑hop QA, mobile VLM agents, and broader surveys of LLM applications, summarizing each work’s problem statement, prior approaches, novel contributions, experimental results, limitations, and future directions.

Agentic AILLM EvaluationLarge Language Models
0 likes · 46 min read
Exploring Recent Large‑Model Agent Papers: Insights and Analyses